the rise of generative artificial intelligence (AI), spurred by OpenAI’s ChatGPT in November 2022, has ignited widespread discussion about its potential to reshape workplaces and redefine occupations. A study released on February 26, 2025, delves into how this technological shift may influence postsecondary institutions and their students. The analysis, leveraging data from the National Survey of College Graduates 2013-2021, identifies college majors, demographic groups, and educational institutions that could be substantially affected by generative AI’s predicted impact on the labor market. mathematics and Computer Science-related fields, Political Science and Government, and Accounting may be most affected by generative AI.

The study emphasizes the importance of understanding how generative AI’s effect on labor markets may influence postsecondary institutions and their students, considering the technology’s potential to reshape workplace dynamics and the types of skills that are valued.Postsecondary institutions play a notable role in preparing students for the labor market, and their ability to respond to these changes will influence both individual students’ career trajectories and broader economic outcomes.

The report highlights key findings regarding the potential impact of generative AI on various aspects of higher education and the workforce. These findings offer valuable insights for institutions and students alike as thay navigate the evolving landscape of education and employment.

Key Findings: Majors, Demographics, and Institutions at the Forefront

The analysis pinpoints specific college majors that may experience significant shifts due to generative AI. It also examines how different demographic groups might be affected and identifies the types of educational institutions that could feel the impact most acutely.

College Majors

According to the study, Mathematics and Computer Science-related fields, political Science and Government, and Accounting may be most affected by generative AI. the report acknowledges uncertainty regarding whether these fields will face automation or augmentation. If automation prevails, demand for graduates in these areas could decline, leading to staffing and engagement reductions in affiliated departments. Conversely, augmentation could spur growth in these programs.

The potential impact on Mathematics and computer Science is especially noteworthy, given the increasing reliance on these skills across various industries. Political Science and Government, along with Accounting, also face potential disruption, highlighting the broad reach of generative AI’s influence.

Demographic Groups

The study suggests that generative AI’s impact may vary across demographic groups. College majors with higher percentages of Hispanic and Asian graduates might potentially be more exposed to certain aspects of generative AI, while college majors with higher percentages of females, whites, and blacks might potentially be less exposed. The implications of this differential exposure are unclear, as it remains uncertain whether it will lead to improved or worsened outcomes for Hispanic and Asian graduates.

Understanding these demographic nuances is crucial for developing targeted strategies to support students from all backgrounds as they prepare for the future workforce. Further research is needed to fully assess the potential consequences of these trends.

Educational institutions

The report indicates that the impact of generative AI may be more pronounced at Liberal Arts I institutions, which award a relatively high percentage of social Sciences degrees, and Research University I institutions, which award a relatively high percentage of STEM degrees. This suggests that institutions with a strong focus on these disciplines may need to adapt their curricula and resources to address the changing demands of the labor market.

Liberal Arts I institutions, known for their emphasis on critical thinking and dialogue skills, may need to integrate AI-related topics into their social sciences programs. Research University I institutions, with their focus on STEM fields, may need to explore how AI can enhance research and innovation in these areas.

Data and Methodology: Unveiling the Insights

The analysis relies on data from two primary sources: Generative AI occupational Exposure (AIOE) measures and the National Survey of College Graduates (NSCG). These datasets provide an extensive view of the intersection between AI and the labor market.

Generative AI Occupational Exposure (AIOE) Measures

The AIOE measures, derived from research, assess the exposure of various occupations to language modeling and image generation, two key capabilities of generative AI.These measures are based on survey responses from freelance workers regarding the relatedness of these applications to occupational abilities.

national Survey of college Graduates (NSCG)

The NSCG, a nationally representative survey of college graduates, provides detailed demographic and labor market data. The study utilizes data from the 2013-2021 waves of the NSCG, employing sampling weights to ensure the calculations are representative of the entire college-educated population.

Linking AIOE Measures to NSCG

To analyze the impact of generative AI on college majors and educational institutions, the study links the AIOE measures to the NSCG data. This involves matching NSCG occupation codes to Standard Occupational Classification (SOC) codes and computing AIOE averages for college fields of study and educational institution types.

The study also provides summary statistics of the weighted sample, revealing that 51.17 percent of college graduates are female, 79.51 percent are white, 8.03 percent are black, 9.11 percent are Asian,and 8.8 percent are Hispanic. The mean real annual salary is approximately $88,037. Furthermore, the data indicates that college graduates’ occupational exposure to language modeling (0.76) is higher than their exposure to image generation (0.56).

Conclusion: Navigating the AI-Driven Future

As generative AI continues to evolve and reshape the labor market, it is crucial for postsecondary institutions and students to stay informed and adapt accordingly. The findings of this study offer valuable insights into the potential impact of AI on various college majors, demographic groups, and educational institutions.

By monitoring these trends closely and proactively addressing the challenges and opportunities presented by generative AI,institutions and students can ensure they are well-prepared for the future of work.The ability to adapt to these changes will influence both individual students’ career trajectories and broader economic outcomes.

A recent analysis has shed light on which college majors may be most affected by the rise of generative artificial intelligence and its predicted impact on the labor market. The study indicates that mathematics and Computer Science-related fields, along with Engineering and Technology-related fields, are likely to experience the most significant effects. Furthermore, the analysis identifies Political Science and Government and Accounting as two of the most popular majors that could be substantially impacted by these technological advancements. Postsecondary institutions and students are advised to closely monitor these fields to adapt effectively to the evolving professional landscape.

The analysis focuses on identifying college majors that could be significantly influenced by generative AI’s growing presence in the labor market. The findings suggest that certain fields, particularly those related to STEM, may face considerable changes.The study uses z-scores for the AIOE (AI Occupational Exposure) measures of both language modeling and image generation to assess the potential impact on various college major categories.

Key Findings: Exposure to Generative AI by College Major

The study categorizes college majors and assesses their exposure to generative AI based on language modeling and image generation capabilities. The results highlight specific fields that may need to adapt their curricula and skill development strategies to prepare students for the future job market.

STEM Fields at the Forefront

According to the analysis, Mathematics and Computer science-related fields show high scores for both language modeling and image generation exposure. This suggests that these fields may experience significant changes in job roles and required skills as AI technologies become more prevalent. The study also notes that Engineering and Technology-related fields score the highest for image generation exposure.

Generative AI Exposure across College Major Categories

The analysis categorizes college majors and quantifies their exposure to generative AI using Z-scores for both language modeling and image generation. The data reveals significant disparities across different fields of study.

According to the analysis, STEM – Mathematics and Computer Science Fields exhibit a high exposure, with a 5.81 percent representation, a language modeling Z-score of 1.1, and an image generation Z-score of 1.49.

In contrast, STEM – Biological and Health Science Fields, representing 14.2 percent of majors, show negative Z-scores for both language modeling (-2.02) and image generation (-1.09), indicating lower exposure to these AI technologies.

Other categories, such as STEM – Physical Science Fields (2.92 percent),STEM – Engineering and Technology Fields (9.93 percent), education Fields (8.92 percent), Economics and Business Fields (22.7 percent), Liberal Arts – Arts and Humanities fields (14.3 percent), Liberal Arts – Social Science Fields (11.7 percent), and Liberal Arts – Other Non-Science and Engineering Fields (9.52 percent), each demonstrate varying degrees of exposure to generative AI, as measured by their respective Z-scores.

Table 2. College Major Categories and Their Exposure to Generative AI

Top College Majors most Exposed

The analysis further pinpoints the specific college majors that exhibit the highest exposure to generative AI, considering both language modeling and image generation. These findings emphasize the significant impact on Computer-related and Engineering-related fields.

For language modeling, Astronomy and astrophysics leads with a 0.02 percent representation and a Z-score of 1.93.actuarial science follows with 0.08 percent and a Z-score of 1.72, and Computer teacher education with 0.01 percent and a Z-score of 1.71.

In terms of image generation, Computer and systems engineering tops the list with 0.68 percent and a Z-score of 2.34. Civil engineering is next with 1.11 percent and a Z-score of 2.25, closely followed by Environmental engineering with 0.08 percent and a Z-score of 2.23.

Table 3. College Majors Most Exposed to Generative AI (Top 3 Shown)

Implications for Education and career Planning

The findings of this analysis carry significant implications for both postsecondary institutions and students. As generative AI continues its rapid evolution, it is imperative that educational programs adapt to equip students with the necessary skills and knowledge to succeed in a transforming job market.

the analysis suggests that as generative AI continues to evolve,it is indeed crucial for educational programs to adapt and equip students with the skills and knowledge needed to thrive in a changing job market. monitoring the impact of AI on specific fields of study will enable institutions to make informed decisions about curriculum development and career guidance.

For students, understanding the potential impact of generative AI on their chosen field is crucial for making informed decisions about their education and career paths. Developing skills that complement AI technologies, such as critical thinking, creativity, and complex problem-solving, will be essential for future workforce success.

The analysis emphasizes the importance of students understanding the potential impact of generative AI on their chosen field can help them make strategic decisions about their education and career paths. Developing skills that complement AI technologies, such as critical thinking, creativity, and complex problem-solving, will be essential for success in the future workforce.

A comprehensive study has recently explored the potential impacts of generative artificial intelligence on various college majors, demographic groups, and educational institutions. The analysis specifically focuses on language modeling and image generation, uncovering significant disparities in exposure across different fields of study and demographic categories. The findings suggest that certain groups may face greater impacts from the increasing prevalence of generative AI in the labor market, raising crucial questions about future workforce readiness and educational adaptation.

The study’s methodology includes an examination of exposure to generative AI based on the 1994 Carnegie classification system.This classification is determined by the type and number of academic degrees awarded by each institution, providing a standardized framework for comparing different colleges and universities.

Exposure Scores by College Major: A Detailed Look

The analysis reveals considerable variation in the majors with the highest language modeling scores. While some fields related to Mathematics and Computer Science appear prominently in the top ten, the study also highlights significant exposure in other disciplines, indicating a broader impact than initially anticipated.

Notably, Accounting and Political Science and Government, two of the most popular college majors (by percentage), are also among the top ten majors most exposed to language modeling. This finding suggests that generative AI could significantly alter the skill sets required in these fields, potentially impacting future job prospects and curriculum design.

Conclusion: navigating the Future of Education in the Age of AI

The study’s findings underscore the importance of understanding and addressing the uneven impact of generative AI across different academic disciplines. As AI technologies continue to evolve, educational institutions and policymakers must proactively adapt curricula and training programs to ensure that students are equipped with the skills necessary to thrive in an AI-driven workforce. Further research is needed to fully understand the long-term implications of these trends and to develop strategies for mitigating potential negative consequences.

Generative AI Exposure across College Majors

A recent study sheds light on the varying degrees to which different college majors are exposed to generative AI technologies, specifically language modeling and image generation. The research suggests that certain fields of study are more susceptible to the influence of AI, potentially leading to significant shifts in the labor market and educational landscape.

The study categorizes college majors based on their exposure to generative AI, providing a detailed overview of the fields most likely to be affected. This categorization allows for a nuanced understanding of how AI might reshape various professions and academic disciplines.

The analysis reveals a spectrum of exposure levels, with some majors facing a higher potential for automation or augmentation through AI, while others remain relatively less impacted. This disparity underscores the need for proactive adaptation strategies within educational institutions and workforce development programs.

Demographic Depiction and Generative AI Exposure

The study further investigates how generative AI’s potential impact on college majors may disproportionately affect different demographic groups. The analysis highlights that unequal effects on various demographic groups could exacerbate existing gaps in pay and work opportunities.

The research indicates that college majors with higher percentages of Hispanic graduates are significantly more exposed to language modeling,with a correlation coefficient of 0.21.Additionally, college majors with higher percentages of Asian graduates are significantly more exposed to image generation, showing a correlation coefficient of 0.47.

The study acknowledges that the implications of these correlations are ambiguous. Given that high exposure to generative AI may mean automation or augmentation of the jobs of these demographic groups, it is ambiguous whether the significant positive correlations suggest that the welfare of Hispanic and Asian graduates will be improved or worsened.

Conversely, college majors with higher percentages of female, white, and black graduates are significantly less exposed to image generation, with correlation coefficients of -0.67, -0.29, and -0.25, respectively. This suggests that these groups might potentially be less affected by this particular application of generative AI.

Impact on Educational Institutions

The analysis extends to examining which types of educational institutions may be most affected by generative AI’s predicted impact on the labor market. The study considers the exposure to generative AI based on the 1994 Carnegie classification, which categorizes institutions by the type and number of academic degrees they award.

The findings reveal that Liberal Arts I institutions have the highest z-score for language modeling exposure at both the bachelor’s and graduate levels. Research University I institutions have the highest z-score for image generation exposure.

This suggests that any effect generative AI may have on labor demand would translate into a larger effect in student engagement and staffing at these educational institutions.

College Majors and Generative AI Exposure

The rise of generative AI is poised to reshape the labor market,but its impact is not uniform across all fields of study. A recent analysis indicates that certain college majors have significantly higher exposure to generative AI applications than others. Specifically,Mathematics and Computer Science-related fields show high exposure to both language modeling and image generation. Engineering and technology-related fields are particularly exposed to image generation, while Accounting and Political Science and Government majors face considerable exposure to language modeling.

The data suggests a strong correlation between these majors and occupations already impacted by AI.A considerable majority, 67 percent, of graduates with majors in Mathematics and computer Science, engineering and technology, and Accounting enter mathematics, computer science, engineering, and management occupations. These fields have been identified as having relatively high occupational exposure scores in previous research.

in contrast, 30 percent of Political Science and Government majors find employment in management occupations, while 35 percent enter legal or office/management roles. This distribution highlights the diverse pathways for graduates in these fields and the potential for AI to augment or automate their tasks.

This aligns with findings from Bick et al. (2024), whose early study on generative AI adoption found that 46 percent of workers who majored in STEM use generative AI at work, compared with 40 percent for workers who majored in business, economics, or dialogue and 22 percent for all other majors, including liberal arts and humanities. Generative AI adoption at work is highest for computer/mathematical occupations (49.6 percent) and management occupations (49.0 percent).

the findings also comport with Mohnen and lee (2024), who demonstrated that the demand for AI skills in computer and mathematical occupations has grown significantly since 2010, especially for computer scientists, data scientists, and statisticians. The authors noted that those in computer and mathematical occupations are largely responsible for the development and implementation of AI technologies.

The implications of this exposure are twofold. If associated occupations are automated, the labor demand for graduates in STEM fields, Accounting, and Political Science and Government may decrease, potentially leading to declines in staffing and student engagement within these departments. Conversely, if these occupations are augmented by AI, the demand for graduates may rise, fostering growth in affiliated departments.

Demographic Disparities in AI Exposure

The analysis also reveals that the potential impact of generative AI may not be evenly distributed across demographic groups. College majors with higher percentages of Hispanic graduates are significantly more exposed to language modeling, while those with higher percentages of Asian graduates are significantly more exposed to image generation. Furthermore, majors with higher percentages of females, whites, and blacks may be less exposed to image generation compared to other demographic groups.

These correlations raise vital questions about equity and access in the evolving labor market. Whether these positive correlations translate into increased or decreased welfare for Hispanic and Asian graduates remains uncertain, highlighting the need for further examination and proactive measures to ensure equitable outcomes.

These results align with early empirical studies showing that generative AI use is unequal across varying demographics (Aldasoro et al. 2024a; Aldasoro et al. 2024b; Bick et al. 2024; Emanuel and harrington 2024; Humlum and vestergaard 2024).

Impact on Educational Institutions

The study suggests that certain types of educational institutions may be more profoundly affected by generative AI’s impact on the labor market. Liberal Arts I and Research University I institutions are identified as potentially facing the most significant adjustments.

liberal Arts I institutions, characterized by a high percentage of Social Science majors, may experience pronounced changes in staffing and student engagement due to language modeling’s effect on labor demand. Conversely, Research University I institutions, with a higher concentration of STEM majors, may see the most significant shifts driven by image generation’s impact.

Data Summary

the following table summarizes the exposure to language modeling and image generation across different types of educational institutions:

The rapid integration of generative artificial intelligence (AI) into educational curricula is sparking widespread discussion about its potential effects on the future of work and the evolving skills demanded of the workforce. A new FEDS Note, authored by Jean Xiao Timmerman and released on February 26, 2025, delves into this emerging trend, providing critical insights into how educational institutions are adapting to the rise of AI technologies. The FEDS Note, officially titled “Educational Exposure to Generative Artificial Intelligence,” aims to illuminate the preparedness of the next generation for an increasingly AI-driven labor market. This analysis is crucial for understanding the shifting dynamics within education and the broader economy.

Timmerman’s analysis explores the multifaceted implications of exposing students to generative AI tools and concepts. As these technologies become more prevalent, understanding their impact on learning outcomes and skill development is paramount. The FEDS Note serves as a valuable resource for educators, policymakers, and anyone interested in the intersection of AI and education.

The Rise of AI in Education

The proliferation of generative AI tools, such as ChatGPT and other large language models (LLMs), has ignited both interest and concern across various sectors, most notably in education. These technologies, capable of generating text, images, and other media, are rapidly finding their way into classrooms and training programs. this influx raises fundamental questions about their impact on learning outcomes and the development of essential skills.

Educational institutions are actively grappling with the optimal methods for incorporating these powerful tools into their curricula. While some view generative AI as a transformative force capable of enhancing learning and fostering creativity, others express valid concerns about potential misuse, including plagiarism and the erosion of critical thinking skills. This debate underscores the need for careful consideration and strategic implementation of AI in educational settings.

Key findings from the FEDS Note

Jean Xiao Timmerman’s FEDS Note provides a detailed examination of the current landscape of AI education. While this summary does not detail the specific findings, the note likely explores the types of educational programs that are incorporating generative AI, the specific skills being taught, and the potential benefits and challenges associated with this integration. Understanding these elements is crucial for shaping effective AI education strategies.

The analysis likely considers the implications for various fields, ranging from computer science and engineering to the humanities and social sciences. It may also address the ethical considerations surrounding the use of AI in education, such as bias, fairness, and openness.These ethical dimensions are increasingly important as AI becomes more deeply embedded in educational practices.

Implications for the Labor Market

One of the central themes of Timmerman’s FEDS Note is the critical connection between AI education and the future of the labor market. As AI technologies continue to advance and automate various tasks, the demand for workers with AI-related skills is expected to grow exponentially. Educational institutions, thus, play a pivotal role in preparing students to meet this evolving demand and thrive in an AI-driven economy.

The note likely explores the types of skills that will be most valuable in an AI-driven economy, such as AI development, data analysis, and AI ethics. It may also consider the potential for AI to displace certain jobs and the imperative for workers to adapt and acquire new skills throughout their careers. This adaptability will be essential for navigating the changing landscape of the labor market.

Conclusion

The integration of generative AI into education is a complex and rapidly evolving phenomenon. Jean Xiao Timmerman’s FEDS Note provides valuable insights into this trend, highlighting the potential benefits and challenges of exposing students to AI technologies. As AI continues to transform the labor market, educational institutions must adapt and equip students with the skills they need to succeed in an AI-driven world.

The FEDS Note, published on February 26, 2025, serves as a crucial resource for educators, policymakers, and anyone interested in understanding the future of AI and education. The full note can be accessed through the Federal Reserve System’s publications, offering a comprehensive analysis of this transformative trend.

Two recent analyses shed light on the transformative impact of generative AI on college majors, each offering unique perspectives and methodologies. The first article presents a broad, comprehensive analysis using data from the National Survey of College Graduates (2013-2021) and Generative AI Occupational Exposure (AIOE) measures. The second article focuses more narrowly on the impact of generative AI on specific STEM fields, providing a comparative view of AI’s influence on higher education.

Comprehensive Analysis: NSCG and AIOE Data

The first article leverages data from the National Survey of College Graduates (NSCG) spanning 2013 to 2021, coupled with Generative AI Occupational Exposure (AIOE) measures, to provide a detailed examination of AI’s impact across various college majors, demographic groups, and types of institutions.this comprehensive approach allows for a nuanced understanding of the evolving landscape.

Key findings from this analysis include the identification of the most affected majors: Mathematics and Computer Science, Political Science and Government, and Accounting. The analysis acknowledges uncertainty regarding whether AI will lead to automation or augmentation within these fields. Furthermore,the study suggests that Hispanic and Asian graduates in certain majors might be more exposed to AI’s effects than white,Black,and female graduates,although the implications remain unclear. The analysis also predicts that Liberal Arts I and Research University I institutions will be most affected due to their focus on social sciences and STEM, respectively.

The methodology is clearly outlined, detailing the data sources (NSCG and AIOE) and providing descriptive statistics on the sample population, including gender, race, and salary. The method of linking AIOE measures to NSCG data is also thoroughly explained, enhancing the transparency and credibility of the findings.

focused Analysis: STEM Fields and AI Exposure

The second article narrows its focus to the impact of generative AI on specific STEM fields, while also acknowledging the impact on Political Science and Accounting.The primary emphasis is on the exposure of different STEM majors to AI’s language modeling and image generation capabilities, offering a targeted viewpoint on AI’s influence within these disciplines.

This analysis identifies Mathematics and Computer Science, and Engineering and technology (especially in image generation) as the most affected majors. The methodology employs z-scores for AIOE measures (language modeling and image generation) to assess impact. Though,it provides less detailed information on the data sources or sample population,presenting only summary statistics for a limited number of major categories. The data presented lacks the rich detail found in the first article,and the methodology is less descriptive.

Notably, this analysis lacks a demographic and institutional breakdown, limiting its ability to provide a comprehensive understanding of AI’s impact across different groups and educational settings.

Comparative summary

the first article provides a broader, more nuanced analysis with a clear methodology and more comprehensive data, offering a robust understanding of AI’s impact across various dimensions. The second article offers a more focused look at STEM fields,particularly highlighting the impact on Mathematics,Computer Science,and Engineering,but lacks the depth and breadth of the first article’s analysis. The first article’s findings are more robust due to its superior methodological approach and richer data set.