Refers to a period in which enthusiasm, investments, or market valuations surrounding artificial intelligence (AI) exceed the technology’s demonstrated capabilities, adoption, or current economic returns. In an AI bubble, investors, organizations, and the public may overestimate AI’s impact, leading to unrealistic or inflated expectations, speculative investments, and rapid market growth that later may be followed by a correction.

Typically refers to generative AI models, such as ChatGPT. Machine learning, another form of AI, is not considered part of any potential bubble.

Refers to the use of artificial intelligence systems to provide personalized coaching, guidance, feedback, and skill development support to individuals in workplace or learning environments. AI coaches typically operate through conversational interfaces—such as chat, voice interaction, or digital avatars—and provide on-demand assistance with professional development, goal setting, communication skills, performance improvement, and behavioral change.

AI coaching systems analyze user inputs and may draw on organizational data, leadership models, competency frameworks, or training resources to deliver tailored recommendations and practice exercises. Common uses include role-playing workplace conversations, providing feedback on communication or presentation skills, reinforcing leadership behaviors, and nudging users toward performance goals.

Organizations are increasingly integrating AI coaching into learning and development strategies because it can scale coaching access across large workforces. Unlike traditional coaching—often reserved for senior leaders due to cost—AI coaching can provide continuous development support to frontline employees, managers, and professionals across the organization.

In large organizations, AI coaches may interact daily with thousands of employees. As a result, they can also reinforce leadership principles, workplace norms, and organizational values, effectively acting as digital ambassadors of company culture within workplace learning systems.

Most current approaches emphasize AI-augmented coaching, in which AI complements rather than replaces human coaches. In these models, AI systems support everyday skill development and practice, while human coaches focus on high-stakes leadership challenges, complex interpersonal dynamics, and transformational development.

As AI capabilities advance, AI coaching is expected to become an increasingly common component of employer learning ecosystems.

Related terms:

• AI-Assisted Coaching – A coaching model in which AI tools support or enhance human coaching. AI systems may analyze conversation data, generate reflection prompts, recommend development resources, or provide practice simulations, while a human coach leads the core coaching relationship.
• AI-Augmented Coaching – An approach that integrates AI systems with human coaching to extend the reach and effectiveness of coaching programs. In AI-augmented models, AI coaches often provide continuous, on-demand skill-building support, while human coaches focus on complex leadership challenges, strategic development, and high-touch interpersonal work.

See Topic Brief: AI Coaching for Employees | Learn & Work Ecosystem Library

Refers to the idea that artificial intelligence (AI) systems and humans can work together in ways that improve performance, productivity, decision-making, creativity, or problem-solving beyond what either could accomplish alone. AI complementarity focuses on how AI can support, extend, or enhance human capabilities rather than replace human workers. In these models, humans and AI systems often perform different but connected tasks based on their respective strengths. For example, AI systems may rapidly analyze large amounts of data, identify patterns, automate repetitive tasks, or generate drafts, while humans provide judgment, context, ethics, relationship-building, creativity, oversight, and decision-making. Rather than assuming AI will eliminate most jobs, complementarity models examine how work roles may be redesigned so humans and AI systems contribute together.

The term is increasingly used in workforce and labor market research, economics, business strategy, higher education and workforce development, human resources and talent development, and public policy discussions about AI and the future of work and is becoming increasingly important in discussions about job redesign, reskilling and upskilling, skills-first hiring, human-AI collaboration, workforce productivity, and organizational change.

Examples of AI complementarity include:

• Healthcare: Clinicians use AI to identify possible medical conditions while clinician makes final treatment decisions.
• Education: Faculty members use AI tools to help generate lesson outlines while instructors provide expertise, teaching judgment, and student engagement.
• Customer service: Workers use AI-generated summaries or recommendations during conversations with clients.
• Researchers: Use AI tools to organize and analyze large collections of information while interpreting meaning and implications.

Refers to the growing set of organizations, benchmarks, standards, and research efforts used to test and assess AI systems. As AI tools become more widely used in workplaces, education systems, employer hiring processes, and public services, there is increasing interest in ways to evaluate how these technologies perform in real-world settings. AI evaluation efforts examine issues such as model accuracy, reliability, safety, bias, transparency, and how people actually interact with AI tools in practice.

Unlike traditional sectors where “product testing” is centralized or regulated through well-established institutions, the infrastructure for evaluating AI is still emerging. Evaluation activities are currently carried out by a variety of actors, including technology companies that test their own systems, academic researchers studying real-world performance and impacts, independent benchmarking organizations that develop standardized tests for comparing models, and government agencies developing frameworks and guidance for responsible AI deployment.

Several types of organizations participate in this emerging evaluation ecosystem. For example:

• Benchmarking organizations: Develop standardized tests for comparing machine learning models and tracking technological progress.
• Government standards bodies: Publish frameworks to help organizations evaluate and manage risks associated with AI systems.
• Academic research centers: Study how AI performs in real-world environments, including how users interact with these systems and how outcomes vary across contexts.
• Independent research and policy organizations: Examine broader social, economic, and governance implications of AI deployment.

As AI becomes more integrated into education advising systems, hiring platforms, workplace productivity tools, and learning technologies, the development of credible and transparent evaluation systems is increasingly viewed as essential. These efforts help organizations understand how AI functions in practice, identify potential risks, and support more responsible and effective deployment of AI in education, workforce development, and other parts of the learn-and-work ecosystem.

See Topic Brief: AI Evaluation Ecosystem | Learn & Work Ecosystem Library

Refers to the growing sense of exhaustion, skepticism, or disengagement that individuals and organizations experience from the rapid pace of artificial intelligence (AI) developments. AI fatigue may result from:

• Information overload
• Frequent technology changes
• Unrealistic organizational expectations
• Repeated vendor marketing
• Concerns about job displacement
• Uncertainty about which AI tools provide meaningful value
• Pressure to learn and adopt AI technologies without adequate training, support, or time to adjust.

Refers to the policies, processes, roles, standards, and oversight mechanisms used to guide how artificial intelligence (AI) is developed, selected, deployed, used, and monitored within an organization or system.

AI governance establishes who is responsible for AI-related decisions, what uses are permitted or restricted, and how organizations manage risks related to accuracy, privacy, security, bias, transparency, accountability, and other potential impacts.

Effective AI governance may include approved-use policies, human oversight requirements, risk assessments, data protections, documentation and audit trails, procedures for reviewing AI-generated outputs, and rules governing when AI systems may act autonomously and when human approval is required.

As AI systems become more capable—including agentic AI systems that can plan and carry out multi-step tasks—governance increasingly extends beyond approving particular AI tools to governing the processes, decisions, data, and actions in which AI participates.

AI governance seeks to balance innovation and responsible use by creating clear boundaries and accountability while enabling individuals and organizations to benefit from AI.

See Topic Brief: Regulation/Policy in Artificial Intelligence (AI) | Learn & Work Ecosystem Library

AI Hiring Discrimination Lawsuits are legal claims brought by job applicants, employees, advocacy organizations, or government enforcement agencies that allege a hiring-related decision (e.g., screening, ranking, testing, interviewing, or selection) produced or was driven by an artificial-intelligence or algorithmic system that unlawfully discriminated against people on the basis of protected characteristics (race, sex, age, disability, national origin, etc.).

These claims typically assert violations of existing civil-rights and employment statutes (e.g., U.S. civil rights laws, including Title VII of the Civil Rights Act of 1964, the Age Discrimination in Employment Act (ADEA), and the Americans with Disabilities Act (ADA)by showing either intentional disparate treatment or a disparate-impact (neutral practices that disproportionately harm protected groups) caused or amplified by an AI tool.

See Topic Brief: AI Hiring Discrimination Lawsuits | Learn & Work Ecosystem Library

See Glossary Term: AI Hiring Discrimination, Lawsuits & Accountability | Learn & Work Ecosystem Library

Refers to the emerging legal, ethical, and regulatory actions addressing the use of artificial intelligence (AI) in employment screening and selection processes that result in bias or disparate impact against protected groups. These cases test how longstanding civil rights laws—such as Title VII of the Civil Rights Act and the Americans with Disabilities Act (ADA)—apply to automated hiring tools.

As employers increasingly rely on AI to process massive volumes of applications, job seekers have begun challenging algorithmic systems they believe discriminate based on race, gender, age, or disability. Notable cases include 2024–2025 filings against vendors such as Aon, HireVue, and Intuit, alleging biased or inaccessible AI tools. Federal agencies including the Equal Employment Opportunity Commission (EEOC) and Federal Trade Commission (FTC) have asserted that both employers and technology vendors can be held liable for discriminatory AI outcomes.

These developments mark a new phase in employment law where accountability extends beyond human decision-makers to the digital systems they deploy. The movement underscores the importance of explainable AI, regular bias audits, and transparent vendor oversight to prevent automated discrimination at scale.

See Glossary Term: Explainable AI | Learn & Work Ecosystem Library

See Topic Brief: AI Hiring Discrimination Lawsuits | Learn & Work Ecosystem Library

Several terms have emerged in the evolution of artificial intelligence (AI) that describe how multiple agents (machines, humans, or both) interact, share information, and produce coordinated or collective outcomes. These terms are related but they are not interchangeable. They reflect different aspects of how intelligence is distributed and organized across systems.

• AI Hive Mind
  • Descriptive, non-technical term for a system in which multiple AI agents are interconnected to share information, learn from one another, and coordinate actions in real time.
  • The agents function as a collective intelligence rather than as isolated systems.
  • Emerged as a term late 2010s–2020s, influenced in part by cultural references such as Star Trek and the rise of interconnected AI systems.
• Swarm Intelligence
  • Formal area of study that focuses in how decentralized, self-organized systems (often inspired by biological examples such as ants or bees) coordinate behavior through simple rules and local interactions.
  • Emerged as a term in the 1990s.
• Collective Intelligence
  • Intelligence that emerges from the collaboration and interaction of individuals or agents.
  • This concept applies broadly across human systems, machine systems, and hybrid human–AI environments.
  • Emerged as a term 2000s – 2010s as expansion of collective intelligence occurred across social, digital, and organizational contexts.
• Multi-Agent Systems (MAS)
  • A technical field in computer science focused on systems composed of multiple interacting agents, each with some level of autonomy.
  • These agents may cooperate, coordinate, or compete to achieve individual or shared goals.

These terms describe different dimensions of a similar phenomenon. Multi-Agent Systems refer to the technical structure of multiple interacting agents. Swarm Intelligence refers to a specific model of decentralized coordination. Collective Intelligence refers to the emergent outcome of group interaction. AI Hive Mind is a modern, metaphor-driven term that describes these systems as operating like a unified intelligence.

Use of these terms has implications especially for policy and governance, employers and workforce, and education:

• Policy and governance
  • Distributed decision-making complicates accountability and oversight
  • Transparency becomes more difficult as systems become more interconnected
  • Standards for interoperability and communication across systems become more important
• Employer and workforce
  • Movement from individual AI tools to coordinated networks of agents
  • Growth of roles focused on orchestration, supervision, and system design
  • Increased need for systems thinking and human–AI collaboration skills
• Education 
  • Shift from teaching students to use individual tools toward understanding systems of interacting technologies
  • Greater emphasis on systems thinking, including how multiple agents coordinate and influence outcomes
  • Expansion of digital and AI literacy to include evaluating outputs from multiple interacting systems, not just one tool
  • Emerging need to teach students how to orchestrate AI, including assigning roles to different tools, sequencing tasks, and monitoring results
  • New ethical questions around responsibility, transparency, and decision-making in distributed systems

Refers to the widespread promotion, enthusiasm, and elevated expectations surrounding artificial intelligence (AI) that may exceed its current capabilities or practical applications. AI hype is typically driven by media coverage, marketing, investment activity, public enthusiasm, and high-profile technology announcements that emphasize AI’s potential while sometimes minimizing its limitations or implementation challenges.

AI hype can accelerate awareness, experimentation, and investment, but it may also create unrealistic expectations about AI’s speed of adoption, accuracy, cost savings, or ability to replace human work.

AI hype is commonly associated with the early stages of emerging technologies.

AI literacy refers to the foundational knowledge and skills required to understand, use, and critically evaluate artificial intelligence (AI) systems. It typically includes basic awareness of how AI works, where it is used, its benefits and limitations, and its ethical and social implications. AI literacy emphasizes competent and informed use of AI tools. In most frameworks, AI literacy focuses on:

• Understanding what AI is and is not
• Knowing how AI systems are trained and deployed
• Recognizing bias, limitations, and ethical concerns
• Using AI responsibly and appropriately
• AI literacy largely assumes that users are interacting with AI as intended.

Adversarial literacy is an emerging concept that builds on AI literacy—but goes further. It emphasizes the ability to actively interrogate, challenge, and stress-test AI systems. It involves understanding how AI systems can be manipulated, misled, or produce harmful outcomes—and developing the skills to recognize, expose, and respond to those vulnerabilities. Rather than asking “How do I use AI well?”, adversarial literacy asks:

“How can AI fail, be exploited, or mislead—and how do I detect that?”

Adversarial literacy includes competencies such as:

• Recognizing adversarial inputs, prompts, or examples designed to manipulate AI behavior
• Probing AI systems to reveal bias, hallucinations, or unsafe outputs
• Evaluating AI responses for reliability, intent, and context sensitivity
• Engaging in ethical “red-teaming” or adversarial questioning to surface system weaknesses
• Understanding that AI systems are contestable, not authoritative

The term adversarial literacy is increasingly used by scholars and others to describe a paradigm shift, i.e. the emergence of several AI-driven shifts:

• AI systems are no longer passive tools: Generative and predictive systems actively shape information environments—and can be manipulated intentionally or unintentionally.
• Trust is no longer sufficient: AI outputs may sound authoritative while being incorrect, biased, or strategically misleading, requiring users to adopt a skeptical stance.
• Power and agency are at stake: Adversarial literacy equips individuals (earners, workers, citizens) to resist the over-reliance on opaque systems and reclaim human judgment.
• Education must move from use to critique: Teaching people how to prompt (chat with) AI is insufficient; they must also learn how to break, test, and question it safely and ethically.

See Glossary Term: Opacity (in Artificial Intelligence) | Opaque AI | Learn & Work Ecosystem Library

See Glossary Term: Information Literacy | Learn & Work Ecosystem Library

See Topic Brief: Converging Terms:  Digital Literacy & Information Literacy | Learn & Work Ecosystem Library

The AI sandwich is a conceptual framework and newer term that describes how humans and artificial intelligence systems work together in a structured workflow. In this model, humans initiate and frame a task (the top layer), AI systems perform analysis or generate outputs (the middle layer), and humans then review, interpret, and apply the results (the bottom layer).

The term emphasizes that effective AI use depends on human judgment at both the beginning and end of a process, rather than full automation. It is commonly used to illustrate human-in-the-loop approaches and the growing role of AI as a collaborative tool in decision-making, learning, and work.

Refers to a body-worn device (e.g., smart glasses, smart rings and watches, badges, or audio devices) that integrates artificial intelligence (AI) to collect data, interpret context, and provide real-time insights or assistance to the user. AI wearables may support functions such as learning, productivity tracking, health monitoring, navigation, language translation, and workplace performance support.

Within the learn-and-work ecosystem, AI wearables raise important considerations related to privacy, data governance, accessibility, human-AI interaction, and the evolving boundaries between personal technology and institutional systems.

Refers to the way work can expand after artificial intelligence (AI) tools are introduced into the workplace. While AI may help complete some tasks faster, it often creates additional work that happens afterward. For example, employees may need to review AI-generated content for accuracy, correct mistakes, rewrite unclear material, check sources, improve prompts, document changes, or resolve problems the AI missed. In many cases, the task is completed more quickly but the follow-up work increases.

The term also reflects a growing concern that organizations may treat AI as a productivity tool without redesigning workflows around it. As AI makes content and analysis faster to produce, expectations for speed, volume, and responsiveness often increase as well. Workers may be expected to produce more reports, emails, presentations, analyses, or decisions in the same amount of time.

Some observers describe AI workload creep as both a work design problem and a technology problem: how organizations structure work around the tool and how much hidden human oversight remains necessary. The concept highlights a growing form of “reverse engineering” work in which employees must interpret, verify, reconstruct, or explain AI-generated outputs before they can be trusted or used. This invisible layer of review and correction may not appear in productivity metrics, but it can significantly affect workload, attention, and cognitive fatigue.

Refers to the laws, executive directives, guidelines, and strategic frameworks enacted by individual state governments in the United States to govern the development, procurement, deployment, and oversight of AI technologies. Not all states have implemented regulations /policy, but those that do typically address transparency, data privacy, algorithmic accountability, cybersecurity, civil rights protections, ethical use, and the responsible application of AI in public services such as education, workforce development, healthcare, and transportation. State policies may include requirements for risk assessments, impact disclosures, governance structures, procurement standards, and restrictions on high-risk or harmful uses of AI.

State AI regulations operate within a broader national framework shaped by federal AI policies—such as those issued by the White House, the United States Congress, and federal agencies including the National Institute of Standards and Technology (NIST). Federal policy establishes nationwide principles related to AI safety, national security, civil rights, and sector-specific oversight, while states develop complementary or more targeted rules that address local needs and contexts.

See: Learn & Work Ecosystem Library: Glossary / Artificial Intelligence (AI) Regulations / Policy – U.S. Government | Learn & Work Ecosystem Library

See: Learn & Work Ecosystem Library: Topic Brief: Regulation/Policy in Artificial Intelligence (AI) | Learn & Work Ecosystem Library

Refers to the laws, executive actions, regulatory guidance, and national strategies established by the United States government to ensure the safe, ethical, and responsible development and use of AI across the country. Federal AI policy sets nationwide principles and requirements related to civil rights protections, data privacy, national security, AI safety and risk management, transparency, consumer protection, and oversight in key sectors such as healthcare, finance, education, and transportation. Components of federal policy may include agency-specific standards, procurement rules for trustworthy AI, reporting and impact assessment requirements, and governance structures such as federal AI councils or interagency oversight bodies.

Federal policy establishes overarching guardrails and national coordination within which states may adopt more specific or context-based AI regulations. The U.S. approach is developing alongside AI governance efforts in other nations and regions—including the European Union, the United Kingdom, Canada, and member countries of the Organisation for Economic Co-operation and Development (OECD)—which are creating regulatory frameworks that emphasize safety, accountability, human rights protections, and international alignment. Together, these national and international approaches reflect a growing global effort to promote responsible and harmonized AI governance.

See Learn & Work Ecosystem Library: Glossary /  Artificial Intelligence (AI) Regulations / Policy – States | Learn & Work Ecosystem Library

See Learn & Work Ecosystem Library: Topic Brief / Regulation/Policy in Artificial Intelligence (AI) | Learn & Work Ecosystem Library

Refers to two different ways that technology, including artificial intelligence (AI), can be used to perform tasks:

• Augmentation uses technology to support or extend human abilities while the person remains actively involved in thinking, decision-making, or performing the work.
• Automation transfers some or all of a task to technology, reducing the amount of human involvement required.

In human-AI collaboration, the distinction is important because AI can either help people perform work themselves or perform portions of the work for them. Augmentation and automation are not necessarily opposites; a single activity may include elements of both.

A technology system that enables direct communication between the human brain and a computer or digital device. Brain-Computer Interfaces (BCIs) use signals generated by the brain to control software, machines, or other technologies without requiring traditional physical input such as typing, speaking, or touch.

Some BCIs are invasive, requiring surgically implanted devices placed in or on the brain. Others are non-invasive and use external headsets, sensors, or wearable devices to detect and interpret neural activity.

BCIs are currently used primarily in clinical, medical, and research settings, including efforts to help individuals with paralysis, neurological disorders, speech impairments, or mobility limitations communicate or interact with digital systems.

The technology is also increasingly discussed within future-of-work, AI, defense, education, and workforce development conversations. Some researchers, companies, and futurists believe BCIs could eventually influence how humans learn, communicate, access information, interact with AI systems, or perform certain forms of knowledge work.

Although broad commercial adoption remains uncertain and likely years away, BCIs are increasingly viewed as part of longer-term discussions about human-machine interaction, cognitive augmentation, accessibility, and the future relationship between people and intelligent technologies.

A term coined by Cassie Kozyrkov in 2025 to refer to individuals who combine human skills—such as creativity, judgment, and ethical reasoning—with AI-enabled tools and capabilities to expand their professional or learning capacity. Like the Greek mythological chimera—a creature made of distinct parts—chimeric talent represents a new type of workforce augmented by AI, defined by a hybrid skill profile including:

• Rapid skill-shifting: Moving quickly into adjacent or even new capability domains because AI reduces the barrier to entry.
• Role shapeshifting: Boundaries among job roles, with workers able to do pieces of many roles.
• AI-augmented proficiency with core human strengths: Workers use new combinations of skills and tools including core human strengths of creativity, critical thinking, empathy, ethics, and communication, augmented with generative AI tools and digital fluency for analysis, content creation, and automation.
• Adaptability and continuous learning: the ability to learn, unlearn, and relearn quickly.
• Cross-functional collaboration: comfort working across traditional disciplinary or departmental boundaries using technology to connect ideas, people, and processes.

Use of this emerging term signals a shift in what talent means in the AI world: it is less about narrowly defined job titles and more about capability configurations and adaptability. It gives educators and employers a way to talk about hybrid profiles and new workforce demands. It also is useful in framing how future workforce preparation should evolve.

Related Terms

• AI‑augmented workforce: Humans augmented by AI tools.
• Hybrid skills: Describes blending technical + human + domain expertise to meet evolving work demands.
• T‑shaped skills (or T-shaped professionals): Describes an individual with deep expertise in one field (vertical bar) + broad capabilities/knowledge across others (horizontal bar). This term has been in use since the 1980s.

See Topic Brief:  Rise of an AI-Powered Workforce | Learn & Work Ecosystem Library

Closed-circuit AI models are artificial intelligence (AI) systems designed to operate within restricted, organization-controlled environments. They are trained or configured using limited, curated datasets specific to a defined domain, organization, or set of tasks. These models are intentionally constrained in scope, data access, and behavior to meet requirements related to privacy, compliance, reliability, and operational control, especially by companies that commission closed-circuit models.

Employers often adopt closed-circuit AI models for use cases in human resources, legal review, training systems, and internal knowledge management, particularly where sensitive data, regulatory obligations, or reputational risk are involved. While these models typically offer less breadth than general large language models (LLMs), they provide greater governance, predictability, and alignment with organizational requirements.

Closed-circuit does not mean that the information received from AI is more accurate or transparent; rather, it reflects a design choice that prioritizes control and accountability over open-ended exploration. Many organizations increasingly use hybrid approaches, combining the breadth of LLMs with closed-circuit controls. Employers in regulated and high-risk environments (e.g., healthcare, finance, law) increasingly favor closed-circuit AI deployments due to data sensitivity, compliance requirements, and reputational considerations. Consequently, many organizations limit general-purpose AI tools to exploratory or low-risk tasks, reserving operational decision-making for constrained systems.

See Glossary: Large Language Models (LLMs) | Learn & Work Ecosystem Library

See Topic Brief: AI Architectures in the Workplace: Large Language Models & Closed-Circuit AI Systems | Learn & Work Ecosystem Library

A model of work in which outcomes are produced through sustained interaction between humans and intelligent machines, with responsibility and influence distributed across human judgment, decision-making, and machine-driven pattern recognition and execution.

Prior to roughly 2023, most discussions of AI in the workplace framed human–machine interaction in terms of automation, augmentation, or AI assistance, positioning machines primarily as tools that replaced or supported specific human tasks.

With the increasing adoption of generative AI systems, this framing has shifted toward co-production, reflecting the growing reality that work outcomes increasingly emerge from joint human–machine activity (shared agency). This is characterized by humans shaping goals, context, and constraints while machines extend, recombine, and scale linguistic and computational patterns across workflows.

The practice of using external tools, technologies, or actions to reduce the amount of mental effort needed to complete a task. Examples include using a calendar instead of remembering an appointment, a calculator instead of doing arithmetic mentally, or a search engine instead of recalling information from memory. The concept predates generative AI.

AI greatly expands the kinds of cognitive work that can be offloaded, including summarizing information, generating ideas, analyzing material, drafting text, solving problems, and organizing knowledge. Cognitive offloading can improve efficiency, but increased reliance on external tools may also affect how often people practice or develop the underlying cognitive skills themselves.

The intentional design and management of the linguistic, informational, and interactional context in which an AI system operates in order to influence its outputs over time. Unlike prompt engineering, which focuses on individual prompts, context engineering shapes the broader conditions that guide model behavior—such as persistent instructions, accumulated examples, role continuity, reference materials, and conversational history—leveraging how language models extend patterns across context windows.

Context engineering reflects a shift from controlling AI through phrasing to guiding it through structured environments, making it especially relevant for learning systems, knowledge platforms, and workplace AI tools.

See: Prompt Engineering | Learn & Work Ecosystem Library

Refers to the reduction, erosion, simplification, or loss of human skills, knowledge, judgment, or expertise resulting from changes in technology, automation, work organization, or job design. Deskilling occurs when tasks that previously required specialized training, experience, or decision-making become automated, standardized, fragmented, or guided by systems that reduce the level of human discretion or expertise needed to perform the work. As a result, workers may rely less on deep knowledge or independent judgment and more on predefined procedures, software systems, algorithms, or machine-assisted processes.

The term is used in labor economics, sociology of work, workforce development, and industrial studies to describe the effects of mechanization, industrialization, and automation on workers and occupations. Historically, discussions of deskilling focused on factory production, assembly-line work, clerical labor, and process standardization. More recently, the concept has expanded to include artificial intelligence (AI), algorithmic management, robotics, and digital workflow systems.

Examples of deskilling may include:

• automated systems replacing portions of professional judgment
• software guiding workers through standardized workflows
• AI-generated content reducing the need for certain writing or research tasks
• retail, manufacturing, or service work becoming increasingly procedural
• diagnostic or decision-support systems reducing reliance on manual analysis

Some researchers and employers argue that technology may also create upskilling opportunities by shifting workers toward higher-level responsibilities, oversight, interpretation, communication, or problem-solving. Others caution that excessive automation may weaken long-term human capability, professional expertise, and workforce resilience.

Emerging AI discussions have introduced related concepts such as never-skilling, which refers to concerns that some learners or workers may never fully develop foundational skills because AI systems perform much of the cognitive work from the beginning. See: Never-Skilling (or “Never Skills”) | Learn & Work Ecosystem Library

The ability to use digital technologies confidently, adaptively, and creatively across changing contexts. It goes beyond basic digital literacy or task-specific competency by emphasizing judgment, problem-solving, innovation, collaboration, and the capacity to learn new tools as technologies evolve.

A digitally fluent person not only uses technology, but understands when to use it, how to combine tools effectively, how to evaluate outputs, and how to adjust as systems change. Digital fluency is increasingly important in workplaces shaped by AI, automation, data systems, and continuous technological change.

Digital fluency is often seen as an advanced stage of digital readiness built on foundations of literacy and competency.

The term can apply differently, depending on context.  For example:

• For younger students
  • Focus may include device basics, safe online behavior, media awareness, collaboration tools, and foundational creation skills
• For college learner
  • Focus may include research tools, digital identity, online teamwork, data literacy, AI-assisted learning, platform navigation
• For working adults / professionals
  • Focus may include workflow systems, cybersecurity awareness, data interpretation, remote collaboration, AI tools, continuous upskilling.
• For older adults
  • Focus may include access to services, telehealth, communication tools, fraud awareness, digital confidence, and independence and inclusion.

See Glossary Term: Digital Literacy | Learn & Work Ecosystem Library

Refers to a projected point in the future when accelerating advances in artificial intelligence and automation fundamentally transform the economy to such a degree that traditional labor markets can no longer function as they do today. While speculative, the idea is used by economists, futurists, and workforce-development leaders to explore scenarios in which technological progress outpaces society’s ability to adapt through retraining, new job creation, or policy interventions.

At this stage, machines could perform most economically valuable tasks more efficiently than humans. These tasks include data analysis, report writing, customer service interactions, scheduling and logistics planning, financial modeling, diagnostic support, legal document drafting, and quality-control inspection—areas economists monitor as indicators of accelerating automation.

This scenario could potentially lead to widespread job displacement, major shifts in income distribution, and the need for new economic models to support human well-being. The concept draws from the technological “singularity” idea but focuses specifically on economic and labor-market implications.

See Topic Brief: Economic Singularity: Humans & AI | Learn & Work Ecosystem Library

Refers to the repeated use of digital technologies to monitor, harass, intimidate, threaten, or otherwise target an employee in ways that cause fear, emotional distress, or disruption to their work or personal life.

Cyberstalking may be committed by coworkers, supervisors, former employees, customers, clients, or unrelated individuals and can occur through email, text messages, social media, collaboration platforms, online forums, location-tracking technologies, or other digital communication tools.

Unlike isolated online harassment, cyberstalking involves a persistent pattern of unwanted behavior and may escalate into workplace safety, legal, or security concerns.

Organizations increasingly address employee cyberstalking through workplace violence prevention, anti-harassment, cybersecurity, employee well-being, and acceptable technology use policies, often in coordination with legal actions.

See Topic Brief: Digital Workplace Safety | Learn & Work Ecosystem Library

Refers to a person’s ability to take an active role in seeking, evaluating, producing, using, and questioning knowledge versus simply accepting information provided by others. In education, it includes learners taking responsibility for determining what they know, what they need to know, and how their learning can be evaluated.

The concept has become increasingly relevant in the use of AI because AI systems can quickly provide answers, explanations, and synthesized information. Maintaining epistemic agency means that the human remains actively involved in judging the information rather than treating AI-generated information as automatically authoritative.

Refers to news, analysis, or informational content that remains relevant and useful over a long period, rather than being tied to a specific, time-sensitive event. This content continues to provide value to readers or users months or even years after publication. Evergreen journalism is especially valuable in education, workforce development, and lifelong learning, where users seek guidance, explanations, or historical perspectives that retain relevance over time.

In an AI-enabled information environment, evergreen content requires structured storage, metadata tagging, and retrieval systems to ensure discoverability and accessibility. AI tools can also help maintain its relevance by suggesting updates or linking it to new developments.

Examples

• Explainers on emerging workplace technologies
• Historical analyses of industries or labor trends
• Educational “how-to” or skill-building articles

A set of processes and methods that allows human users to comprehend and trust the results and outputs created by the machine learning algorithms of AI systems. Instead of simply producing a score or recommendation, explainable AI shows which factors the algorithm considered and how much weight each factor received.

Many AI hiring tools operate as “black boxes,” making it difficult to determine why a candidate was selected or rejected. Explainable AI mitigates this problem by offering visibility into how the model functions, allowing employers to identify potential sources of discrimination and to demonstrate accountability in hiring practices. This interpretability enables users—such as employers, regulators, and job applicants—to comprehend, trust, and audit algorithmic outcomes. This is critical  for ensuring fairness, detecting bias, maintaining compliance with anti-discrimination laws, and ensuring transparency and accountability. A growing concern is that many employers purchase AI systems from vendors without fully understanding how those systems function. This lack of insight can lead to unintended legal and ethical consequences when algorithmic decisions replicate or amplify bias.

See: AI Hiring Discrimination, Lawsuits & Accountability | Learn & Work Ecosystem Library

Fair use is a legal doctrine in U.S. copyright law that permits limited use of copyrighted material without permission from the rights holder for purposes such as education, research, scholarship, commentary, criticism, and news reporting. Fair use determinations are context-specific and rely on an analysis of four factors: (1) the purpose and character of the use, (2) the nature of the copyrighted work, (3) the amount used, and (4) the effect on the market value of the original work.

The growth of artificial intelligence (AI) has complicated traditional understandings of fair use, raising new legal and institutional questions about the use of copyrighted materials.  In digital learning, research, and AI-enabled environments, fair use plays a critical role in enabling access, innovation, and knowledge sharing while balancing the rights of content creators. AI complicates fair use practices because it raises unresolved questions such as:

• Whether training on copyrighted works constitutes fair use?
• Whether AI outputs are “transformative”?
• Who bears responsibility when outputs resemble copyrighted material?
• How should attribution, licensing, and compensation work at scale?

Courts have historically emphasized human purpose and transformation. AI introduces non-human intermediaries, probabilistic reuse, and scale far beyond traditional educational copying. This matters for educators, researchers, libraries, and publishers because fair use is no longer just about what humans copy: it is about what systems ingest, how outputs are generated, and how institutions manage risk, disclosure, and compliance.

Refers to the current era of technological transformation characterized by the convergence of digital, physical, and biological technologies. Building on earlier industrial revolutions driven by mechanization, electricity, and computing, the Fourth Industrial Revolution is distinguished by advances in artificial intelligence (AI), robotics, the Internet of Things (IoT), cloud computing, advanced manufacturing, biotechnology, quantum computing, digital twins, autonomous systems, and data analytics.

Unlike previous industrial revolutions that primarily transformed manufacturing, the Fourth Industrial Revolution is reshaping nearly every sector of society—including education, healthcare, government, transportation, finance, and the workforce. It represents a transformation in how knowledge is created, work is organized, and value is generated. Many occupations are being redesigned, requiring workers to continually acquire new skills and adapt to evolving technologies. Educational institutions, employers, governments, and workforce organizations are responding by developing more flexible learning pathways, new credentialing models, and stronger alignment between education and employment.

The term was popularized by Klaus Schwab in 2016 through his book The Fourth Industrial Revolution and has since become a widely used framework for understanding the societal implications of rapid technological change.

See Glossary Term: Industry 4.0 | Learn & Work Ecosystem Library

See Topic Brief: The Fourth Industrial Revolution: Transforming Learning & Work | Learn & Work Ecosystem Library

A term introduced and popularized by Microsoft through its 2025 Work Trend Index research to describe organizations increasingly integrating artificial intelligence (AI), AI agents, and AI-enabled workflows into everyday operations and decision-making processes. According to Microsoft, “frontier firms” move beyond using AI solely as a productivity tool or isolated experiment. Rather, frontier firms embed AI systems and AI-supported workflows across multiple business functions, including communication, research, customer service, software development, analysis, planning, and operational support. The concept is associated with human-AI collaboration, AI-enabled workflow redesign, “human-agent teams,” emerging AI-related workforce roles, and organizational adaptation to AI-enabled work environments.

Microsoft’s research also describes workers increasingly supervising or coordinating AI systems and agents, sometimes referring to these workers as “agent bosses.”

In 2025, Microsoft reported that frontier firms are already taking shape, and within the next 2–5 years, every organization is expected to be on their journey to becoming one. The 2025 report finds that 82% of leaders say this is a pivotal year to rethink key aspects of strategy and operations; 81% expect agents to be moderately or extensively integrated into their company’s AI strategy in the next 12–18 months; and adoption is accelerating with 24% of leaders indicating their companies have already deployed AI organization-wide, while 12% remain in pilot mode.

See Glossary: Frontier Professional | Learn & Work Ecosystem Library

See Initiative: Research Report: Work Trend Index – Microsoft | Learn & Work Ecosystem Library

An emerging term used in discussions about AI-enabled work environments to describe workers who operate effectively in organizations using artificial intelligence (AI), AI agents, and human-AI collaboration as part of everyday work processes. The term is associated with professionals who increasingly work alongside AI systems and agents, evaluate and verify AI-generated outputs, coordinate AI-supported workflows, combine human judgment with machine-generated insights, and adapt to rapidly changing digital work environments.

The concept reflects broader shifts in professional work in which value increasingly centers on judgment, contextual understanding, communication, adaptability, systems thinking, and the ability to work effectively within human-AI teams.

See Glossary: Frontier Firm | Learn & Work Ecosystem Library

See Initiative: Research Report: Work Trend Index – Microsoft | Learn & Work Ecosystem Library

Refers to strategies used to improve how content is discovered, interpreted, summarized, and cited by generative artificial intelligence (AI) systems which include large language models and AI-powered search and assistant tools. GEO builds on rather than replaces traditional Search Engine Optimization (SEO). While GEO is gaining importance, SEO remains foundational but is no longer sufficient—it is being absorbed into the broader information stack  with two key implications: (1) users increasingly receive answers instead of links; and (2) content visibility now depends on whether it is considered authoritative enough to be included in an AI response and whether it can be summarized accurately without distortion. SEO optimizes for indexing and ranking in search engines while GEO optimizes for selection, synthesis, and citation by generative systems.

See: Search Engine Optimization (SEO) / Key Word Optimization | Learn & Work Ecosystem Library

Refers to individuals who are willing and able to work but are systematically excluded from employment opportunities due to hiring practices—most notably the use of automated applicant tracking systems (ATS), credential filters, and rigid job requirements that screen out qualified candidates. As a result, these workers remain “hidden” from employers despite possessing relevant skills, experience, and potential.

Research in 2023 from Harvard Business School’s Project on Managing the Future of Work shows that increased reliance on technology and changing demographics have shaped the way companies hire. Hiring processes are designed to find top candidates in an efficient manner, but in doing so systematically exclude several categories of qualified workers, including caregivers, veterans, the formerly incarcerated, those with disabilities, etc. Companies who have hired one or more of these groups of hidden workers report that these workers are more loyal and perform better on several key metrics compared to traditional sources of talent. With many companies facing a talent shortage, hiring these hidden workers is proposed as a solution by researchers.

The merging of human resources (HR) functions with information technology (IT) systems to build a digital workplace. The integration of previously separated units within a company can streamline workforce management; support data-driven decisions that improve efficiency, compliance, and the employee experience; centralize data; and automate routine tasks. It typically connects HR processes such as recruitment, payroll, training, performance management, and employee engagement—with digital platforms, analytics, and enterprise systems to create a seamless flow of information and operations across the organization.

Refers to the knowledge, skills, competencies, experiences, health, creativity, and other attributes that enable individuals to contribute to work, society, and their own well-being. Human capital is developed through education, training, work experience, lifelong learning, health, and other life experiences, and is widely regarded as one of the primary drivers of economic growth, innovation, organizational performance, and individual opportunity.

The concept emerged in economics during the mid-twentieth century through scholars who argued that investments in education, training, and health increase individuals’ productive capabilities in much the same way that investments in physical capital increase the productive capacity of businesses and economies. Human capital theory subsequently became a foundational concept in economics, workforce development, education policy, and organizational management.

Today, many researchers view human capital as encompassing the capabilities that enable individuals to adapt to technological change, exercise judgment, solve complex problems, collaborate effectively, and continue learning throughout increasingly long and dynamic careers. Organizations such as the Stanford Center on Longevity further emphasize that human capital encompasses not only workforce skills, but also personal growth, civic participation, caregiving, health, and the capacities that enable individuals to flourish across the lifespan.

In an AI-enabled economy, discussions of human capital increasingly focus on the complementary relationship between human and machine capabilities. As AI automates routine tasks, the importance of uniquely human capabilities (e.g., contextual intelligence, ethical judgment, creativity, communication, adaptability, and relationship building) has become an important component of human capital. Consequently, many workforce strategies now emphasize continuous learning, reskilling, upskilling, and lifelong development as essential investments in maintaining and strengthening human capital.

Refers to the structured and dynamic interaction between humans and machines, particularly AI-enabled systems, in which each contributes distinct capabilities to perform tasks, solve problems, or make decisions. Rather than replacing human labor, machines in collaborative arrangements augment human judgment, efficiency, and insight; and humans provide context, oversight, ethical reasoning, and accountability.

Although the phrase has existed in in the literature for decades (as HCI / Human–Computer Interaction and in robotics literature), it is experiencing an updated understandings due to generative AI and autonomous systems:

• AI systems now reason, generate, recommend, and decide; they do not just execute tasks.
• Collaboration is no longer sequential (“human → machine” or “machine → human”) but it is continuous and interdependent.

As AI systems become more autonomous and generative, human–machine collaboration increasingly functions as a continuous partnership rather than a handoff between human and machine roles.

A related term is Machine-Human Workforce which focuses on composition and structure; Human–Machine Collaboration focuses on interaction and process.

See Glossary Term: Machine-Human Workforce | Learn & Work Ecosystem Library

See Topic Brief: https://learnworkecosystemlibrary.com/topics/machine-human-workforce/

An emerging term used to describe situations in which organizations present artificial intelligence systems, automated process, or technology-enabled service as more human, human-controlled, or human-like than it actually is. The term is used to describe efforts that may mislead users about the role, capabilities, oversight, or involvement of humans in a system, particularly in contexts involving AI and automation. Current usage generally falls into three categories:

• Making AI appear more human than it really is
  • Refers to presenting AI systems as possessing human-like capabilities, emotions, judgment, or understanding that they do not actually possess, thereby misleading users about what the technology can do.
• Hiding AI behind a human façade
  • Where organizations imply that a service is provided by humans when AI is actually doing much of the work.
  • Examples include AI chatbots that are not clearly disclosed as AI or synthetic avatars presented as real people.
• Claiming “human oversight” that is largely symbolic
  • Organizations emphasizing that humans remain “in the loop” to reassure stakeholders, even when human review is limited, ineffective, or largely performative.
  • In this sense, humanwashing uses the language of human judgment and accountability to make systems appear safer or more responsible than they are.

Refers to the transformation of manufacturing and industrial production through the integration of advanced digital technologies, automation, and connected systems. Often described as the industrial dimension of the Fourth Industrial Revolution, Industry 4.0 emphasizes the use of intelligent technologies to create more efficient, flexible, data-driven, and autonomous production environments. Industry 4.0 combines technologies such as artificial intelligence (AI) , Industrial Internet of Things (IIoT), robotics and autonomous systems, cloud computing, big data and advanced analytics, digital twins, cyber-physical systems, additive manufacturing (3D printing), advanced sensors, and edge computing. Rather than relying on isolated machines or manual processes, Industry 4.0 enables equipment, products, and production systems to communicate, exchange data, and make real-time decisions. Manufacturers can monitor operations remotely, predict equipment failures before they occur, customize products more efficiently, and optimize production through intelligent automation. Beyond manufacturing, Industry 4.0 principles are increasingly influencing logistics, supply chains, healthcare, agriculture, construction, transportation, energy, and other sectors.

For education and workforce development, Industry 4.0 has increased demand for workers who possess both technical and human-centered skills, including digital literacy, data analysis, systems thinking, cybersecurity, AI literacy, collaboration, and continuous learning. It has also accelerated interest in industry certifications, microcredentials, apprenticeships, competency-based education, and employer-education partnerships designed to prepare learners for technology-enabled workplaces.

Industry 4.0 is widely viewed as one of the primary technological drivers of the broader Fourth Industrial Revolution.

See Glossary Term: Fourth Industrial Revolution (4IR) | Learn & Work Ecosystem Library

See Topic Brief: The Fourth Industrial Revolution: Transforming Learning & Work | Learn & Work Ecosystem Library

An emerging leadership approach for organizations in which people and artificial intelligence (AI) systems increasingly work together.  Integrated leadership recognizes that the workforce increasingly consists of people and AI systems working together.

The concept reflects a shift in workforce composition, in which leaders are responsible for both employee development and organizational performance, as well as determining how work should be allocated among people, AI systems, automation, and other digital tools. Success depends on understanding where human judgment adds the greatest value, where AI can enhance decision-making or productivity, and how the two can work together effectively.

Key characteristics of integrated leaders:

• Understand both human capabilities and AI capabilities.
• Design work to determine which tasks are best performed by people, AI systems, or a combination of both.
• Build employee trust and confidence in the responsible use of AI.
• Help employees adapt to changing roles and workflows.
• Support the ongoing development of both employees and AI systems through learning, training, evaluation, and continuous improvement.

The term is particularly relevant for:

• Executive leaders, who establish organizational strategy for AI adoption.
• Human Resources leaders, who redesign jobs, workforce planning, learning, and talent strategies.
• Operational and frontline managers, who oversee teams where people increasingly work alongside AI systems and digital agents.
• Organizational development and change management professionals, who guide organizations through workforce transformation.

Refers to an employer’s practice of enabling employees to move into new or expanded roles within the same organization. It includes promotions, lateral transfers, cross-functional changes, rotational assignments, and project-based or gig-style internal work.

Employers use internal mobility strategies to deploy talent more effectively, retain employees, and support employee development.

Modern internal mobility systems increasingly rely on AI-enabled talent platforms that analyze employees’ skills, experiences, and learning histories to match them with current or emerging roles. These platforms help identify internal candidates who might otherwise be overlooked, recommend personalized career pathways, and surface internal opportunities more efficiently and equitably. While internal mobility can support career advancement, the term specifically refers to movement within the organization, not necessarily upward movement.

Learning and skill development that occurs so seamlessly within everyday work or other activities that it may not be experienced as a separate learning or training event. In the workplace, invisible learning can occur as employees solve problems, collaborate with colleagues, receive guidance, use digital or AI-enabled tools, participate in mentoring or job shadowing, or receive information and support at the moment it is needed.

Invisible learning is related to, but not identical to, informal learning. Informal learning often occurs naturally and spontaneously through experience, observation, conversation, and problem-solving.

Invisible learning may also occur informally, but it can be intentionally designed into workplace technologies, systems, and processes so that learning support is available while employees are doing their work, without requiring them to stop and participate in a separate training activity. The term is increasingly used in workforce learning and development to describe a shift from scheduled courses and training programs toward continuous learning embedded in the flow of work. AI is accelerating this approach by enabling contextual guidance, adaptive support, and real-time assistance while employees are performing tasks.

Large Language Models (LLMs) are a class of artificial intelligence (AI) systems that consist of very large and diverse collections of text (and often code, images, and other data) that recognize patterns in language and generate human-like responses. LLMs are designed as general-purpose models capable of performing a wide range of tasks, such as summarizing, drafting, translating, reasoning, and answering questions across many domains.

LLMs do not “know” facts in a human sense; rather, they predict responses based on statistical patterns learned during training. Examples of LLMs include ChatGPT (OpenAI), Gemini (Google), LLaMA (Meta AI), Bard (Google AI), and Claude (Anthropic).

Depending on how they are deployed, LLMs may be further refined through techniques such as fine-tuning, feedback loops, or reinforcement learning, often with governance controls that limit how user interactions are retained or used.

In the context of the learn-and-work ecosystem, LLMs are useful for their breadth, flexibility, and ability to provide information and support exploration by users. Examples of use include career navigation, content generation, and knowledge synthesis. Their general-purpose design results in varying levels of accuracy, consistency, and explainability. For this reason, human oversight and organizational guardrails are needed to work effectively with LLMs.

See Glossary: Closed-Circuit AI Models (Domain-Specific or Enterprise AI Models) | Learn & Work Ecosystem Library

See Glossary: Opacity (in Artificial Intelligence) | Opaque AI | Learn & Work Ecosystem Library

See Topic Brief: AI Architectures in the Workplace: Large Language Models & Closed-Circuit AI Systems | Learn & Work Ecosystem Library

A workforce model in which human workers and technology “intelligent systems”—such as agentic AI, digital assistants, and autonomous software agents—collaborate with employer teams and are integrated within. These AI systems can address traditional task automations functions plus interpret context, learn from data, take the initiative on efforts, and participate actively in decision-making.

Managing a machine-human workforce requires new leadership capabilities at companies, digital fluency, and updated organizational structures that support collaboration within the newly emerging machine/human workforce.

There is no standardized term yet for this workforce model but other terms are in use, such as:

• Hybrid Human–AI Workforce
• AI-Augmented Workforce
• Human–Machine Collaboration
• Digital Workforce.
• Superagency Systems

See Topic Brief: Machine-Human Workforce | Learn & Work Ecosystem Library

MCP stands for Meaning–Context–Probability, a concept that explains how information gains meaning when it’s used by people or by artificial intelligence (AI) systems. It helps us understand that information by itself is not enough—its value depends on how it is interpreted and used.

• Meaning refers to what the information represents.
• Context is the situation or setting in which the information is used.
• Probability reflects how likely it is that the information is relevant or accurate in that situation.

AI systems—and people—use all three parts together.  In today’s AI-driven world, understanding MCP helps users recognize that information is dynamic. The same data can have different meanings in different contexts—such as a “certificate” in higher education versus a “certificate” in cybersecurity training. AI tools rely on MCP principles to interpret search requests, connect related information, and deliver relevant results. For example, the Learn & Work Ecosystem Library Assistant bot (search tool) interprets a query based on what you typed (meaning), what part of the Library you’re searching (context), and what information is most likely to answer your question (probability). By keeping meaning, context, and probability in balance, the Library—and other AI-powered information systems—can help users find information that is not only accurate but also useful and understandable for their purpose.

Tips for Library users to apply MCP when searching with the AI Assistant bot at our site:

• Be clear about what you want to know. Example: Instead of “Tell me about credentials,” try “What are examples of stackable credentials in healthcare?” [meaning]
• Add where or how you’ll use the information. Example: “Explain credential transparency policies in community colleges.”  [context]
• Include what matters most to you—such as “most recent,” “U.S. examples,” or “employer-focused initiatives.”   [probability]

Related Terms

• Artificial Intelligence (AI) – the simulation of human intelligence in machines that can process information, learn, and make decisions.
• Digital Literacy – the ability to use digital tools and technologies to find, create, and communicate information.
• Information Architecture – the structured design of how information is organized, connected, and retrieved in a system.
• Information Literacy – the ability to locate, evaluate, and use information appropriately and responsibly.
• Metadata – data that describes other data to help systems and users find, understand, and use information effectively.

Refers to awareness and management of one’s own thinking and learning processes. This is often described as “thinking about thinking.” It includes planning how to approach a task, monitoring your understanding and progress, recognizing uncertainty or gaps in knowledge, evaluating results, and changing strategies when necessary.

Metacognition is particularly important when using AI because users need to consider what they understand themselves, where they need assistance, whether an AI response makes sense, and when additional information or verification is needed.

Refers to concerns that widespread use of artificial intelligence (AI), automation, and decision-support systems may prevent learners, workers, or professionals from fully developing foundational knowledge, judgment, or competencies because the technology performs too much of the underlying cognitive work.  Never-skilling describes situations in which individuals may never acquire certain capabilities because AI systems reduce the need to practice, struggle through, or independently perform essential tasks.

The concept is increasingly discussed in healthcare, education, software development, and other knowledge-intensive professions. In medicine, for example, educators and clinicians are raising concerns that heavy reliance on AI-assisted diagnosis, documentation, or clinical decision support tools could weaken development of clinical reasoning, pattern recognition, and diagnostic judgment among medical students and early-career practitioners.

Supporters of AI-assisted work argue that AI can improve efficiency, reduce routine burdens, and augment human performance. Critics caution that overreliance on AI may create long-term risks if workers become dependent on systems they do not fully understand or cannot effectively evaluate when errors occur.

The term is not yet standardized across research literature but reflects growing debates about how AI may reshape professional learning, expertise development, and workforce preparation.

See related term, deskilling, which refers to concerns in which workers gradually lose skills they once possessed: Deskilling | Learn & Work Ecosystem Library

Opacity (in artificial intelligence), often referred to as opaque AI, describes the degree to which an AI system’s internal processes and decision-making logic are difficult or impossible for humans to understand, interpret, or explain. An AI system is considered opaque when it produces outputs or recommendations without providing a clear, human-readable account of how those outcomes were generated. Opacity does not imply secrecy, error, or malicious intent. Rather, it reflects a structural limitation in interpretability, where the internal workings of the model are not easily transparent to users, decision-makers, or affected individuals.

AI opacity commonly arises in machine-learning and deep-learning systems that rely on large volumes of data, complex statistical relationships, and probabilistic modeling rather than explicit, rule-based instructions. In such systems, even developers may be unable to trace a specific output back to a sequence of logical steps.

In learning, credentialing, and workforce contexts, AI opacity raises important concerns related to trust, accountability, fairness, and governance, particularly when AI systems influence high-stakes decisions such as college admissions, employer hiring, assessment of learning, or credential recognition.

See Glossary Term:  AI Literacy vs. Adversarial Literacy | Learn & Work Ecosystem Library

See Glossary Term: Information Literacy | Learn & Work Ecosystem Library

See Topic Brief: Converging Terms:  Digital Literacy & Information Literacy | Learn & Work Ecosystem Library

Refers to the policies, structures, decision-making processes, and oversight mechanisms an organization uses to ensure that their employee compensation is fair, competitive, legally compliant, transparent, and aligned with organizational goals. It encompasses how decisions are made regarding salaries, incentives, bonuses, equity awards, promotions, and executive compensation, as well as who is responsible for approving and monitoring those decisions.

Strong pay governance helps organizations manage compensation consistently across the workforce while supporting recruitment, retention, performance, and regulatory compliance. Increasingly, organizations are also considering how AI-assisted compensation tools, pay transparency requirements, and skills-based pay models fit within their governance framework.

Polyworking refers to the practice of intentionally engaging in multiple forms of paid work simultaneously or sequentially—across different roles, employers, platforms, or income streams—as a sustained labor strategy rather than a temporary stopgap. Unlike traditional moonlighting or short-term gig work, polyworking reflects a structural shift in how individuals organize their working lives in response to economic volatility, skills-based labor markets, digital platforms, and longer, more nonlinear career pathways.

Polyworking may include combinations of fulltime employment, part-time roles, freelance or contract work, entrepreneurial activity, platform-mediated gig work, consulting, teaching, or other portfolio-based arrangements. For some workers, polyworking is driven by necessity—income instability, underemployment, or benefits gaps; for others, it is a strategic choice related to autonomy, skill development and diversification, career resilience, or other purposes.

From an employer and human resources (HR) perspective, polyworking can introduce both opportunity and concerns. HR leaders may become aware of polyworking through employee self-disclosure, outside employment or conflict-of-interest reviews, scheduling and performance patterns, or data security and confidentiality considerations.  Most organizations do not use the term polyworking explicitly. Instead, relevant policies typically appear under:

• Outside employment or “moonlighting” policies
• Conflict of interest policies
• Non-compete or non-solicitation clauses (where legally permitted)
• Confidentiality and intellectual property agreements
• Time and attendance expectations for salaried employees

These policies were largely designed for a single-job norm and may not fully account for portfolio careers, platform work, or skills-based side work that does not compete with the employer.

At the system level, polyworking challenges long-standing assumptions embedded in HR practices, labor policy, benefits design, credentialing, and workforce data systems, which typically presume one primary employer at a time. As polyworking becomes more common, it raises implications for performance management, skills recognition, portable benefits, worker protections, and how learning and employment records capture the full scope of an individual’s work and learning across a longer life course.

See Topic Brief: Polyworking — Workers, Employers & Shifting Labor Landscape | Learn & Work Ecosystem Library

Refers to a planning and risk assessment exercise in which a group imagines that a project, initiative, policy, or strategy has failed in the future and then works backward to identify what may have caused the failure. Unlike a traditional “postmortem,” which analyzes problems after something has gone wrong, a premortem is conducted before implementation begins. The goal is to identify hidden risks, flawed assumptions, unintended consequences, operational weaknesses, or overlooked challenges early enough to improve decision-making and reduce the likelihood of failure. The approach is especially valuable in periods of rapid change and uncertainty because it encourages people to think critically about vulnerabilities that may otherwise be ignored during optimistic planning processes. The growing use of premortems reflects a broader shift toward proactive planning approaches designed to help organizations prepare for uncertainty, complexity, and rapid technological change.

Premortems are increasingly used in strategic planning, project management, higher education transformation initiatives, technology implementation, AI governance and deployment, public policy, healthcare, business innovation, cybersecurity and risk management, and organizational change efforts

A typical premortem exercise asks participants to imagine: “It is two years from now, and this initiative failed. What happened?” Participants then identify possible causes such as unrealistic timelines, lack of stakeholder support, poor communication, insufficient training, ethical concerns, financial problems, technology limitations, data quality issues, regulatory barriers, and unintended impacts on workers or learners

Premortems are increasingly being used in discussions about artificial intelligence as organizations try to anticipate risks before deploying AI systems at scale. Examples:

• A college implementing AI advising systems may conduct a premortem to identify possible student privacy concerns, inaccurate recommendations, or unequal impacts on different student populations.
• A employer adopting AI hiring tools may use a premortem to examine risks related to bias, transparency, workforce resistance, or legal compliance.
• A government agency may conduct an AI premortem to explore cybersecurity vulnerabilities, misinformation risks, or public trust issues before launching an AI-enabled service.

Some organizations use AI tools during premortem exercises themselves. In these cases, AI systems help generate possible failure scenarios, identify overlooked risks, simulate stakeholder reactions, summarize patterns from prior project failures, or support scenario-planning activities. However, human judgment remains important because AI-generated risks may be incomplete, inaccurate, or lack organizational context.

Pro-worker technologies, including artificial intelligence, refer to technologies that expand human capabilities and increase the market value of worker expertise. The term is the subject of a NBER paper, ‘Building Pro-Worker Artificial Intelligence’.

Key aspects of this concept:

•  Technology is pro-worker if it makes human skills more useful rather than less necessary.
•  “New task-creating” tools are pro-worker because they generate demand for novel human expertise.
• AI is most effective as a collaborator that handles unstructured data to support high-stakes human decision-making.
• Misaligned incentives and a “pro-automation ideology” are leading the private market to underinvest in collaborative, pro-worker tools.
• Several policy shifts (e.g., tax code reform, anti-trust enforcement, intellectual property protections for worker expertise, and targeted investments in health care and education) are redirecting AI development.

The paper’s view is that AI’s potential to serve as a collaborator by extending human judgment, enabling new tasks, and accelerating skill acquisition, is transformative and currently underexploited.

An organization-level disclosure that clarifies what role AI did—and did not—play in producing knowledge, credentials, or guidance. These statements support trust, reproducibility, and informed interpretation while acknowledging the growing normalization of AI-assisted work.  These statements are increasingly required by publishers, journals, educators, funders, researchers, workforce organizations, and other oversight bodies to ensure transparency around AI-assisted creation, analysis, and decision-making. These statements commonly address:

• Scope of Use – Whether and how GenAI is used in content creation, editing, analysis, coding, translation, or administrative tasks; and whether use is optional, limited, or embedded in workflows.
• Human Oversight and Accountability – Describes review or approval processes and clarifies that humans retain responsibility for accuracy, interpretation, and final decisions.
• Data and Privacy Protections – Whether proprietary, personal, or sensitive data are excluded from GenAI tools Safeguards to prevent data leakage or unauthorized reuse.
• Validation and Quality Control – Methods used to check accuracy, bias, hallucinations, or misuse of information. Ongoing monitoring or auditing practices, where applicable.
• Ethical and Policy Alignment – Alignment with organizational policies, publisher guidelines, or professional standards; and disclosure of any restrictions imposed by funders, regulators, or partners.
• Transparency to Readers or Users – How AI use is communicated to audiences (e.g., footnotes, acknowledgments, methodology sections).

Refers to the process by which previously defined job roles, responsibilities, work processes, or organizational structures become unclear, fluid, or subject to reinterpretation due to ongoing technological, organizational, economic, or workplace changes.

The term is increasingly used in discussions about artificial intelligence (AI), automation, digital transformation, agentic AI systems, and new models of work in which traditional job boundaries are constantly being reshaped. Unlike role ambiguity, which typically describes uncertainty within an existing role, re-ambiguation emphasizes the repeated disruption and redefinition of roles after they have already been established.

Examples of re-ambiguation include:

• employees assuming responsibilities previously assigned to other departments
• AI systems performing portions of professional work once associated with specific occupations
• workers managing teams of AI agents
• organizations replacing traditional hierarchical reporting structures and organizational charts with project-based or cross-functional teams
• evolving expectations about who owns decisions, information, or outcomes.

Researchers and workplace analysts have noted that persistent uncertainty about roles and responsibilities can contribute to worker stress, reduced job satisfaction, and organizational inefficiency. In AI-enabled workplaces, re-ambiguation may become a recurring condition as technologies continuously reshape how work is organized and performed.

Structured or informal learning relationship in which an individual who is earlier in their career—often younger in age—shares specific expertise or perspectives (such as emerging technologies or workplace trends) with a more senior or experienced colleague. This approach inverts the traditional mentoring model, where knowledge typically flows from senior to junior staff.

The concept was popularized in 1999 by former General Electric CEO Jack Welch, who paired younger employees with senior executives to help leaders learn emerging internet technologies. Since then, reverse mentoring has expanded beyond technology training to include areas such as workplace culture, communication styles, and generational perspectives.

In today’s learn-and-work ecosystem, reverse mentoring is gaining renewed attention as organizations navigate rapid technological change—particularly the rise of artificial intelligence (AI). Workforce data suggests that younger employees are often more engaged with emerging tools, creating opportunities for them to guide more experienced colleagues in building new skills and confidence with technologies that are reshaping work.

Research also highlights the reciprocal nature of these relationships. Both participants benefit: senior employees gain exposure to new tools and perspectives, while earlier-career employees develop leadership skills, confidence, and a deeper appreciation for experience-based knowledge. These exchanges can strengthen collaboration across generations and reinforce a culture of continuous learning.

Organizations use reverse mentoring to:

• Build digital and AI fluency among senior leaders
• Surface emerging workforce trends and expectations
• Strengthen collaboration across multi-generational teams
• Support more inclusive and open workplace cultures
• Accelerate knowledge-sharing in rapidly changing environments

Reverse mentoring takes many forms, from formal programs with defined goals and timelines to informal partnerships that develop organically. Successful efforts typically emphasize mutual respect, clear expectations, and recognition that both participants bring valuable expertise to the relationship.

As workforce demographics shift and technological change accelerates, reverse mentoring is increasingly viewed as a practical strategy for bridging knowledge gaps, fostering adaptability, and supporting continuous learning across the lifespan.

Refers to the process of comparing an organization’s severance pay practices and separation benefits against those of similar employers, industries, or labor markets to determine whether policies are competitive, fair, financially sustainable, legally defensible, and aligned with current norms.

Severance benchmarking may include reviewing weeks of pay per year of service, minimum or maximum payouts, continuation of health benefits, treatment of bonuses or equity, outplacement services, and differences by employee level or job category. It is commonly used during layoffs, reorganizations, mergers, executive transitions, or when updating formal severance policies.

More recently, interest in severance benchmarking is expected to grow as organizations respond to automation and AI-related workforce changes, where employers are increasingly judged on both efficiency gains as well as how they support workers through job disruption and transition.

Refers to the extent to which information used in the hiring process provides reliable, authentic, and meaningful evidence about a job candidate, employer, or employment opportunity. High-quality signals increase confidence that information (e.g., a person’s identity, skills, credentials, work experience, assessments, references, or an employer’s identity and job posting) is accurate and can be trusted in making employment-related decisions.

Artificial intelligence and digital hiring platforms are making it easier to generate résumés, applications, profiles, credentials, job postings, and interview responses at scale, including “fraudulent” applications. In this context, traditional hiring signals may become less reliable indicators of a candidate’s qualifications or organizational legitimacy. Therefore, employers and hiring platforms are increasingly supplementing “traditional” signals with verification and risk indicators such as identity verification, credential verification, device and network information, location consistency, behavioral patterns, digital identity history, and fraud detection. Many of the verification measures are being implemented at the front end of the hiring process, not at the backend when employers traditionally conducted reference checks and credential verification.

It is increasingly critical to implement signal quality in the hiring process early in the process. Signal quality does not necessarily measure whether a candidate is qualified for a job. It addresses whether the information and evidence used to evaluate the candidate, employer and opportunity are sufficiently authentic, reliable, and trustworthy to support a hiring decision.

There is now an increasingly used category of “hiring technology” emerging around candidate identity, hiring fraud detection, and workforce verification. Several companies illustrate different parts of this emerging market:

• Greenhouse offers Real Talent, a suite specifically described as helping organizations reduce fraud, improve “signal quality,” and build trust across the hiring pipeline. This is especially significant because Greenhouse is a major applicant tracking/hiring platform, suggesting the concept is moving into mainstream recruiting infrastructure.
• Socure launched a Workforce Verification solution in 2025. Its approach evaluates identity, contact, device, behavioral, résumé, and other signals to identify potentially fraudulent applicants. It can apply different levels of verification depending on perceived risk rather than subjecting every applicant to exactly the same process. Socure distinguishes this from a traditional background check: a background check verifies information about someone, while workforce verification seeks to establish that the person behind that information is authentic.
• Crosschg offers candidate fraud detection and identity verification, including government-ID and biometric checks, device/IP fingerprinting, résumé analysis, deepfake detection, and reference validation. Its ApplicantX offering, launched in 2025, is positioned as an end-to-end hiring-fraud defense platform.
• TurboCheck focuses specifically on applicant fraud detection for recruiters. Its tools examine digital identity and contact information and can flag suspicious résumé, profile, email, phone, and identity patterns before candidates progress further in hiring. Its website identifies customers including staffing and recruiting organizations, providing evidence that these tools are being deployed in actual hiring environments.

It should be noted, not every digital signal is inherently fair or appropriate for employment decisions. Location, device reputation, behavioral patterns, and digital-history data can create privacy, discrimination, accessibility, and false-positive/negative concerns. A candidate traveling internationally, using a VPN, sharing a device, having a thin digital footprint, or changing locations could appear “unusual” without being fraudulent. These systems can raise important questions about what signals should be collected, how they are weighted, whether candidates can challenge errors, and whether a low confidence score becomes an automated barrier to employment.

Digital systems used by employers to deliver, curate, personalize, and measure workforce learning, upskilling, reskilling, and talent development. These platforms integrate learning content from both internal and external sources. They also recommend individualized learning pathways, track credentials and skill progression, and increasingly apply artificial intelligence to align employee development with organizational skill needs and workforce strategy.

Historically, corporate learning technologies fit into and developed within distinct types:

• Learning Management Systems (LMSs) focused on compliance training and course administration.
• Learning Experience Platforms (LXPs) emerged to improve learner engagement through content aggregation and personalized discovery.
• Corporate course library providers built proprietary catalogs for enterprise training.
• Skills and talent intelligence platforms focused on mapping workforce capabilities and informing talent strategy.

Over the past decade, these once-separate categories have steadily converged. LXPs now incorporate skills analytics, content providers embed AI-driven recommendation engines, and talent platforms integrate learning delivery tools. As a result, many modern enterprise learning platforms now combine content delivery, learner experience design, skills intelligence, and workforce analytics within a single system. The historical categories are still useful for understanding the origins and emphasis of these platforms but boundaries among them are increasingly blurred in practice.

Examples:

• Coursera for Business – Enterprise version of academic course platform offering role-aligned professional learning pathways.
• Degreed – Learning Experience Platform aggregating internal and external content with personalized learning pathways, credential tracking, and emerging skills analytics.
• Disprz – AI-driven enterprise learning and skill analytics platform integrating LMS, LXP, and workforce intelligence functions.
• Fuse – Learning platform emphasizing knowledge sharing, collaborative learning, and content aggregation.
• Learn Amp – Learning experience platform integrating learning delivery with performance alignment tools.
• LinkedIn Learning – Enterprise learning platform offering broad course libraries with data-driven recommendations.
• Skillsoft (Percipio) – Corporate learning platform providing proprietary content libraries with analytics and adaptive learning paths.
• Udemy Business – Enterprise training platform delivering on-demand course libraries with management and analytics tools.

Refers to the practice of identifying, searching for, extracting, or inferring specific skills from individuals, job postings, resumes, educational records, digital profiles, work histories, or labor market data for hiring, workforce planning, talent matching, or workforce analytics purposes.

Skills fishing typically involves employers, recruiters, platforms, or AI systems attempting to identify transferable, adjacent, hidden, emerging, or partially documented skills that may not be visible through traditional job titles, degrees, or resumes. Examples include AI systems scanning resumes to infer skills not explicitly listed, platforms matching workers to jobs based on inferred competencies, employers searching internal workforce data to identify employees with transferable skills, and talent marketplaces identifying workers who could transition into adjacent occupations.

The term is increasingly used in discussions about skills-first hiring, AI-driven recruiting, talent intelligence systems, labor market analytics, workforce platforms, credential matching, and automated skills extraction technologies.  The term may be used positively to describe broader talent discovery and workforce mobility opportunities. The term may also be used more cautiously to describe overly broad AI-driven candidate searching, aggressive talent mining, excessive workforce surveillance, or systems that infer skills without sufficient transparency or accuracy.

A platform or interconnected system that matches individuals with employment, career, learning, or development opportunities based on their skills, experience, credentials, interests, and other relevant information. Talent marketplaces are designed to make people’s skills and available opportunities more visible and to improve connections between talent and opportunity.

Talent marketplaces are commonly used in two contexts:

• Employer or internal talent marketplaces operate within an organization. They help employers identify and connect current employees with internal job openings, projects, temporary or stretch assignments, mentoring, professional development, and other opportunities. They can support internal mobility, employee development, skills identification, and more effective use of an organization’s existing talent.
• State or regional talent marketplaces operate across a broader education and workforce ecosystem. They may connect job seekers, workers, students, employers, education and training providers, workforce organizations, and government agencies. These marketplaces can help individuals identify jobs and career pathways, help employers find people with needed skills, and connect education and training opportunities with labor market demand.

Many contemporary talent marketplaces use skills data and artificial intelligence (AI) to help identify and recommend potential matches. They may also incorporate or connect with skills profiles, assessments, credentials, credential registries, Learning and Employment Records (LERs), digital wallets, career pathways, learning opportunities, and labor market information.

In the United States, a growing number of states are developing talent marketplaces as part of broader efforts to build skills-based education and workforce systems. These initiatives may be developed through partnerships involving state agencies, employers, education and training providers, workforce organizations, intermediaries, technology providers, and philanthropic organizations.

Whether used within an individual employer or across a state or region, talent marketplaces generally seek to improve the visibility and portability of skills and create stronger connections among people, learning, and work.

Refers to an employer strategy of moving employees into different roles, assignments, or out of the organization based on their skills, adaptability, and future potential. Traditionally, the term has described structured programs where employees rotate across departments or functions to broaden experience, build skills, and prepare for leadership. Increasingly, in the context of rapid technological change (including artificial intelligence), talent rotation can also involve reassessing workforce readiness: employees whose skills are adaptable may be retrained or redeployed into new roles, while those who cannot—or choose not to—adapt may be rotated out of (exited from) the organization.

See Topic Brief: Talent Rotation in the AI Era | Learn & Work Ecosystem Library

Refers to a workplace practice where employees are judged—formally or informally—by how much they use AI tools, rather than by the quality or impact of their work. Instead of focusing on outcomes, some organizations track how often employees prompt (query) AI systems or how many “tokens” they use. High usage may be seen as a sign of productivity or engagement, while low usage may raise concerns.

The term is derived from “tokens,” the units of text processed by large language models (LLMs). Each prompt, response, or workflow consumes tokens, allowing organizations to quantify AI usage at the individual or enterprise level. In some cases, this data is used to identify “power users,” compare employees, or demonstrate organizational adoption of AI technologies.

This practice has emerged as organizations seek to measure the value of generative AI. Because AI-enabled work can be difficult to evaluate directly, usage data—what is visible and easily counted—can become a stand-in for productivity.

Tokenmaxxing can create mixed incentives. It may encourage learning, experimentation, and workforce development, especially in early stages of AI adoption. In complex tasks such as research, writing, or multi-step problem-solving, iterative prompting is often necessary and can contribute to higher-quality results. However, it can also lead to employees generating more AI activity without improving results; e.g., by generating redundant prompts or over-iterating on tasks.

At its core, tokenmaxxing reflects an evolving challenge: how to define and measure performance in a workplace where human–AI collaboration is central to the work.

Alternative term: AI activity tracking

Refers to workers whose paths to career mobility are narrowing because artificial intelligence (AI) threatens not only their current jobs but also the jobs they would be most likely to move into next. Rather than having a clear pathway from an AI-exposed occupation into a less vulnerable one, trapped workers may find that nearby or realistic employment options are being disrupted by the same technological changes.

The term was introduced in 2026 by the Bipartisan Policy Center (BPC) in research examining worker mobility in the AI economy. BPC defines trapped workers as those whose vulnerability is compounded because AI threatens both their current role and their most realistic next job. Its analysis examined 595,000 actual worker transitions from 2019–2026 and found that workers tend to move within clusters of related occupations rather than simply shifting to any job requiring similar skills.

The concept shifts attention from whether a particular job is exposed to AI to whether workers have viable pathways out of exposed jobs. It therefore has implications for career navigation, education and training, reskilling, and workforce policy aimed at creating pathways into occupations with lower AI exposure.

Refers to the variation in how different people interact with artificial intelligence systems. Even when people use the same AI tool, their approaches, expectations, and interpretations may differ significantly. This variability influences how effective the AI is in practice and can affect whether systems succeed, stall, or produce inconsistent results in real-world settings. For example, users may:

• Ask questions in different ways.
• Use different levels of detail when providing instructions or context.
• Have different expectations of what AI systems can do.
• Interpret AI responses differently.
• Incorporate AI outputs into their work in different ways.

Because of this variation, the same AI system can produce very different results depending on the user.

In early testing and research on AI systems, differences in how people used these tools were sometimes treated as “noise” in the data—something to average out in order to measure system performance. More recent research suggests this variability is an important signal. Studying how people actually use AI can reveal why systems perform well for some users but not others, and why certain deployments succeed while others encounter unexpected risks.

Several related practices and terms include:

• Prompt engineering focuses on designing prompts—questions, instructions, templates, or workflows—that guide AI systems toward more reliable outputs.
• Prompt templates structure how users interact with AI systems.
• Prompt chaining sequences multiple prompts to generate more complex results, improved human–AI interface design, and efforts to strengthen AI literacy so users better understand how to interact effectively with AI tools.

An informal, emerging term that describes a way of creating digital tools, software, content, or workflows using artificial intelligence (AI) by focusing on what an individual wants to achieve rather than how it is technically built. Instead of writing detailed code or step-by-step instructions, users describe their goals, outcomes, or intent in natural language and rely on AI tools to generate and refine the underlying technical components through iteration and feedback.

The term originated in software development communities but increasingly reflects a broader shift across learning and work. AI tools now allow people without advanced technical training to build things that once required specialized expertise. As a result, capabilities previously considered advanced technical skills are becoming accessible to a much wider group of users.

In the learn-and-work ecosystem, vibe coding is best understood as a signal of changing skill expectations and work practices rather than a formal methodology or best practice. It highlights opportunities created by AI-enabled tools but also the ongoing need for human responsibility. Although AI can write code that works through vibe coding, it cannot guarantee the code is correct, safe, or appropriate. Human oversight remains essential, and the type of human oversight may change—from technical execution to judgment, validation, and responsibility.

Video recruiting refers to the use of recorded or live video technologies to screen and assess job candidates as part of the hiring process. Video-based hiring tools allow employers to evaluate communication skills, behavioral competencies, and job-readiness through asynchronous interviews, recorded skill demonstrations, and simulated work tasks. Once a niche practice, video recruiting has become increasingly mainstream as employers seek alternatives to resume-heavy screening in an era of AI-generated applications and growing hiring volumes.
Video recruiting is closely connected to the broader shift toward skills-based hiring, in which demonstrated capabilities and performance evidence may be prioritized over traditional credentials or self-reported qualifications.

See Topic Brief: Video Recruiting | Learn & Work Ecosystem Library

Refers to an emerging approach to career and workforce development that encourages individuals to grow according to their unique strengths, interests, skills, and opportunities rather than following predetermined career paths or traditional organizational hierarchies. The concept emphasizes adaptability, continuous learning, lateral skill development, and individualized career growth in response to changing workplace needs, particularly in an economy shaped by artificial intelligence (AI), technological innovation, and evolving workforce demands.

Rather than viewing career success solely through promotions or linear advancement, wildflowering recognizes that employees may thrive by developing expertise across projects, disciplines, and roles. Like wildflowers that flourish under different conditions without following a uniform pattern of growth, workers contribute value in diverse ways based on their talents, experiences, and the changing needs of organizations.

The term first gained attention in 2025 as a social media and relationship trend describing people who flourish when they stop trying to meet conventional expectations and instead grow in ways that reflect their own strengths and circumstances. More recently, the metaphor has been adapted to the workplace to describe individualized career development, where employees are encouraged to cultivate their capabilities rather than conform to standardized career ladders. Although the term originated outside the workforce literature, its application to work reflects broader trends toward skills-based organizations, internal talent mobility, AI-enabled work, continuous learning, career progression, and more flexible models of career development.

A term introduced by Harvard Business Review to describe AI-generated work that appears polished or authoritative but lacks depth, accuracy, or meaningful value. The term highlights the growing tendency for people to accept and use AI-produced content—such as definitions, reports, or summaries—without adequate human review or critical thought, leading to a decline in the overall quality of work.

Work slop has implications for both education and workforce systems. As AI tools become more integrated into learning and work environments, educators, employers, and policymakers rely on digital literacy, critical evaluation skills, and responsible AI use to ensure that efficiency does not come at the expense of quality or credibility.

A recurring workforce research framework developed by Microsoft through its WorkLab initiative to examine changing patterns in work, workplace technology adoption, digital collaboration, workforce expectations, and artificial intelligence (AI)-enabled organizational transformation. Launched in 2021 during the COVID-19 pandemic, the Work Trend Index combines global worker surveys, LinkedIn labor market data, and aggregated Microsoft workplace analytics to study how work practices and organizational models are evolving. Initially focused on remote and hybrid work, the annual Work Trend Index has increasingly shifted toward AI-enabled work, human-AI collaboration, digital labor, and organizational redesign, popularizing terms such as:

• Frontier Firms
• Human-Agent Teams
• Agent Boss
• Infinite Workday

See Initiative:  Research Report: Work Trend Index – Microsoft | Learn & Work Ecosystem Library

Refers to AI-generated audio, video, images, or digital identities used to impersonate employees, job applicants, executives, customers, or other individuals within employment and workforce settings. These synthetic media can be used for legitimate purposes such as training simulations or accessibility, but they are more commonly discussed in relation to fraud, cybersecurity threats, identity theft, hiring deception, financial scams, and workplace misinformation.

Examples include:

• fake job candidates using AI-generated identities during virtual interviews
• criminals impersonating executives to authorize financial transactions
• fabricated employee communications
• manipulated videos designed to damage reputations or spread false information.

As generative AI becomes more sophisticated, organizations are adopting stronger identity verification, authentication procedures, cybersecurity training, and governance policies to detect and reduce the risks associated with workforce deepfakes.