Northeastern AI Hiring Survey: What It Means for Entry-Level Job Seekers

A new Northeastern University survey of 8,174 people who hire or significantly influence hiring found that 54% use AI tools to screen or skim resumes at least to some extent. Among those who say AI has changed their hiring, 26% are hiring fewer entry-level workers and 28% now expect entry-level hires to do more, or higher-level, work. For job seekers, especially early-career ones, the practical message is to write resumes that survive AI summarizing and to show that you can already do more than a traditional beginner.

The report, AI in the Hiring Room, was published on November 1, 2026 by Northeastern's Institute for Information, the Internet and Democracy and covered by Northeastern Global News the next day. Below is what it actually measured, where the headline numbers need care, and what to change in your search because of it.

What the survey found

The figures come from Northeastern's AI and Society Survey, fielded from June 21 to August 11, 2026 with 38,375 adults in all 50 states and Washington, DC. Respondents who said they have authority to hire staff or significantly influence hiring decisions, 8,174 people, form the group the report calls hiring decision-makers. The main findings for job seekers:

AI resume screening is mainstream, and more common at bigger employers

• 54% of hiring decision-makers use AI-based tools to screen or skim resumes some (37%) or a lot (17%). The survey described these as tools that summarize, rank or extract information. Only 27% said not at all.
• Use rises with employer size: 48% at small employers (1 to 49 employees), 62% at mid-size employers (50 to 499) and 68% at organizations with 500 or more employees.
• Industry matters: 83% in information and technology and 71% in finance, insurance and real estate, compared with 39% in arts and entertainment, the lowest named industry.

Entry-level hiring is being cut by some and redefined by more

• 47% of hiring decision-makers say AI has changed how they make hiring decisions (34% somewhat, 13% a lot).
• Among that group, 26% say they are hiring fewer entry-level people as a result, against 7% hiring more.
• Another 28% hire about the same number but now expect entry-level employees to do more, or higher-level, work with AI. In information and technology that figure is 36%.
• The share hiring fewer entry-level workers barely varies by region or company size, and no industry differs from the overall 26% by more than the margin of error.

Degrees and AI use in school

• 38% say a college degree is now more valuable for entry-level hires since the rise of AI, including 17% who say much more valuable; 40% say about the same and 16% say less valuable.
• Asked how it would affect a hiring decision to learn that a candidate had used AI to assist with all of their writing or coding assignments, 40% said it would encourage hiring, 21% said it would discourage hiring and 39% said it would make no difference.
• On AI skills, about as many say AI-skilled candidates are easy to find (28%) as hard to find (25%), and a plurality (46%) say they do not specifically look for AI skills at all.

How to read these numbers carefully

Large surveys get compressed into headlines, and a few details change what these figures mean.

• Watch the base. The 26% "hiring fewer entry-level workers" figure is a share of the 47% who say AI has changed their hiring, not of all hiring decision-makers. Most hiring decision-makers did not report cutting entry-level hiring because of AI.
• These are reported intentions, not measured job counts. The survey records what decision-makers say they are doing. It does not count actual entry-level hires or openings.
• "Screening" is broad. The question covered tools that summarize, rank or extract information. That includes a recruiter reading an AI summary of your resume, not only automatic rejection.
• Method. The survey used an online, nonprobability sample with demographic quotas and weighting, and hiring decision-makers were identified by their own answer to one question. That is a common, reasonable approach for a survey this size, but it is not a census of employers.

Read together, the findings describe a market where AI is part of the first look at most applications, and where the entry-level bar is rising more often than the entry-level door is closing.

What this means for your resume

If a tool is summarizing or ranking your resume before a person reads it, your resume needs to make sense when compressed. That rewards the same habits good recruiters have always wanted, applied more strictly.

• Put the match where a summary will catch it. Open with a two-line summary that names the role you are targeting and your most relevant evidence. Our guide to writing a resume summary that gets read shows how.
• Use the posting's language for skills you really have. Tools that extract and rank lean on clear terms. If the job asks for SQL and you use SQL, say SQL. See how to tailor your resume to a job description.
• Write bullets that carry outcomes. A summary of "responsible for reports" says nothing. "Built a weekly sales report used by four regional managers" survives summarizing. Our guide to resume bullet points that show impact has examples.
• Keep the format simple. One column, standard headings and real text extract cleanly. See ATS-friendly resume format.
• Do not try to game the tool. Hidden keywords and white text tend to show up in summaries and look dishonest. Here is why hidden text backfires.

Tailoring for each role is where tools like Tailorapply help most: matching your real experience to each posting's language without rewriting from scratch every time.

What this means for entry-level candidates

The more common response in the survey was not fewer beginners but higher expectations of them. That suggests where to spend your effort.

Show work above a beginner's level

If employers expect entry-level staff to do more with AI, evidence of finished, realistic work matters more than coursework lists. A project that solves a real problem, an internship result with a clear outcome, or a portfolio piece close to the job's daily work all help. Our guides to listing projects on your resume and writing a resume with no experience cover how to present them.

Be ready to explain how you use AI

Only 21% said heavy AI use in school would discourage them, and 40% said it would encourage hiring. That is not permission to hand everything to a chatbot. It does mean you can describe, honestly and specifically, how you use AI tools and how you check their output. Interviewers are likely to probe your judgment, not just your familiarity. For what employers mean by AI skills, see do you need AI skills to get hired.

Choose where to apply with the data in mind

Smaller employers in the survey were less likely to use AI resume screening, and many small businesses hire through direct contact and referrals. If you are getting no response from large employers' portals, adding small businesses and referrals to your search changes who reads your application first. Our full entry-level job search plan for 2026 goes further.

What this means for experienced job seekers

The survey focuses on entry-level effects, but its screening figures apply to everyone. If you are applying to large organizations or to technology and finance roles, assume an AI tool may summarize your resume first. Experienced candidates often have long, dense resumes, and the most relevant achievement can get lost in a summary. Lead with what matters for this role, cut older detail, and make each bullet stand on its own.

The finding that 46% do not specifically look for AI skills is also a useful corrective. Do not rebuild your whole profile around AI if the role does not call for it. Show it where it is genuinely part of how you work.

A short checklist

• Rewrite your summary so a two-sentence AI summary of your resume would still say you fit the role.
• Match the posting's terms for skills you really have; never add ones you do not.
• Turn duties into outcome-based bullets that make sense on their own.
• For entry-level roles, lead with proof of finished, realistic work.
• Prepare a specific, honest answer about how you use AI tools and check their output.
• Balance large-employer portals with smaller employers and referrals.

For signs that a particular employer is using AI in its process, see how to tell if an employer uses AI to screen applications.