We present a report on the status of undergraduate Artificial Intelligence (AI) programs in the United States in Spring 2026. In so doing, we 1) describe our scraping and mapping tools, which dynamically update to track the state of AI education in the U.S., and 2) create a historic record at a time of great upheaval. The tool we developed, available at https://cicmap.ai, detects, scrapes, and displays data from more than 350 undergraduate AI programs--majors, minors, concentrations, and certificates--at 4-year universities. Our tool searched over 560 institutions to locate these programs, a sample that represents 86\% of all undergraduate Computer Science (CS) graduates in the U.S. This tool allows prospective students, guidance counselors, administrators, and faculty to easily access AI program requirements and is designed to continually update as new programs emerge. To the best of our knowledge, this survey represents the most comprehensive snapshot of the state of AI programs in the U.S. to date. With this work we offer three important contributions: 1) a record of AI programs in the U.S. at a time of great upheaval; 2) a tool to explore AI programs and their requirements; and
Most AI literacy courses for non-technical undergraduates emphasize conceptual breadth over technical depth. This paper describes UNIV 182, a prerequisite-free course at George Mason University that teaches undergraduates across majors to understand, use, evaluate, and build AI systems. The course is organized around five mechanisms: (1) a unifying conceptual pipeline (problem definition, data, model selection, evaluation, reflection) traversed repeatedly at increasing sophistication; (2) concurrent integration of ethical reasoning with the technical progression; (3) AI Studios, structured in-class work sessions with documentation protocols and real-time critique; (4) a cumulative assessment portfolio in which each assignment builds competencies required by the next, culminating in a co-authored field experiment on chatbot reasoning and a final project in which teams build AI-enabled artifacts and defend them before external evaluators; and (5) a custom AI agent providing structured reinforcement outside class. The paper situates this design within a comparative taxonomy of cross-major AI literacy courses and pedagogical traditions. Instructor-coded analysis of student artifacts at
The demand for computing education increases due to the rapid development of technology and its involvement in most daily activities. Academic institutes offer a variety of computing majors, such as Computer Engineering, Computer Science, Information Systems, Information Technology, Software Engineering, Cybersecurity, and Data Science. Since a major objective of earning a bachelor's degree is to improve career opportunities, it is crucial to understand how the job market perceives these computing majors. This study analyzed the relationships between various computing majors and the job market in Saudi Arabia, using LinkedIn public profile data, discovering insights into the strong relationship between the focus of certain computing majors and the employment of relevant job positions. Moreover, job category trends were analyzed over the past ten years, observing that demands for System Admin and Technical Support positions declined while demands for Business Analysis and Artificial Intelligence and Data Science inclined. This study also compared earned professional certifications between different computing major graduates that correspond to job position findings.
Improving undergraduate success in STEM requires identifying actionable factors that impact student outcomes, allowing institutions to prioritize key leverage points for change. We examined academic, demographic, and institutional factors that might be associated with graduation rates at two four-year colleges in the northeastern United States using a novel association algorithm called D-basis to rank attributes associated with graduation. Importantly, the data analyzed included tracking data from the National Student Clearinghouse on students who left their original institutions to determine outcomes following transfer. Key predictors of successful graduation include performance in introductory STEM courses, the choice of first mathematics class, and flexibility in major selection. High grades in introductory biology, general chemistry, and mathematics courses were strongly correlated with graduation. At the same time, students who switched majors - especially from STEM to non-STEM - had higher overall graduation rates. Additionally, Pell eligibility and demographic factors, though less predictive overall, revealed disparities in time to graduation and retention rates. The finding
With the surge in data-centric AI and its increasing capabilities, AI applications have become a part of our everyday lives. However, misunderstandings regarding their capabilities, limitations, and associated advantages and disadvantages are widespread. Consequently, in the university setting, there is a crucial need to educate not only computer science majors but also students from various disciplines about AI. In this experience report, we present an overview of an introductory course that we offered to students coming from different majors. Moreover, we discuss the assignments and quizzes of the course, which provided students with a firsthand experience of AI processes and insights into their learning patterns. Additionally, we provide a summary of the course evaluation, as well as students' performance. Finally, we present insights gained from teaching this course and elaborate on our future plans.
The importance of science beliefs such as self-efficacy, interest, identity, sense of belonging, perceived recognition and effectiveness of peer interaction in science education has been increasingly recognized in recent years. Here, we use five years of data from a validated survey administered to non-majors during their first year, physics majors throughout their undergraduate education, and first-year physics Ph.D. students at a large research university in the US. We find that physics majors in the first-year responded to the survey prompts more positively than their non-physics major peers who were in the same introductory courses, with the largest differences in perceived recognition, interest, and physics identity and somewhat smaller differences in self-efficacy, perception of peer interaction, and sense of belonging. Further, the average survey responses of physics majors for each belief remain largely constant over time from their first-year of the undergraduate curriculum through the last year and comparable to the Ph.D. students. This suggests that students are adjusting their interpretation of the survey items to match the current level of expertise expected of them. O
Efforts to promote equity and inclusion using evidence-based approaches are vital to correct long-standing societal inequities that have disadvantaged women and discouraged them from pursuing studies, e.g., in many STEM disciplines. We use 10 years of institutional data at a large public university to investigate trends in the majors that men and women declare, drop after declaring, and earn degrees in as well as the GPA of the students who drop or earn a degree. We find that the majors with the lowest number of students also have the highest rates of attrition. Moreover, we find alarming GPA trends, e.g., women who drop majors on average earn higher grades than men who drop those majors, and in some STEM majors, women who drop the majors were earning comparable grades to men who persist in those majors. These quantitative findings call for a better understanding of the reasons students drop a major and for making learning environments equitable and inclusive.
Efforts to promote equity and inclusion using evidence-based approaches are vital to correcting long-standing societal inequities that have disadvantaged women and discouraged them from pursuing studies, including in many STEM disciplines. We used 10 years of institutional data from a large public university to investigate the grade point average trends in different STEM disciplines for men and women who declared a major and then either completed the degree or dropped the major after declaring it. We found alarming trends, such as that women who dropped majors on average earned higher grades than men, and in some STEM majors, women who dropped the majors were earning comparable grades to men who persisted in those majors. While these quantitative findings call for a deeper understanding of the reasons women and men drop a major, we provide suggestions for approaches to make learning environments more equitable and inclusive so that traditionally excluded stereotyped groups can have a higher sense of belonging and thrive.
Implementation of cognitive apprenticeship in an introductory physics lab group problem solving exercise may be mitigated by epistemic views toward physics of non-physics science majors. Quantitative pre-post data of the Force Concept Inventory (FCI) and Colorado Learning Attitudes About Science Survey (CLASS) of 39 students of a first-semester algebra-based introductory physics course, while describing typical results for a traditional-format course overall (g = +0.14), suggest differences in epistemic views between health science majors and life science majors which may correlate with differences in pre-post conceptual understanding. Audiovisual data of student lab groups working on a context-rich problem and students' written reflections described each group's typical dynamics and invoked epistemic games. We examined the effects of framework-based orientation (favored by biology majors) and performance-based orientation (favored by computer science, chemistry, and health science majors) on pre-post attitude survey performance. We also investigated possible correlations of these orientations with individual quantitative survey results, and with qualitative audiovisual data of lab
Funded by a $3M Department of Defense (DoD) National Defense Education Program (NDEP) award, we are developing and deploying on a national scale a follow-up curriculum to "Our Place In Space!", or OPIS!, in which approx. 3,500 survey-level astronomy students are using our global network of "Skynet" robotic telescopes each year. The goal of this new curriculum, called "Astrophotography of the Multi-Wavelength Universe!", or MWU!, is to boost the number of these students who choose STEM majors. One semester in, our participant program has begun, and participating educators have made good progress on MWU!'s first two modules. Excellent progress has been made on the software front, where we have developed new graphing, analysis, and modeling tools in support of these, and upcoming, modules. On the hardware front, preparation continues to expand Skynet to include a global network of intermediate-sized, radio telescopes, capable of exploring the invisible universe.
In a previous study, students' self-expressed learning orientations towards an exercise centered on self-monitoring one's ability to solve a pre-lab physics problem were identified from a post-test feedback survey given to an introductory algebra-based physics student population spanning six measured semesters, and examined as a potential variable in course performance, force and motion conceptual understanding, and attitudes towards learning physics. The sampled population, which primarily consists of life science majors, was also asked in the same feedback survey to discuss what portion or portions of the course were relevant to their respective choices of major. In this study, we examine the fact that about 50 students out of 218 sampled students, or 23% of the sample population, explicitly stated that they perceived no relevance at all of the course to their respective majors, whereas the other 168 students cited portions of the course or the entirety of the course as being relevant to their majors. A follow-up investigation of perceived relevance versus irrelevance shows that attitudes towards physics will experience more expert-like shifts for students who perceive relevance
Analysis of institutional data for physics majors showing predictive relationships between required mathematics and physics courses in various years is important for contemplating how the courses build on each other and whether there is need to make changes to the curriculum for the majors to strengthen these relationships. We use 15 years of institutional data at a large research university to investigate how introductory physics and mathematics courses predict male and female physics majors' performance on required advanced physics and mathematics courses. We used Structure Equation Modeling (SEM) to investigate these predictive relationships and find that among introductory and advanced physics and mathematics courses, there are gender differences in performance in favor of male students only in the introductory physics courses after controlling for high school GPA. We found that a measurement invariance fully holds in a multi-group SEM by gender, so it was possible to carry out analysis with gender mediated by introductory physics and high school GPA. Moreover, we find that these introductory physics courses that have gender differences do not predict performance in advanced ph
In a previous study, students' self-expressed learning orientations towards an exercise centered on self-monitoring one's ability to solve a pre-lab physics problem were identified from a post-test feedback survey given to an introductory algebra-based physics student population spanning six measured semesters, and examined as a potential variable in course performance, force and motion conceptual understanding, and attitudes towards learning physics. The sampled population, which primarily consists of life science majors, was also asked in the same feedback survey to discuss what portion or portions of the course were relevant to their respective choices of major. In this study, we examine the fact that about 50 students out of 218 sampled students, or 23% of the sample population, explicitly stated that they perceived no relevance at all of the course to their respective majors, whereas the other 168 students cited portions of the course or the entirety of the course as being relevant to their majors. A follow-up investigation of perceived relevance versus irrelevance shows that attitudes towards physics will experience more expert-like shifts for students who perceive relevance
This research uses 10 years of institutional data at a large public university in the USA to investigate trends in the undergraduate majors students declare, drop, and earn degrees, especially comparing physics to other disciplines. We find that physics has the lowest number of students of all science, technology, engineering, and math (STEM) disciplines and it also has the highest rates of attrition of students who declare a major. While many STEM disciplines have students migrating both in and out of those majors, physics primarily has a uni-directional migration of students out of the major. Furthermore, physics has the lowest percentage of women undergraduate majors. Using an equity framework, we view these findings as signatures of inequitable and non-inclusive culture. We suggest that important roles may be played by stereotypes such as the incorrect belief that physics is accessible only to brilliant men, the issue of first-year college physics courses failing to energize students, and apathy in large physics departments toward improving intentional recruitment and retention of physics majors.
This paper studies strict majority reasoning in finite electorates using so-called $\textit{social decision frames}$: finite sets of voters equipped with distinguished families of coalitions interpreted as those voting blocs evaluated to form a strict majority. A coherence criterion for qualitative majority judgments is identified and shown to give an exact characterization for representability of strict majorities by finitely additive measures. In addition, a minimal natural logic for reasoning about strict majorities is shown to be sound and complete. These developments motivate examination of associated combinatorial questions concerning incoherence in finite families of sets; partial results and a conjecture are given. Finally, the results of this paper are applied to correct a classical representation theorem for weak qualitative probability structures due to Patrick Suppes and to establish a May-type characterization for ordinary strict majority rule for social decision frames.
Many real matching markets encounter distributional and fairness constraints. Motivated by the Chinese Major Transition Program (CMT), this paper studies the design of exchange mechanisms within a fresh framework of both distributional and dual priority-respecting constraints. Specifically, each student has an initial assigned major and applies to transfer to a more desirable one. A student can successfully transfer majors only if they obtain eligibility from both their initial major and the applied major. Each major has a dual priority: a strict priority over current students who wish to transfer out and a strict priority over students from other majors who wish to transfer in. Additionally, each major faces a ceiling constraint and a floor constraint to regulate student distribution. We show that the existing mechanisms of CMT result in avoidable inefficiencies, and propose two mechanisms that can match students to majors in an efficient way as well as respecting each major's distributional and dual priority. The efficient mechanisms are based on a proposed solution concept: eligibility maximization (EM), and two processes for identifying improvement cycles--specifically, transfe
We establish two structural majorization relations, which we call precursors, underlying the properties of supermodularity and subadditivity on the lattice induced by majorization. These are precursors in that they immediately imply that all sums of concave functions, which we dub sum-concave functions, are supermodular and subadditive on the majorization lattice. Using these majorization relations, we then show the supermodularity and subadditivity (in the lattice-theoretic sense) of Tsallis entropies (for all $α$) and Rényi entropies (for all $α> 1$), also recovering these properties for the Shannon entropy in the process. We further strengthen these inequalities, showing that: (i) all these entropic functionals are strictly subadditive on the majorization lattice; (ii) Tsallis entropies (and therefore the Shannon entropy as well) are strictly supermodular on the majorization lattice.
In this study, we investigate student performance using grades and grade anomalies across periods before, during, and after COVID-19 remote instruction in courses for bioscience and health-related majors. Additionally, we explore gender equity in these courses using these measures. We define grade anomaly as the difference between a student's grade in a course of interest and their overall grade point average (GPA) across all other courses taken up to that point. If a student's grade in a course is lower than their GPA in all other courses, we refer to this as a grade penalty. Students received grade penalties in all courses studied, consisting of twelve courses taken by the majority of bioscience and health-related majors. Overall, we found that both grades and grade penalties improved during remote instruction but deteriorated after remote instruction. Additionally, we find more pronounced gender differences in grade anomalies than in grades. We hypothesize that women's decisions to pursue STEM careers may be more influenced by the grade penalties they receive in required science courses than men's, as women tend to experience larger penalties across all periods studied. Furtherm
Universities often present the Common Basic Cycle (CBC) as a neutral levelling stage shared by several degree programmes. Using twenty years of longitudinal administrative records from a Faculty of Engineering and Exact Sciences, this study tests whether the CBC actually operates as a uniform gateway or as a differential filter across majors. We reconstruct student trajectories for 24,017 entrants, identifying CBC subjects (year level <= 1), destination major, time to exit from the CBC, and final outcome (progression to upper cycle, drop-out, or right-censoring). The analysis combines transition matrices, Kaplan-Meier survival curves, stratified Cox models and subject-level logistic models of drop-out after failure, extended with multi-major enrolment data and a pre/post 2006 curriculum reform comparison. Results show that the CBC functions as a strongly differential filter. Post-reform, the probability of progressing to the upper cycle in the same major ranges from about 0.20 to 0.70 across programmes, while overall drop-out in the CBC exceeds 60%. Early Mathematics modules (introductory calculus and algebra) emerge as structural bottlenecks, combining low pass rates with a two
Majorization theory is a powerful mathematical tool to compare the disorder in distributions, with wide-ranging applications in many fields including mathematics, physics, information theory, and economics. While majorization theory typically focuses on probability distributions, quasiprobability distributions provide a pivotal framework for advancing our understanding of quantum mechanics, quantum information, and signal processing. Here, we introduce a notion of majorization for continuous quasiprobability distributions over infinite measure spaces. Generalizing a seminal theorem by Hardy, Littlewood, and Pólya, we prove the equivalence of four definitions for both majorization and relative majorization in this setting. We give several applications of our results in the context of quantum resource theories, obtaining new families of resource monotones and no-goes for quantum state conversions. A prominent example we explore is the Wigner function in quantum optics. More generally, our results provide an extensive majorization framework for assessing the disorder of integrable functions over infinite measure spaces.