Will AI Make College Degrees More Important? Employer Survey Insights

Will AI Make College Degrees More Important? Employer Survey Insights

Will AI make college degrees more important? Some recent employer surveys suggest that degrees are gaining value as artificial intelligence makes it harder to judge whether a candidate’s skills are genuine. That does not mean every student needs a four-year degree, or that a degree guarantees a job. It does mean students may need to compare credentials and demonstrable skills more carefully than before.

A Cengage survey found that 46% of employers believe the rise of AI will make college degrees more important, not less (Cengage Group, last year). A WGU survey of 3,128 U.S. hiring professionals found that 60% say AI has made it harder to evaluate candidates’ real skills (WGU, last month).

Together, those findings point to a practical question for high school students, college students, career changers, and families: Is a degree becoming more useful as a signal of preparation, while specific AI skills provide evidence of what a person can actually do? The available data supports that possibility. It does not prove that employers everywhere are returning to degree-only hiring.

What changed in employer surveys?

The clearest change is in how employers report viewing college degrees. In the WGU survey, 73% of employers considered college degrees important, compared with 68% the year before (WGU, last month). That is a change in reported employer opinion, not an independent measurement of the labor-market value of every degree.

Cengage reported a separate shift in entry-level requirements. In its survey, 71% of employers said they require a two- or four-year degree for entry-level roles, up from 55% in 2024 (Cengage Group, last year). The finding applies to the employers surveyed and to the requirement described. It should not be read as proof that every entry-level position now requires a degree.

The surveys also show that employers are having difficulty evaluating candidates. Nearly one in five hiring professionals in the WGU research identified confirming that they were interviewing a human rather than an AI tool as a top challenge (WGU, last month). The source does not establish that this challenge caused degree requirements to rise. It does help explain why employers might place more weight on credentials that are easier to verify than a polished application alone.

The same survey found that the share of employers still figuring out how to evaluate AI-related skills rose from 16% in 2025 to 32% in 2026 (WGU, last month). That result describes uncertainty in employer evaluation practices. It does not show that degree-holders are automatically better prepared to use AI, or that certificate-holders are less capable.

A careful interpretation is therefore narrower: employers report more difficulty judging skills, and they also report stronger support for degrees. Those findings are consistent with employers using credentials as one way to reduce uncertainty. They do not prove that AI caused the increase in degree requirements.

How AI is changing employer hiring requirements

The phrase “skills-based hiring” can make the debate sound more settled than it is. A degree requirement and a skills-based screen can exist in the same hiring process. One may be used to narrow the pool, while the other helps an employer decide which candidates show potential.

Nearly two-thirds of employers responding to a NACE survey reported using skills-based hiring to identify candidates with potential. Among employers that use those practices, more than two-thirds said they use them always or most of the time (NACE, last year). The denominator matters: the second figure refers to employers already using skills-based hiring, not to all employers.

This evidence complicates any claim that employers have abandoned skills-first hiring. At the same time, the Cengage finding on two- or four-year degree requirements suggests that skills-based screening has not eliminated formal education requirements. The surveys do not show whether the same employers use both approaches, but taken together they suggest that employers may be combining them.

That combination is sometimes described as a “yes, and” approach: yes, a candidate should show useful skills, and a degree or another credential may still matter. The phrase is useful as a summary, but it should not become a universal rule. Hiring practices vary by employer, occupation, and role, and the supplied surveys do not establish one standard process.

Regional results reinforce that caution. A WGU analysis found that between 70% and 86% of employers across regions reported that AI was shaping the candidates they sought and the skills they prioritized (WGU regional analysis, earlier this year). New York employers had the highest reported result, with 86% saying AI had fundamentally changed their hiring decisions (WGU regional analysis, earlier this year).

The regional range matters for students comparing opportunities. A hiring pattern in one location should not automatically be treated as a national requirement. Before choosing a program, readers should examine postings from the employers and locations that actually interest them.

Are college degrees more valuable in the age of AI, or just easier to trust?

The data supports a distinction between value and verification.

A degree can signal that a person completed a defined program of study. It may help an employer sort applicants when résumés, portfolios, take-home assignments, or other application materials are difficult to evaluate. But the surveys do not show that a degree proves current AI fluency, practical judgment, or job performance.

That distinction matters because AI can affect both sides of a hiring process. Employers may use AI tools to review applications or structure interviews, while applicants may use AI tools to produce application materials or complete assignments. The WGU findings establish that hiring professionals are having more difficulty evaluating real skills. They do not separately measure which application materials are most affected.

A certificate has a similar limitation. In the WGU survey, 91% of employers viewed certificates as valuable indicators of skills, up from 86% the previous year (WGU, last month). That is meaningful evidence that certificates are being taken seriously by many surveyed employers. It does not show that all certificates carry equal weight, or that a certificate can replace a degree in every occupation.

The regional WGU analysis found that 21% of employers nationwide were prioritizing candidates with AI-related certifications to validate technical fluency (WGU regional analysis, earlier this year). For a student, this is a reason to investigate which certifications appear in relevant postings, not a reason to collect credentials without a clear purpose.

The findings suggest that degrees and certificates may function as complementary signals. They do not establish that employers require both, or that adding a certificate always improves a candidate’s prospects. A useful certificate should connect to the work a student hopes to do and be recognizable to the employers being considered.

What employers and colleges may still disagree about

Another part of the evidence concerns what students learn, not only which credential they hold.

Cengage found that employers ranked job-specific technical abilities as their top priority, while educators placed those abilities last among the priorities in the survey and instead favored skills such as critical thinking and problem-solving (Cengage Group, last year). This does not mean every employer values technical skills above judgment, or that every educator overlooks technical preparation. It shows a difference between the priorities reported by the two groups.

That difference can affect how students evaluate a program. A course may emphasize broad reasoning, writing, research, and problem-solving. Those abilities can be useful across jobs, but a student may still need opportunities to apply them with current tools or in a specific technical setting. A program with a strong general education foundation may not include every skill named in a particular job posting.

The Cengage survey also found that half of educators said they devote 20% or less of their curriculum to workforce skills. In the same research, 35% of surveyed graduates wished their education program had worked more closely with employers to build career-relevant courses and skills (Cengage Group, last year). These are reported perceptions and allocations, not an independent audit of every program.

At the same time, 90% of employers said it was important for graduates to develop AI-related skills while in college, and 81% expressed confidence that colleges and universities were helping students build competencies aligned with current and emerging workplace applications of AI (AAC&U, last year). Those findings create a contrast: employers may have broad confidence in colleges while still prioritizing specific technical abilities and, in some cases, wanting closer employer collaboration.

For students, the practical lesson is not to choose between “soft skills” and AI skills. Strong programs may develop both, but the balance can vary. The important question is how a specific program turns coursework into evidence a student can explain and demonstrate.

What alternatives should students compare?

The survey findings do not show that every student needs a four-year degree. They also do not show that a certificate, associate degree, apprenticeship, work-based route, or other pathway has the same value in every field. A pathway decision should begin with the target occupation and its stated requirements, not with a general prediction about AI.

A high school student considering college can compare programs by looking for the following:

• How often do two- or four-year degree requirements appear in five to 10 job postings for the target field?
• Do those postings name a particular AI tool, technical skill, certificate, or type of project?
• Does the program include supervised projects, labs, portfolios, internships, or other ways to practice the work?
• Can the program explain how students develop AI literacy, rather than simply listing AI in a course title?
• Do employers in the intended field recognize the credential?
• Are there lower-cost or shorter pathways that meet the same stated requirements?

A current college student can use the same questions to choose electives, projects, or certificates. A career changer can compare the time and scope of a degree with a more targeted credential, while checking whether employers in the intended field actually mention that credential.

Job postings provide signals, not proof. If a certificate appears in several postings, that is a reason to investigate its purpose and recognition. It is not evidence that every employer requires it, and it is not a reason to assume that any certificate will have the same effect.

Students should also ask a program representative or career services office specific questions:

• Which courses include hands-on work with relevant tools?
• Can students complete a supervised project that reflects the target occupation?
• Which credentials do local or target employers mention?
• How are students expected to describe responsible AI use in a portfolio or interview?
• What requirements vary by employer, location, or occupation?

These questions turn broad survey findings into a manageable comparison. They also protect students from paying for credentials based only on a trend.

What the surveys can and cannot answer

The surveys show stronger reported support for degrees and certificates, increased difficulty evaluating candidates’ real skills, continued use of skills-based hiring, and growing attention to AI-related preparation. They do not show that every degree has gained value, that every certificate is useful, or that AI has caused the change in hiring requirements.

They also do not measure job offers, wages, retention, or performance for degree-holders compared with certificate-holders. A degree may help a candidate pass an initial screen while leaving the employer unconvinced about specific technical abilities. A certificate may help demonstrate focused preparation while not meeting a degree requirement. The outcome depends on the employer and role being considered.

The most defensible answer to the central question is therefore qualified. AI may be making college degrees more important as one way employers assess preparation and reduce uncertainty. The same evidence suggests that a degree alone may not communicate the specific AI skills a job requires.

Before choosing a pathway, review five to 10 postings in the field, record how often a degree appears, note any named tools or certifications, and compare programs for supervised projects and employer recognition. Current students should ask an adviser or career services office which AI-related courses, projects, and micro-credentials are available before graduation. Career changers should verify the requirements with employers in the target field rather than assuming that a national survey applies to every job.