Existing Literature on GenAI Surveys

Existing surveys of student use and perceptions of GenAI in higher education reveal some common themes: the potential GenAI tools have for personalized learning, writing assistance, and research support, as well as concerns about accuracy, privacy, and ethical issues (Chan & Hu, 2023). However, while these themes remain prominent, surveys of student use and perceptions also vary considerably in the levels of adoption, concern, and perceived benefit. Moreover, institutional type and student demographics are differentiating factors, impacting perception and usage trends among surveyed populations. For instance, Parker et al. (2023) reported that 65% of undergraduate students at the University of Kansas had used ChatGPT or similar GenAI tools for academic tasks, with 37% employing it both for studying and completing assignments. Ethical concerns are prevalent as well, with students expressing mixed views on the appropriateness of using GenAI in academic contexts (Parker et al., 2023). On the contrary, the University of Maryland’s 2024 survey shows that only 41% of students reported experimenting with GenAI for coursework and few used it routinely; concerns about academic integrity were prominent, though students were more confident than faculty in their ability to ethically navigate GenAI use (Masters et al., 2024).

A multi-site survey of student perceptions and GenAI use, conducted by researchers across six campuses in California and Hawaii, highlighted common themes as well as significant differences when disaggregating data. For example, while 45% of students surveyed reported using GenAI in their studies, this figure looks significantly different at the institutions with higher reported GenAI usage rates than at those with lower rates: at San Diego State University, 48% of students reported using GenAI in their studies, but at California State University (Chico), only 36% of students reported using GenAI in their studies (Frazee et al., 2024). This difference between university contexts was even greater when broken down by the adoption of specific GenAI tools. Of all students surveyed, 72% reported having used ChatGPT (Frazee et al., 2024). However, ChatGPT usage stood at 82% at San Diego State and only 44% at Chico State. Similarly, the GenAI tool Grammarly had a reported overall adoption of 64%, with the lowest usage rates reported at the University of Hawaii (23%), and the highest usage rates reported at California State University, Fullerton (75%) (Frazee et al., 2024). The diversity of campus climates was prominent in student desires for more exposure to GenAI in the curriculum, too. While 49% of all students surveyed believed their curriculum lacked adequate exposure to GenAI, this figure stood at 58% at San Francisco State and 35% at the University of Hawaii (Frazee et al., 2024).

In addition to institutional differences, the date of the survey’s issuance is another impactful variable. Since the release of ChatGPT in November 2022, the adoption of GenAI tools has rapidly grown in educational contexts as more students learn about and become familiar with them (Pedersen, 2024). Along these lines, perspectives and emotions concerning GenAI technologies have shifted as more and broader impacts and use cases of GenAI have come to light. A BestColleges (2023, March) survey of U.S. undergraduate and graduate students revealed that 22% of students admitted to using GenAI to help them complete assignments or exams (Welding, 2023). In October of that same year—merely seven months later—that number jumped to 56% (Nam, 2023). In the second iteration of the BestColleges survey, 53% of students reported having been assigned coursework which required them to use GenAI as a part of an assignment (Nam, 2023). Similarly, in a survey distributed at San Diego State in 2023 and 2024, the number of students who claimed to regularly use AI-powered tools in their studies (with Strongly disagree assigned a value of 1 and Strongly agree assigned a value of 6) grew from a 2.84 average in 2023 to a 3.23 average in 2024 (Goldberg et al., 2024). The average of San Diego State students who responded that their professors encouraged the use of GenAI in coursework also jumped from 2.23 to 2.57 (Goldberg et al., 2024). Finally, in the first iteration of the BestColleges survey, only 39% of respondents said their instructors had discussed the use of GenAI (Welding, 2023), but by the second iteration, 79% of respondents reported at least one instructor who had discussed the use and ethics of GenAI in the classroom (Nam, 2023).

The presence of policies concerning GenAI use also changed dramatically between the two surveys. In the initial survey, 60% of students stated that neither their instructors, syllabi, course materials, nor school honor code specified how to use GenAI tools responsibly (Welding, 2023). By the second survey, this number had dropped to 10% (Nam, 2023). Despite policy changes, the ambiguity concerning perspectives of GenAI tools and cheating did not materially change between the two surveys: 51% of students in the initial survey believed that using GenAI tools for assignments or exams constituted cheating, while 54% of students shared this belief in the second survey (Welding, 2023; Nam, 2023).

The BestColleges survey also highlights an apparent discrepancy between instructor policies and student expectations. While 31% reported explicit prohibitions on GenAI in their coursework, 61% of students anticipated that GenAI tools would become normalized in education (Welding, 2023). Frazee et al. (2023) highlighted a similar disconnect between students’ perceptions of GenAI’s benefits—56% reported positive impacts—and the limited encouragement from faculty, with only 24% of students indicating that their professors promoted its use. This tension reflects broader uncertainties about the role of GenAI in fostering innovation while supporting academic integrity.

International surveys of student GenAI use patterns and perspectives also reveal a diverse range of experiences and responses. A Swedish study by Stöhr et al. (2024) found that over a third of students regularly used ChatGPT in educational settings, with 47.7% agreeing that GenAI made them more effective learners. Notably, engineering students exhibited the highest engagement, while humanities and medicine students were less enthusiastic. Ethical concerns remained prevalent, with 61.9% viewing the use of chatbots for assignments as cheating (Stöhr et al., 2024). This aligns with U.S. findings, underscoring global apprehension about ethical boundaries.

In Hong Kong, Chan and Hu (2023) revealed a generally positive attitude toward integrating GenAI into education, with students valuing its ability to provide personalized feedback. However, concerns about reliability, transparency, and privacy persisted. Similarly, Zhou et al. (2024) in the United Kingdom highlighted the dual-edged nature of GenAI, as students appreciated its potential for productivity and personalized learning but worried about misuse and over-reliance. Tierney et al. (2025) likewise found that U.K. students desired clearer guidance regarding appropriate use of GenAI in their studies while acknowledging that GenAI use and guidance will vary across academic disciplines.

Almassaad et al. (2024) found that 78.7% of Saudi Arabian students frequently used GenAI tools, a markedly higher rate than those reported in most Western studies at that time. The EY and TeachAI report found that trust in GenAI varies by region, with respondents in the Middle East, Africa, and India having the highest trust but respondents in North America exhibiting the lowest (Merriman & Sáiz, 2024). However, barriers such as limited awareness and ethical concerns are significant for non-users, even in regions where trust and adoption are high (Almassaad et al., 2024). Moreover, the “100 Student Voices on AI and Education” report by the World Bank documented that there are many regions where GenAI adoption remains low due to high internet costs and insufficient or inconsistent connectivity (Cobo, 2024).

While commonalities exist across survey data, GenAI adoption varies significantly by demographics. Studies consistently show that campus-based students adopt GenAI at higher rates than online students, with male students and those in STEM fields expressing more positive attitudes than female students and humanities majors (Dello Stritto et al., 2024; Stöhr et al., 2024; Nam, 2023). Age patterns remain inconsistent—while some research identifies Gen Z as the most optimistic adopters (Chan & Lee, 2023), others found millennials using GenAI for coursework at higher rates (Nam, 2023).

While there are fewer published surveys on the usage patterns of educators and their perspectives on GenAI in higher education, studies which have been published demonstrate a similar mix of sentiment among educators. Educators express heightened concerns about overreliance and pedagogical implications, with academic discipline as a notable variable (Ghimire et al., 2024; Chan & Lee, 2023). Ghimire et al. (2024) found that in surveys and interviews educators were generally positive about GenAI for education; however, computer science educators showed more positivity and more confidence in their technical understanding of the tools. Among medical educators, Cervantes et al. (2024) found a generally positive attitude toward GenAI. The greatest perceived strengths of the tool among this group were the ability to conduct research efficiently, automate tasks, and increase content accessibility; the greatest concerns related to cheating, false information, lack of context, and the removal of human interaction (Cervantes et al., 2024). As with students, location also appears to be a significant variable among faculty. In a study of both U.S. and Australian university faculty, Smolansky et al. (2023) found significant differences in the usage patterns of surveyed faculty populations. Compared to their students, who used ChatGPT for coursework at least weekly at rates of 29% (Australian university) and 24% (American university), faculty reported weekly use of ChatGPT for professional purposes at 35% (Australian population) and 10% (American population) (Smolansky et al., 2024). Like students, educators emphasize the need for proper guidelines and policies to ensure responsible use of GenAI in education (Zastudil et al., 2023; Chan & Lee, 2023).

Despite this growing wealth of data, several gaps remain in the literature. Few studies address the long-term implications of GenAI on critical thinking and academic skill development. Additionally, there is limited research on how institutional policies and faculty attitudes shape student engagement with GenAI. Cross-cultural comparisons are often hindered by methodological inconsistencies, highlighting the need for standardized survey instruments. The literature reveals a complex interplay of enthusiasm and apprehension among students regarding GenAI in higher education. While many recognize its potential to enhance learning and productivity, ethical concerns and inconsistent institutional support remain significant barriers. In addition to these gaps, although there are significant data on the use and perspectives of GenAI use, there is little evidence on how these results have turned into needs-based professional learning and support. While many institutions, or units within those institutions, have provided resources and direction to faculty and students in the form of websites, workshops, courses, trainings, policy statements, etc., these materials may be more based on national trends than their unique local contexts. Taken together, the findings highlight the need for research that maps current student and faculty practices and perceived support/training needs in generative AI, using one institution’s experiences to show how such evidence can guide CTL programming.

Participants

A total of 785 individuals responded to the surveys—504 student respondents and 281 faculty. This response rate is out of a total undergraduate population of 20,000 at the online campus and 11,300 at the residential campuses, which includes dynamic demographics: 60% veterans (online), a mixture of traditional and nontraditional students (online: average age 32), and 35% international students (residential). A total of 37.6% of faculty and 5% of students responded from the two residential campuses while 15% of faculty and 2.6% of all students responded from the online campus.

More information on faculty respondent demographics is provided in Table 1 below.

More information on student respondent demographics is provided in Table 2.

Data

Each survey included open and closed questions and was divided into three sections: Experience with GenAI tools, Areas for Additional Support, and Demographic Information. The Institutional Review Board approved the study and the survey’s electronic distribution via university email. Surveys were distributed through the Institutional Research department in the Spring of 2024 (IRB #24-095). To complete the survey, participants first provided informed consent.

Analysis

All quantitative and qualitative data were explored as a single cohort for each group (i.e., all faculty and all students), and then participants from online and residential campuses were analyzed as separate cohorts. This established six distinct cohorts (all faculty; all students; online-only faculty; online-only students; residential-only faculty; and residential-only students), with each representing a single case for analysis. Researchers leveraged Qualtrics analytics for descriptive statistics and Excel for frequency distributions. Further disaggregation of data was completed between online and residential populations. Visualizations were created to review these data in different ways. Qualitative data were imported into Dedoose, a piece of qualitative data analysis software. These data went through multiple rounds of coding by all the researchers. The first round was descriptive, with saturation being achieved. The four researchers calibrated their findings through consensus discussions to refine a codebook. After the codebook had been finalized, researchers applied codes to each campus cohort twice, with consensus discussions between. Dedoose data visualizations, like concept matrices, assisted the researchers in identifying unique patterns within each case and across cases. Multiple themes emerged from these data. Memos were utilized for both clarification and interpretation during the process.

Student GenAI Data

Collecting survey data from students across all three campuses (n=504) allowed for a more data-driven characterization of student GenAI use and needs. Overall, 85% of students acknowledged some use of GenAI, with Table 3 offering additional information on students’ use and self-reported understanding.

At the time of survey distribution in the Spring of 2024 (SP24), students were most likely to learn about GenAI through non-academic avenues, like being self-taught (318, 63%), learning from friends and peers (268, 53%), and learning through social media (223, 44%), over academic and professional sources, such as professors (144, 29%) or professional development (76, 15%). A total of 72% of students reported wanting to learn more about GenAI in their field or discipline, and 60% desired clarity around course expectations and policies concerning GenAI use; additional information on their areas of future interest is displayed below in Table 4.

Across student respondents, the data show a population widely using GenAI but often learning about it through sources that do not prioritize critical thinking, academic or learning values, or address discipline- or field-specific current or future appropriate use.

Faculty GenAI Data

Survey data showed faculty respondents (n=281) were also using GenAI at similar rates to students (85%, 240). Table 5 shows faculty use and self-reported understanding of GenAI. However, while students were asking for additional clarity on GenAI course use, faculty responses (n=281) showed that only 18% (52) had course-specific GenAI policies (Table 6), yet 53% (151) affirmed that GenAI had impacted their course design or teaching.

Some faculty were already integrating GenAI into courses, with Table 7 showing how faculty reported offering more information on GenAI integration activities and assignments.

When faculty were given a chance to explain why they had not yet integrated GenAI into their courses, 24% (67) reported lacking knowledge, 20% (57) did not see how GenAI was relevant to their discipline, 19% (53) reported insufficient resources, and 16% (45) were concerned about privacy. Across faculty responses, these data show a population that is starting to use GenAI but is lagging behind in terms of aligning course policies and teaching practices with GenAI integration.

Qualitative Data

In addition to the quantitative data, qualitative data were collected through open-ended questions. The responses were inductively coded to create nine parent code categories and 13 child codes; not all coded statements fall within a child code. Total code frequency is displayed in Table 8.

The most frequent codes included additional training/information wanted (255), general benefits of GenAI (182), risks and limitations of GenAI (158), and avoiding AI (138). Overwhelmingly, students were more likely to request additional training than faculty, with students focusing on three major areas (sample responses below). First, how GenAI could impact their professional future:

• “I don’t know how it may impact my professional future or higher education in general. I am a non-traditional student (adult learner) and I am intimidated by AI because of my lack of understanding it. It feels like AI is one more very large, complicated thing I need to learn about and I feel overwhelmed.”

• “I want to keep myself up-to-date [sic] about developments in my field of study and how future employers might adopt it/ban it.”

Second, how GenAI can and should be used within their courses:

• “I want my professors to have an understanding of how AI functions, and how it fits into the world of education. I would like them to be able to help us understand when it is or is not appropriate to use AI.”

• “Professors need to be more specific when promoting or discouraging use of AI by giving reasons to use it or not as well as acceptable uses or times to use AI.”

Third, information on what it may mean to use GenAI productively:

• “I would like more instruction that embraces AI and shows how it can be used in a positive way. I think this technology will be widely used in the future and so we should understand it.”

• “I would like to learn more about all of the different types of AI technology and the different ways that we can use [it].”

• “I know how to use AI but sometimes I don’t feel safe since sometimes it could be wrong and I want to learn how to recognize when it happens.”

Faculty respondents, although less likely to show an interest in additional information, wanted to learn more about GenAI limitations, AI detection, and how GenAI could alleviate faculty workload and labor.

Students and faculty both mentioned GenAI tools’ potential benefits, noting that these tools could assist in different aspects of student and faculty life, like productivity (i.e., “It’s a power tool that would make students more productive, by offloading mundane tasks to the machines, leaving the real creative tasks to the students”), writing (i.e., “AI should be allowed as a tool to help organize papers for grammar and paragraph structure analysis. Also, when a student uses the prompt to generate an outline to help write a paper, I think this is acceptable as well. I think it becomes unethical when students are using AI to write their entire paper with little effort by the student”), coding (i.e., “very helpful for coding and debugging”), and learning support (i.e., “they could be a huge support tool for students impacted with learning disabilities. AI-Chatbots can help re-state a question, ask more generalized questions, and grade a student based on their work and understanding throughout a semester”). However, as one student-respondent noted, one cannot just take GenAI on benefits alone: “AI Literacy is a necessary course, both teaching students to properly prompt AI, to understand the limitations and biases of AI, understand when it may be appropriate, understand where the data AI is trained on actually comes from and the ethical and moral implications of it, and understand that it can and will hallucinate facts no matter how advanced it may seem.”

Faculty were more likely to highlight the risks of GenAI, such as concerns about hallucinations (i.e., “unchecked integration could easily lead to AI generated courses and materials that have hallucinated content, that a student directs their AI assistant to assimilate and regurgitate, thereby bypassing any actual work or understanding”), academic integrity (i.e., “I would like to have students use AI in a responsible way but not simply copy and paste without any thought. Given there is no way to say whether a student has used AI or not, many students are using it to cheat and not do the work”), and how these tools can be used to evade the learning process (i.e., “I think AI usage probably starts out as a tool to support students who are stuck or struggle with developing ideas into words but can quickly turn into the ‘easy button’ for homework or other study”).

Just as faculty were suspicious of students, students also wondered if faculty were using GenAI to review and respond to student work:

• “We see faculty members using AI to summarize student submissions”

• “Professors using it to grade assignments and respond to students”

• “To professors, if you are blatantly using AI in your classes please don’t expect the students to not use it in their assignments. Monkey see-monkey do. Practice what you preach”

Students additionally voiced concern around misinformation and incorrect output, including hallucinations, deepfakes, and nonexistent research sources.

Finally, students and faculty both stated reasons to avoid GenAI, with students being more likely to express deeply negative associations. Students noted that GenAI would “stifle the creativity of students,” “encourages students to put less work and effort forward,” “poses significant negative impacts to human capabilities for self-reliance,” would “steal art and artists’ jobs,” and “cheapens [sic] the degree’s value for everybody.” Several students recommended banning GenAI entirely. Some faculty did mention avoiding GenAI for issues with trustworthiness or efficacy, but more attributed an avoidance of GenAI to the importance of in-class or interactive learning (i.e., “I have not talked about this, because 80% of the course grade is based on in-class activity”), lacking disciplinary proficiency (i.e., “I mentioned to the students that AI is still coming along in physics understanding, so do not trust it”), or lack of perceived AI interest by students (i.e., “AI has not come up in my classes”). For contingent and online faculty, in particular, there was a recurring question regarding whether they could adapt standard curricula (i.e., “WW courses are templated, and since I am not the course designer, I don’t have the ability to make those changes. However, there needs to be a major overhaul to the design of this course”). In the section that follows, the research team explains how these data led to key findings and key responses in CTL programming.

Key Theme: AI Literacy Needs for Faculty and Students

As noted above, while both students and faculty were using GenAI, only 33% (96) of faculty reported using GenAI to achieve education goals, 29% (83) reported ongoing discussions about GenAI with students, and only small segments identified activities that trained students on GenAI (7%, 22), modeled or practiced using GenAI tools with students (13%, 37), prompted students to think critically about GenAI tools (17%, 48), or used GenAI to achieve course learning outcomes (14%, 40). Qualitative data, likewise, showed faculty frustration with how to leverage GenAI to support learning and literacy when they felt students were most likely to use GenAI to outsource learning. From these data, we concluded that additional opportunities were needed for faculty to develop GenAI literacy and receive guidance on how GenAI could be intentionally brought into their courses to support learning goals. This finding largely aligns with national research on GenAI in higher education, particularly calls for meaningful development that helps instructors negotiate skill-building and AI integration alongside an awareness of the limitations and risks of GenAI tools (Bannister & Carver, 2024; Mathew & Stefaniak, 2024).

All three campuses developed new programming based on our localized data, which differed significantly from national trends. Survey results revealed that faculty and students prioritized practical teaching applications over generic AI literacy. In response, both the online and the residential campuses created asynchronous trainings focused on discipline-specific implementations, ethical practices, and concrete teaching strategies—bypassing the typical introductory content about how AI works. The online campus refined its existing required primer while the residential campuses developed parallel courses, both directly addressing faculty’s expressed needs for practical, teaching-focused guidance rather than theoretical AI concepts.

Each campus was able to tailor professional development in ways that would appeal to their faculty norms while meeting campus needs and guidelines. For instance, one residential campus received feedback that faculty desired more discipline-specific examples and opportunities for conversation, so they developed an “AI in Action” workshop series that spotlighted faculty success stories in integrating GenAI. University-wide, the three CTLs collaborated on a virtual synchronous “First Friday AI” workshop series that provided a conversational space for faculty across all three campuses to gather and discuss different elements of GenAI. University-wide, the CTLs have additionally clarified that existing grants supporting course design, Scholarship of Teaching & Learning (SoTL) projects, and small changes to pedagogy can be used to support GenAI integration. Finally, the researchers have been invited to join colleges and departments to host more discipline-specific GenAI workshops related to GenAI ethics, GenAI learning applications, and GenAI as a tool for course design and development.

While CTLs are typically faculty-facing and less involved in student support offices and initiatives, they can be good partners in developing student literacy support programming. Responding to the student needs identified through our survey, the Generative AI Solution Specialist partnered with the library and faculty leadership to develop an asynchronous student-facing mini-course, “Generative AI Foundations”, which foregrounds institution-specific guidance and complements it with core AI literacy content (GenAI basics, hallucinations, tools, and prompt engineering). In parallel, the Generative AI Solution Specialist co-developed for-credit AI courses (e.g., “HUMN (Humanities) 250: Generative AI in Real-World Contexts”) with input from the online College of Arts and Sciences leadership. Residential CTLs have been invited to present to student populations on GenAI-related topics, particularly to graduate student populations, undergraduate research experience participants, and student organizations. In these roles, CTL materials are being retrofitted to convey the same information faculty are receiving with small adjustments for a student audience.

Key Theme: Guidance on GenAI Usage in Teaching/Learning Environments

Given the general lack of consensus in the broader higher education community on the use or misuse of GenAI, our university was hesitant to develop a formal GenAI policy, which is similar to the responses of other universities at that time. However, 60% of student respondents wanted clearer course expectations around GenAI use, with 58% wanting to know how GenAI use relates to other university policies, like the honor code and plagiarism. While 53% of faculty stated that they believed GenAI impacted their course, only 18% had a course-specific GenAI policy in Spring 2024.

In response to the relatively low number of faculty who reported a GenAI policy for their course, our CTLs designed interventions to increase support around policy creation. At one residential campus, policy design was covered in the optional asynchronous course in a module that asked faculty to review university guidelines, define their AI values in education, explore model policies, draft their own policy, and revise it based on individualized feedback from a CTL Associate Director.

The online campus, however, developed a mandatory training on GenAI policy creation for all online faculty in response to data demonstrating that the online campus faculty were the least likely to have a GenAI policy (9% reported providing a policy, in comparison to 28% of residential faculty). Qualitative data also demonstrated that online campus faculty were the least comfortable writing a policy. Because one of the most urgent needs requested by online students (63%) was clearer expectations regarding appropriate GenAI use in classes, the faculty GenAI policy training was designed with specificity in mind, encouraging faculty to provide precise approved and prohibited uses of GenAI for each type of major assignment in the course. The training also provided examples of designating what this could look like in practice. Alongside this required policy training, the online campus created a longer, optional, self-paced training for students and faculty, breaking down specific examples of ethical and unethical uses of GenAI for learning. This longer training, developed in consultation with academic leadership at the online campus, provided students and faculty with the groundwork to understand the university’s stance on appropriate use of GenAI for learning. Faculty were encouraged to build their specific course policies on this foundation. Many academic departments in the online campus have subsequently made this more in-depth training mandatory for their faculty. Short-term results of this data-driven intervention demonstrate that over 60% of online campus instructors now provide a GenAI policy (according to a search of courses in the online campus Learning Management System (LMS) approximately two months after the training).

In light of these advancements, the CTL offices are looking to expand this training to make it available to faculty on the residential campuses. In this case, ERAU is lucky to have three separate campuses that can innovate in various ways, learn from each other, and adopt practices that could benefit the entire university community and not just one campus.

Key Theme: Need for Clarity on Uses of AI outside the University

The survey results reveal a clear and significant interest among students in understanding how GenAI is used beyond their classrooms in professional and real-world contexts. Over 72% of student respondents expressed a desire to learn more about GenAI applications within their specific fields or disciplines, while qualitative responses further emphasized a gap in understanding the role of GenAI in post-graduate settings. One student encapsulated this sentiment, stating, “I want to keep myself up-to-date [sic] about developments in my field of study and how future employers might adopt it or ban it.” Another highlighted, “I would like to learn more about all of the different types of AI technology and the ways we can use it.” In addition, students noted a desire to learn more about how GenAI could contribute to “innovation and safety when in industry settings” and requested “examples of how AI is positively used in fields and industries.” This interest aligns with broader trends in GenAI adoption across industries, where GenAI tools and platforms are being leveraged for innovation and creativity as well as tasks ranging from writing support to automated workflows. Only 39% of faculty reported that they understood how GenAI was used in their field or discipline, highlighting a discrepancy between students’ need for information concerning professional uses of GenAI and faculty’s ability to provide the desired information.

These findings suggest a need for higher education institutions to expand beyond classroom-focused GenAI literacy initiatives to incorporate career-oriented training and interdisciplinary workshops. Some progress has been made in this area; for example, one residential CTL has collaborated with academic departments to host workshops on “AI in Professional Contexts.” For the online campus, the Generative AI Solution Specialist developed programming that evolved into sustainable academic offerings, with the creation of both non-credit learning opportunities and a formal minor program on the online campus that positions students to navigate AI’s role in their future careers. Further, the online campus surveyed and interviewed their industry advisory board concerning new skills and competencies their industries would require in response to the growing impact of GenAI, ensuring these academic offerings aligned with evolving work. The results of the interview and survey were shared with college departments in the interest of building competencies in these areas in future curricula. Preparing students for GenAI integration across professional fields requires broader institutional investment beyond the CTLs’ current scope. The survey data provide stakeholders with concrete evidence to support this expanded investment.