How Students and Recent Grads are Responding to the Rise of AI
A recent Lumina Foundation-Gallup poll found that 42 percent of bachelor’s degree students have reconsidered their degree choice because of Artificial Intelligence. Another 16 percent say they have already changed their field of study due to AI.
Students consider current labor market conditions when choosing a degree, so it isn’t too surprising that AI could shape students’ decisions today.1
But the study does not ask students exactly how AI has shaped their choices, only whether it has. Are students shying away from fields that have more exposure to AI, perhaps worried that AI will shrink the number of jobs available to them? Or are students shifting towards those fields, preparing for a future in which they will have to be comfortable using AI?
To find out, we can check enrollment for groups of degrees based on the AI exposure of the jobs that students with those degrees are likely to take, as shown in Figure 1.
As is clear, undergraduates are flocking towards the most-AI-exposed degrees, with enrollment in those degrees up 8 percent last year compared to 2017. This trend holds despite a notable decline in Computer Science degrees, one of the most-AI-exposed degrees, but whose decline is more than offset by increases in other exposed degrees like Engineering.
(A quick primer on our methodology before we move on: To estimate AI exposure, the general approach taken by researchers is to look at the tasks that workers perform in a given occupation and count how many of those tasks AI is likely to be good at. The more tasks associated with a given occupation that AI can perform, the higher the AI exposure of a worker in that occupation. AI exposure measures have important limitations, most notably that exposure can mean either augmenting tasks or automating tasks. We estimate the AI exposure of degrees chosen by college students by using the typical occupations that graduates of each degree end up choosing.2 See the Appendix for more details.)
Why are students still pursuing AI-exposed degrees? Part of the answer is shown in Figure 2: Wages are highest for those with the most-AI-exposed degrees.
In addition to our earlier work showing that AI-exposed occupations have higher incomes, this finding is also consistent with research from Morgan R. Frank, Alireza Javadian Sabet, Lisa Simon, Sarah H. Bana, and Renzhe Yu. They used data from LinkedIn profiles, combined with degree-level AI-exposure inferred from 3 million higher education course syllabi, and found that after the launch of ChatGPT, highly-exposed students enjoyed higher salaries and found jobs more quickly.
What About Recent Graduates?
While students today have the chance to switch degrees, those who graduated before the rise of generative AI are stuck with the degrees they have. It is theoretically plausible that young graduates with AI-exposed degrees face lower labor demand in their field and are forced to find work elsewhere. Think software developer turned retail manager. But is it in the data?
We can use American Community Survey (ACS) data to see the degrees and occupations of young graduates. The table below shows, for example, the top ten occupations of young graduates with a Computer and Information Sciences degree and how they have changed — or not changed — over time. The overwhelming majority of Computer Science graduates work in Computer and Mathematics occupations, the share having fallen less than a percentage point from the pre-COVID average to the years 2023 and 2024.
It doesn’t seem like young graduates with Computer Science degrees are upending their careers to find work. What about those with other AI-exposed degrees?
We first looked at the top three most-common occupations for each degree during the years 2015–2019. For Computer Science degree holders, this would be Computer & Mathematical, Office & Administrative Support, and Management occupations from the table above. We can then determine how many young graduates with each degree in subsequent years have also ended up in those same three occupations. The results are in Figure 3.
Across all three groups — Computer Science graduates; all graduates with the most-AI-exposed degrees including Computer Science; and all graduates with any degree — the share of graduates going to the most-common occupations has changed little over time. This result is consistent with a more-complex but generalized measure of how occupation choices change over time, as shown in the Appendix.
Taking Stock
There are several possible explanations for why we don’t see students avoiding AI-exposed degrees or young graduates working in occupations outside their field.
First, how AI is impacting the job market today is not at all clear. As Jed Kolko recently put it, this research is still in its early innings. If students are looking at the labor market to decide what to study, it’s not clear how AI would reorder their choice.
Second, the content of degrees or occupations themselves may change. If the curricula of AI-exposed degrees change to focus on skills complementary or resilient to AI, students may not feel the need to avoid those degrees. Similarly, the mix of tasks workers do in highly-AI-exposed occupations may be changing in ways that leave plenty of work for newly minted college graduates.
Third, as we noted above, the AI-exposure measures themselves have a lot of limitations. Knowing that a job contains tasks that an AI might be good at doesn’t necessarily tell you what will happen to employment in that job. This could help explain why Computer Science had decreased enrollment while engineering, which is also classified as highly AI-exposed, had increased enrollment.
Finally, there might be some behavioral frictions that slow the reallocation of students and graduates to occupations outside their field. Rather than looking far and wide for work, young highly-AI-exposed graduates might hold out for work in their field, waiting in unemployment longer.
Perhaps next year’s enrollment data will show movement away from AI-exposed degrees, but we doubt it. Not all “exposure” is the same. Occupation and sector-specific bottlenecks, diffusion dynamics, task reallocation, and output demand effects will jointly determine labor demand — and, in turn, the degrees students choose.
See our github with replication code here.
APPENDIX
Classifying degrees by AI exposure
We classify groupings of degrees by AI exposure using two pieces of information: (1) occupational-level AI-exposure measures and (2) the cross-sectional mapping of degrees to occupations. We utilize the Eloundou et al. (2024) GPT-4 beta scores, the most commonly used measure in the literature, as our occupational measure of AI exposure. This approach essentially walks occupational exposure back onto degrees based on how often individuals with each degree are observed in each occupation before the introduction of generative AI.
We start by using a sample of college graduate workers between the ages of 22 and 27, inclusive, in the 1-year ACS for years 2009 to 2019. Using these years avoids contamination with AI-induced changes in degree-to-occupation flows and changes during COVID. In each year, each individual in the sample is observed to be in one of 39 groupings of degrees in the ACS’s classification scheme and the Census 2010 4-digit occupation code. We compute the weighted mean of occupational AI-exposure scores for each degree-occupation pair. We then use this weighted mean to classify the 39 degrees into 5 quintiles based on AI exposure.
The AI-exposure scores are crosswalked to 2018 census occupation codes following our methodology in “AI and Jobs”, and to Census 2010 codes based on a crosswalk constructed using ACS samples with codes available under both vintages (occ2010 and occ for post-2018 samples). In some analyses, like Table 1 and Figure 2, we collapse occupation detail to 24 major SOC occupation groups. The degree-level exposure measures are shown below.
Measuring Enrollment
The ACS data is not well-suited to measuring how responsive college enrollment counts are to the introduction of generative AI. The ACS only reports college majors for those who have received their diploma. Students who graduated in 2023 and before were unable to adapt their degree choice in response to developments in AI that occurred in that year or after. Students graduating in 2024 would have needed to select a major by their sophomore or junior years (2023 or 2022), and so may have already been anticipating AI disruption when making this selection, but many would have been locked in to a major before ChatGPT’s release in November 2022. The 2025 cohort of college graduates with a bachelor’s degree are the most likely to have been capable of taking AI into account in their degree selection, but the 1-year ACS runs only through 2024.
To address these issues, we rely on the National Student Clearinghouse Research Center, which reports the number of enrollees — graduate and undergraduate — by declared major. This provides a more timely estimate of student majors. The clearinghouse received enrollment data submissions from 97 percent of all Title IV, degree-granting institutions in the U.S. in the fall of 2024. Coverage rates vary by year, but are handled with a weighing system described on their methodology pages here. Degree majors follow a different classification as the ACS, though most majors match up one-to-one.
Additional Figures
The figure below shows the change in degree-to-occupation shares for young college graduates with a Computer and Information Sciences degree. The figure shows the L1 distance, which is computed as the sum of absolute differences between shares in a given year and the base year. It answers the question: “How different is the occupation mix of students with a given degree now than it was in the past?”
If students were forced to look outside their field for a job, this should show up as an increase, even if temporary, at the end of each of these lines — the occupation mix would be different from prior years. Across different baselines, changes in degree-to-occupation shares are relatively stable over the last few years.
The chart below shows the percentage change in enrollment between 2024 and 2025 by degree and AI exposure. Bubble size is weighted by 2025 enrollment. We can see the 8 percent decline in enrollment in Computer and Information Sciences, which is among the most-exposed degrees. Engineering and business degrees, which are also highly exposed, saw rising enrollment. These two different trends net out to the slight increase in enrollment for all highly exposed degrees shown above. The linear trend is statistically insignificant at the 5% level (-17.17 [8.97]).
References
Blume-Kohout, M. E., & Clack, J. W. (2013). Are graduate students rational? Evidence from the market for biomedical scientists. PLoS One, 8(12), e82759.
Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2024). GPTs are GPTs: Labor market impact potential of LLMs. Science, 384(6702), 1306-1308.
Frank, M. R., Sabet, A. J., Simon, L., Bana, S. H., & Yu, R. (2026). AI-exposed jobs deteriorated before ChatGPT. arXiv preprint arXiv:2601.02554.
Long, M. C., Goldhaber, D., & Huntington-Klein, N. (2015). Do completed college majors respond to changes in wages?. Economics of Education Review, 49, 1-14.
Ryoo, J., & Rosen, S. (2004). The engineering labor market. Journal of political economy, 112(S1), S110-S140.
Research shows that college enrollees are responsive to current labor market conditions in their field. See Ryoo & Rosen (2004), Blume-Kohout & Clack (2013), and Long Goldhaber & Huntington-Klein (2015), for example, who find that the enrollment decisions of engineering students, biomedical sciences PhDs, and college students more generally are responsive to market conditions in their field of study.
We measure degree-level exposure by combining Eloundou et al. (2023) occupation-level AI-exposure measures with degree-to-occupation information between 2009 and 2019. See the Appendix for more details.
Another hypothesis: AI makes STEM courses easier. STEM majors have long been attractive because their reputation of leading to high-paying jobs, with enrollment bottlenecked by students' mathematical abilities.