A tool that uses artificial intelligence to create resumes for individuals, from entry-level to executive level employment searches. The tool can write text, check the entire document for errors, and format the resume. The tool includes templates, writing tips, and automated features. Algorithms within the AI resume generator enables analysis of large amounts of data in order to provide individuals with tailormade content and design suggestions based on the user’s requests.

Since Applicant Tracking Systems (ATS) are increasingly used for first-stage resume review by many employers, there is growing pressure by job applicants to submit machine-readable professional resumes.

Examples of 17 of the best free resume builders recommended by C. Forsey at HubSpot’s  Marketing, Sales & Services blogs (April 2024):

• Zety: Best for Expert Resume Creation Tips
• Resume Genius: Best for Easy and Fast Resume Creation
• Wepik: Best for Customizing Pre-Made Resumes
• My Perfect Resume: Best for Guided Resume Creation Help
• Standard Resume: Best for Active LinkedIn Users
• Kickresume: Best for Quick and AI-Assisted Resume Creation
• Canva: Best for Design Creativity and Expression
• Pixpa: Best for Creating Online Resume Websites
• Indeed: Best for In-Platform Job Seekers
• com: Best for Minimalist Resume Creation
• Novoresume: Best for ATS-Friendly Resume Building
• VisualCV: Best for Multimedia Resumes
• CakeResume: Best for Resumes With an Online Portfolio
• Resume Now: Best for Time-Saving Resume Creation
• ResumeNerd: Best for Resume Writing Help
• Jofibo: Best for Comprehensive Guides
• Hloom: Best for Resume Templates

Transferring occurs from one educational institution to another. According to the National Center for Education Statistics, over a million students have transferred among colleges since 2015. Students can transfer from a community college or two-year program to a four-year college or university to graduate with both an associate and bachelor’s degree (this is called reverse transfer). Students can transfer in between all types of institutions – private, public, large, small, community, and research. Students can also transfer college credits from a high school dual-credit program to a two- or four-year program, and can use those credits toward their degree. Transfer includes the transition of credits from one institution to another, while still maintaining the value of those credits. Course articulation is an important part of that. Course articulation is the process of comparing the content of courses that are transferred between postsecondary institutions – one institution matches its courses or requirements to coursework completed at another institution. Transfer systems can be set up within states or systems. To make this process easier, some schools offer guaranteed transfer credit acceptance if students transfer from pre-approved schools.

Refer to the processes used by employers to verify the identity, credentials, criminal history, and other relevant background information of job candidates, employees, contractors, and vendors. These practices are essential for protecting workplace safety, ensuring regulatory compliance, and reducing the risk of legal, financial, and reputational harm.

Common areas of risk management include:

• Criminal background checks
• Education and credential verification
• Employment history verification
• Drug testing and fingerprinting
• Professional license and certification verification
• Global sanctions and compliance checks
• Identity and driver record verification
• Executive and contractor screening

These services are particularly critical in highly regulated industries such as healthcare, finance, transportation, education, and government. Accurate screening helps prevent credential fraud, supports trust in the workforce, and ensures alignment with federal, state, and industry-specific regulations.

 Related terms:

• Credential Verification
• Identity Verification
• Compliance and Regulatory Oversight
• Workforce Risk Management
• Employer Due Diligence

Examples of providers include: Checkr, Cisive, First Advantage, HireRight, Sterling Infosystems, and TazWorks, among others.

See Topic:  Comprehensive Background Screening and Risk Management, Especially in Highly Regulated Industry Sectors | Learn & Work Ecosystem Library

A security process to verify individuals’ identity by using their unique biological traits (fingerprints, facial recognition, eye iris scans, or voice patterns). Unlike traditional authentication methods that rely on passwords or PINs, biometric systems use attributes unique to each person, making them more secure and harder to replicate.

As defined by the Velocity Network Foundation, protocol used to achieve agreement on a single data value among distributed processes or systems.

According to Digital Promise, data sharing systems are technological platforms where data collected by multiple entities can be shared across and within multiple stakeholders’ organizations.

Refers to a streamlined college application process which results in immediate acceptance based on quantitative factors such as test scores.  Direct admission programs often eliminate many features of a traditional college application process, such as essay writing, letters of recommendation, and application fees. Direct admission applications can be evaluated more quickly, often using an automated process requiring less human oversight. Guaranteed admissions offered by some state colleges to residents who meet specific criteria are an example of direct admission. 

According to EDUCAUSE, refers to an institutional platform that provides electronic distribution of text, enabling students to access e-books/e-texts on mobile devices and offering additional features such as annotation, search across texts, and note sharing. Basic systems deliver existing packaged e-content; advanced systems enable collection, copyright clearance, and bundling of content similar to e-texts and e-coursepacks.

Refers to the practice by which individuals or criminal actors enroll in postsecondary courses or programs under fictitious or stolen identities (i.e., ghost students), occupy class seats, and sometimes obtain financial-aid benefits — yet never intend to participate in the courses or complete the credential. These fraudulent or fictitious enrollments exploit institutional admissions and aid systems, diverting spots and resources meant for actual learners, and can impose significant operational and financial risk on colleges and universities.

In ghosting situations:

• The “student” profile may be entirely synthetic (fabricated) or created using stolen personally identifiable information such as Social Security numbers, names, and birthdates.
• After admission and enrollment, the fake student may register for one or more courses (often online or asynchronous) but will not engage (e.g., no attendance, login, assignment submission) — or may engage just enough (sometimes using automation or AI tools) to avoid early detection.
• Once enrolled, the actor uses the enrolled status to apply for and receive financial aid (federal, state, institutional) or other funds, then disappears (or becomes dormant) before meaningful academic progress is made.
• The presence of ghosting students can displace legitimate students (by filling class seats), create distortions in enrolment data (giving a false sense of demand or success), trigger regulatory or audit risks (especially around aid disbursement), and cause identity theft harms to victims whose credentials were used without their knowledge.

In an era of increasing online- and hybrid-learning options, streamlined application processes, and large public financial-aid programs, higher education institutions are more vulnerable to ghosting students. For instance, in the U.S., community-colleges with open admission and minimal identity verification have reported large volumes of suspicious applications and ghost enrollments. The effects ripple across the learn-and-work ecosystem: financial-aid funds intended for actual upskilling and reskilling are diverted, actual learners may be blocked from essential courses or delayed, and institutional trust is eroded. In addition, organizations that rely on accurate institutional data (e.g., for workforce-development partnerships, credential tracking, analytics) may obtain misleading signals.

Note: The term should not be confused with the more colloquial term, “ghosting” when a student simply drops out or ceases participation while enrolled.

See Topic Brief: Ghosting Students (Ghost Students) | Learn & Work Ecosystem Library

Occurs when children who grow up in families with incomes below the poverty line are themselves poor as adults. Rates of intergenerational poverty in the United States are significantly higher for Black (37%) and Native American (46%) children than other children. Intergenerational poverty affects the overall economic output of the nation and individuals, and particularly burdens educational, criminal justice, and healthcare systems.

Interoperability is the ability of different information systems, devices or applications to connect, in a coordinated manner, within and across organizational boundaries to access, exchange and cooperatively use data amongst stakeholders.

According to EDUCAUSE, refers to a group of two or more colleges or universities—each with substantial autonomy and headed by a chief executive or operating officer—that fall under a single governing board served by a system chief executive officer who is not also the chief executive officer of any of the individual higher education institutions. Such a system is to be distinguished from a “flagship” campus with branch campuses and also from a group of campuses or systems, each with its own governing board, that is coordinated by some state body.

Technical infrastructure supporting badge issuers and display sites to ensure interoperability across open badge systems, established by Mozilla Foundation in 2012 and currently maintained by 1EdTech Consortium (formerly IMS Global)

As defined by the Velocity Network Foundation, in decentralized identity systems, a presentation is the act of showing credentials to a verifier. It involves selectively disclosing certain parts of a credential to prove authenticity without revealing more information than necessary, ensuring privacy-preserving verifications. 

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.

A skill set refers to the various types of abilities and knowledge that allows someone to successfully perform a job or accomplish specific tasks. A person’s skill set may include specific technical skills as well as a variety of general types of skills.

Skills classifications systems identify the combination of skills needed to successfully perform a job or accomplish specific tasks. There are many types of skills classification systems. Most include some variation of skills that are classified as:

• Job-specific:  skills needed to complete certain tasks within a position.
• Soft skills:  the behaviors and abilities that allow someone to work successfully with others such as the ability to communicate with others, resolve problems, and share ideas in the workplace.
• Hard skills:  the technical knowledge and abilities needed to perform specific tasks.

An example is the Australian Skills Classification, led by Jobs & Skills Australia. The Classification explores connections between skills and jobs and is intended to be a “common language” for core skills. The Classification identifies three categories of skills for Australian occupations: (1) 10 core competencies common to all jobs to varying degrees of proficiency; (2) specialist tasks that describe the day-to-day work within an occupation; (3) technology tools – software and hardware that are used in an occupation. The Classification groups similar skills into skills clusters. This enables the Classification to be explored by similar skills as well as occupations.

The National Science Foundation’s Systems Research Networks and Smart and Connected Communities Programs are part of NSF’s portfolio of investments in interdisciplinary research that advance fundamental knowledge about urban, rural, and other communities and systems.

SRS Programs define sustainable regional systems as connected urban and rural systems, including all systems in between, designed with the goal of measurably advancing the equitable well-being of people and the planet. Regions are defined as networks of urban, rural, and all systems in between, that make up a dynamic, symbiotic system with complex social and physical interactions. Urban systems are geographical areas with a high concentration of human activity and interactions, embedded within multi-scale interdependent social, engineered, and natural systems. Rural systems are any settlements with population, housing, economic activity, or areas not in an urban geographical area.

S&CC Program defines a smart and connected community as a community that synergistically integrates intelligent technologies with the natural and built environments, including infrastructure, to improve the social, economic, and environmental well-being of those who live, work, or travel within it. Communities are defined as having geographically-delineated boundaries – such as towns, cities, counties, neighborhoods, community districts, rural areas, and tribal regions – consisting of various populations, with the structure and ability to engage in meaningful ways with proposed research activities.

Refers to a concept promulgated by the National Association of Higher Education Systems (NASH), on behalf of its network of 51 higher education systems working collaboratively to address critical issues in higher education. The concept is founded in the recognition that systems working together are greater than the sum of their parts.  The core of their commitment is to leverage their power to convene and facilitate, along with their governing and policy-making authority, to build collaborations to support students and campuses—rather than trying to mediate competitive actions.

Technology tools and systems are hardware tools which include computers, mobile devices, servers, networks, printers, and other physical components that enable technologies; and operating systems which include software that manages computing resources and runs applications.

Equity at the workplace refers to the fairness of organizational systems and the absence of systematic and persistent disparities in the opportunities and resources available to employees, regardless of their demographic and social identities.