Examining faculty and student perceptions of generative AI in university courses
Open accessAbstract
As generative artificial intelligence (GenAI) tools such as ChatGPT become more capable and accessible, their use in educational settings is likely to grow. However, the academic community lacks a comprehensive understanding of the perceptions and attitudes of students and instructors toward these new tools. In the Fall 2023 semester, we surveyed 982 students and 76 faculty at a large public university in the United States, focusing on topics such as perceived ease of use, ethical concerns, the impact of GenAI on learning, and differences in responses by role, gender, and discipline. We found that students and faculty did not differ significantly in their attitudes toward GenAI in higher education, except regarding ease of use, hedonic motivation, habit, and interest in exploring new technologies. Students and instructors also used GenAI for coursework or teaching at similar rates, although regular use of these tools was still low across both groups. Among students, we found significant differences in attitudes between males in STEM majors and females in non-STEM majors. These findings underscore the importance of considering demographic and disciplinary diversity when developing policies and practices for integrating GenAI in educational contexts, as GenAI may influence learning outcomes differently across various groups of students. This study contributes to the broader understanding of how GenAI can be leveraged in higher education while highlighting potential areas of inequality that need to be addressed as these tools become more widely used.
AI snapshot
Written from the paper’s full text.
TL;DR
A Fall 2023 survey of 982 students and 76 faculty at a large public southeastern U.S. university found few student–faculty differences in generative AI attitudes, except ease of use, hedonic motivation, habit, and personal innovativeness. Student attitudes and use varied by gender and STEM/non-STEM major, with STEM males generally most positive and non-STEM females least positive.
Key findings
- Students and faculty differed on some UTAUT factors: students rated generative AI higher on Effort Expectancy (p < 0.01), Hedonic Motivation, and Personal Innovativeness (p < 0.01), while faculty rated Habit higher (p < 0.05); Behavioral Intention did not differ significantly. Use rates were similar (W = 33,630; p = 0.542), with about 70% of both groups using generative AI less than once per week and only 6% of students and 9% of faculty using it daily.
- For learning impacts, students were significantly more positive than faculty only on Academic Performance (W = 39,673; p < 0.001); both groups tended to agree generative AI would have more negative effects on Problem Solving, Teamwork, Self-efficacy, Test Anxiety, Intrinsic Motivation, and Engagement. Among student subgroups, STEM males were more positive than STEM females on Academic Performance (p < 0.05), and STEM males were more positive than non-STEM females on Overall Learning (p < 0.01).
- On ethics and policy, students and faculty largely agreed: they agreed generative AI can exacerbate academic dishonesty/cheating (W = 33,283; p = 0.140) but should still be used in courses (W = 34,005; p = 0.071), and approximately 80% agreed the university should develop formal policies. Students were significantly more likely than faculty to find generative AI use for coursework inappropriate (W = 35,334; p = 0.015) or unethical (W = 34,934; p = 0.025); more than two-thirds of students noted it can promote dishonesty, but half still felt it ethical and appropriate for coursework.
- Use differed by student subgroup: STEM male students used generative AI at significantly higher rates than non-STEM female students (p < 0.001), and non-STEM male students used it at significantly higher rates than non-STEM female students (p < 0.05).
Methods
- Anonymous online survey conducted December 2023–January 2024 at a large land-grant university in the southeastern United States; participants were 982 students and 76 faculty engaged in at least one regular Fall 2023 course. Student subgroup analysis used 850 observations due to missing gender; faculty analyses on learning/use excluded 5 observations due to missing values.
- Instrument drew on UTAUT, using Strzelecki’s modification that replaces Price Value with Personal Innovativeness, plus items from Amani et al. and White et al. on learning competencies, ethics, policy, and frequency. Seven constructs were scored: Performance Expectancy, Effort Expectancy, Social Influence, Hedonic Motivation, Habit, Personal Innovativeness, and Behavioral Intention; Facilitating Conditions was excluded due to weak Cronbach’s alpha and inadequate CFA results.
- Analyses included confirmatory factor analysis, Cronbach’s alpha, CFI/TLI/RMSEA; Welch’s two-sample t-tests for student–faculty comparisons; ANOVA and Tukey’s HSD for four gender-STEM student groups; and Wilcoxon rank sum tests for Likert-scale items, using R.
Limitations
- Stated by the authors: the cross-sectional design did not explore how perceptions evolve over time; the faculty sample (n = 76) was small relative to the student sample (n = 982), potentially limiting robustness and generalizability; and the single large public southeastern U.S. institution limits broader applicability.
- Stated by the authors: course-level analysis was outside the study’s scope, and future research should examine generative AI use across disciplines and course types.
- Evident from the text: the survey was voluntary, incentivized by a gift-card drawing, and distributed through multiple outlets, so self-selection may affect the sample; the authors also excluded the Facilitating Conditions factor from final results because of weak psychometric performance.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
0 of 17 cited sources checked · 0 supported · 0 flagged
- Findingown data
Significant differences in attitudes toward generative AI were evident between STEM and non-STEM majors.
“Significant differences were evident among student sub-populations, particularly between STEM and non-STEM majors and across gender lines, with males in STEM showing the strongest positive attitudes toward generative AI.”
Quote in the paperSubstantial claim - Findingown data
Students and faculty use generative AI tools at similar rates (W = 33,630; p = 0.542).
“Students and faculty use generative AI tools at similar rates (W = 33,630; p = 0.542).”
Quote in the paperNot a substantial claim- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
- Findingown data
Both students and faculty tended to agree that generative AI will have more negative effects than positive effects on most aspects of student learning, such as Problem Solving, Teamwork, Self-efficacy, Test Anxiety, Intrinsic Motivation, and Engagement.
“Overall, both groups tended to agree that generative AI will have more negative effects than positive effects on most aspects of student learning, such as Problem Solving, Teamwork, Self-efficacy, Test Anxiety, Intrinsic Motivation, and Engagement.”
Quote in the paperSubstantial claim- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
- Findingown data
Significant differences in attitudes toward generative AI were evident across gender lines among student sub-populations.
“Significant differences were evident among student sub-populations, particularly between STEM and non-STEM majors and across gender lines, with males in STEM showing the strongest positive attitudes toward generative AI.”
Quote in the paperNot a substantial claim - Findingown data
Students were significantly more interested in exploring new technologies (Personal Innovativeness) than faculty (p < 0.01).
“Students were also significantly more interested in exploring new technologies (Personal Innovativeness; p < 0.01) compared to faculty.”
Quote in the paperNot a substantial claim- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
- Findingown data
Non-STEM male students use generative AI tools at significantly higher rates than non-STEM female students (p < 0.05).
“Moreover, non-STEM male students use generative AI tools at significantly higher rates than non-STEM female students (p < 0.05).”
Quote in the paperSubstantial claim- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
- Findingown data
Students and faculty recognized potential benefits of generative AI to academic performance.
“Despite recognizing the potential benefits to academic performance, students and faculty were concerned about negative impacts on student learning competencies and about ethical considerations.”
Quote in the paperNot a substantial claim - Findingown data
Both students and faculty agreed that policies or guidelines are needed to navigate the inclusion of generative AI tools in educational settings.
“Both groups agreed that policies or guidelines are needed to navigate the inclusion of generative AI tools in educational settings.”
Quote in the paperSubstantial claim - Findingown data
Students were significantly more likely than faculty to find the use of generative AI for coursework inappropriate (W = 35,334; p = 0.015).
“The two groups may disagree on the content of such policies, however, as students were significantly more likely than faculty to find the use of generative AI for coursework to be inappropriate (W = 35,334; p = 0.015) or unethical (W = 34,934; p = 0.025).”
Quote in the paperSubstantial claim- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
- Findingown data
Students reported greater ease of learning with generative AI tools than faculty.
“Students reported greater enjoyment and ease of learning with generative AI tools and were more comfortable exploring new technologies than faculty.”
Quote in the paperSubstantial claim - Findingown data
Student groups agreed on the ethical issues associated with generative AI use in courses regardless of their gender and major, except for the need to develop a university policy.
“Student groups agreed on the ethical issues associated with generative AI use in courses regardless of their gender and major, except for the need to develop a university policy (Fig. 7).”
Quote in the paperNot a substantial claim- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
- Findingown data
Attitudinal differences toward generative AI did not translate into significant differences between students and faculty in their intentions to use generative AI tools (Behavioral Intention).
“However, these attitudinal differences toward generative AI did not translate into significant differences between the two groups in their intentions to use generative AI tools (Behavioral Intention).”
Quote in the paperSubstantial claim- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
- Findingown data
Students and faculty did not differ significantly in most of their views on how generative AI tools could affect specific student learning competencies or overall learning.
“Students and faculty did not differ significantly in most of their views on how generative AI tools could affect specific student learning competencies or overall learning, as illustrated in Table 3.”
Quote in the paperSubstantial claim- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
- Findingown data
Students and faculty agreed that generative AI tools can exacerbate academic dishonesty and cheating (W = 33,283; p = 0.140).
“For instance, students and faculty agreed that generative AI tools can exacerbate academic dishonesty and cheating (W = 33,283; p = 0.140) but should still be used in courses (W = 34,005; p = 0.071).”
Quote in the paperNot a substantial claim- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
- Findingown data
STEM male students report generative AI would have more positive impacts on overall learning than non-STEM female students (p < 0.01).
“Additionally, STEM male students report generative AI would have more positive impacts on overall learning than non-STEM female students (p < 0.01).”
Quote in the paperNot a substantial claim- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
- Findingown data
Students reported greater enjoyment with generative AI tools than faculty.
“Students reported greater enjoyment and ease of learning with generative AI tools and were more comfortable exploring new technologies than faculty.”
Quote in the paperSubstantial claim - Findingown data
When comparing the four major/gender groups of students, there were no significant differences in Critical Thinking, Problem Solving, Test Anxiety, Intrinsic Motivation, and Engagement.
“When comparing the four major/gender groups of students (Fig. 5), there were no significant differences in Critical Thinking, Problem Solving, Test Anxiety, Intrinsic Motivation, and Engagement.”
Quote in the paperNot a substantial claim- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
- Findingown data
When comparing the four major/gender groups of students, there were significant differences regarding Teamwork, Self-efficacy, Academic Performance, and Overall Learning.
“However, there were significant differences regarding Teamwork, Self-efficacy, Academic Performance, and Overall Learning.”
Quote in the paperNot a substantial claim- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
- Findingown data
Students and faculty agreed that generative AI tools should still be used in courses (W = 34,005; p = 0.071).
“For instance, students and faculty agreed that generative AI tools can exacerbate academic dishonesty and cheating (W = 33,283; p = 0.140) but should still be used in courses (W = 34,005; p = 0.071).”
Quote in the paperNot a substantial claim- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
- Findingown data
STEM male students use generative AI tools at significantly higher rates than non-STEM female students (p < 0.001).
“STEM male students use generative AI tools at significantly higher rates than non-STEM female students (p < 0.001).”
Quote in the paperSubstantial claim- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
- Findingown data
Generative AI use is more habitual for faculty than students (Habit; p < 0.05).
“Conversely, generative AI use is more habitual for faculty than students (Habit; p < 0.05).”
Quote in the paperNot a substantial claim- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
- Findingown data
Students thought that generative AI tools were easier to learn (Effort Expectancy) than faculty (p < 0.01).
“However, students thought that generative AI tools were easier to learn (Effort Expectancy; p < 0.01) and more fun to use (Hedonic Motivation; 2.”
Quote in the paperNot a substantial claim- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
- Findingown data
Students were significantly more positive than faculty about the potential benefits of generative AI tools for Academic Performance (W = 39,673, p < 0.001).
“The sole exception was Academic Performance (Fig. 4), where students were significantly more positive than faculty about the potential benefits of generative AI tools (W = 39,673, p < 0.001).”
Quote in the paperSubstantial claim- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
- Findingown data
Students thought that generative AI tools were more fun to use (Hedonic Motivation) than faculty.
“However, students thought that generative AI tools were easier to learn (Effort Expectancy; p < 0.01) and more fun to use (Hedonic Motivation; 2.”
Quote in the paperNot a substantial claim- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
- Findingown data
STEM male students think generative AI would have more positive impacts on academic performance than STEM female students (p < 0.05).
“For instance, STEM male students think generative AI would have more positive impacts on academic performance than STEM female students (p < 0.05).”
Quote in the paperNot a substantial claim- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
- Findingnone given
There was no significant difference in the usage of generative AI tools between students and faculty.
“However, there was no significant difference in the usage of generative AI tools between students and faculty.”
Quote in the paperNot a substantial claim - Findingown data
When comparing the four major/gender groups of students, there were significant differences for each of the attitudinal constructs.
“When comparing the four major/gender groups of students, we found significant differences for each of the attitudinal constructs (Table 2).”
Quote in the paperNot a substantial claim- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
- Findingown data
Students and faculty were concerned about ethical considerations regarding generative AI.
“Despite recognizing the potential benefits to academic performance, students and faculty were concerned about negative impacts on student learning competencies and about ethical considerations.”
Quote in the paperSubstantial claim - Findingown data
There were no substantial differences between students and faculty regarding their views on the ethics of and need for policy relating to generative AI tools.
“Overall, there were no substantial differences between students and faculty regarding their views on the ethics of and need for policy relating to generative AI tools (Table 4 and Fig. 6).”
Quote in the paperSubstantial claim- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
- Findingown data
Non-STEM male students agree that a need for a university policy exists more than non-STEM female students (p < 0.05).
“Non-STEM male students agree that such a need exists more than non-STEM female students (p < 0.05).”
Quote in the paperNot a substantial claim- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
- Findingown data
Males in STEM showed the strongest positive attitudes toward generative AI.
“Significant differences were evident among student sub-populations, particularly between STEM and non-STEM majors and across gender lines, with males in STEM showing the strongest positive attitudes toward generative AI.”
Quote in the paperNot a substantial claim - Findingown data
Students and faculty were concerned about negative impacts of generative AI on student learning competencies.
“Despite recognizing the potential benefits to academic performance, students and faculty were concerned about negative impacts on student learning competencies and about ethical considerations.”
Quote in the paperSubstantial claim - Findingown data
Approximately 70% of students and faculty used generative AI tools less than once per week.
“Approximately 70% of students and faculty used generative AI tools less than once per week (Fig. 8); only 6% of students and 9% of faculty used generative AI tools daily in Fall 2023.”
- Also stated in: § Comparing the Frequency of Use of Generative AI Tools in Courses · plain
“Generative AI use for university coursework and teaching preparation is still relatively rare, with approximately two-thirds of students and faculty using generative AI tools less than once per week.”
Quote in the paperSubstantial claim- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
- Also stated in: § Comparing the Frequency of Use of Generative AI Tools in Courses · plain
- Findingown data
The pattern of STEM males being most positive and non-STEM females being least positive was also evident in students' plans to use generative AI tools (Behavioral Intention) in future university courses.
“This pattern was also evident in students' plans to use generative AI tools (Behavioral Intention) in future university courses (Fig. 3).”
Quote in the paperNot a substantial claim- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
- Findingown data
For three of the seven attitudes related to the use of generative AI tools in higher education, students and faculty did not differ significantly.
“For three of the seven attitudes related to the use of generative AI tools in higher education, students and faculty did not differ significantly.”
Quote in the paperNot a substantial claim- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
- Findingown data
Students were more comfortable exploring new technologies than faculty.
“Students reported greater enjoyment and ease of learning with generative AI tools and were more comfortable exploring new technologies than faculty.”
Quote in the paperSubstantial claim - Findingown data
Students were significantly more likely than faculty to find the use of generative AI for coursework unethical (W = 34,934; p = 0.025).
“The two groups may disagree on the content of such policies, however, as students were significantly more likely than faculty to find the use of generative AI for coursework to be inappropriate (W = 35,334; p = 0.015) or unethical (W = 34,934; p = 0.025).”
Quote in the paperSubstantial claim- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
- Findingown data
Respondents recognized that generative AI use may promote academic dishonesty.
“Respondents' views on the ethical issues are somewhat contradictory; while recognizing that generative AI use may promote academic dishonesty, both students and faculty generally feel its use in courses is appropriate and ethical and should not be banned.”
Quote in the paperNot a substantial claim - Findingown data
Both students and faculty generally feel that generative AI use in courses should not be banned.
“Respondents' views on the ethical issues are somewhat contradictory; while recognizing that generative AI use may promote academic dishonesty, both students and faculty generally feel its use in courses is appropriate and ethical and should not be banned.”
Quote in the paperSubstantial claim - Findingnone given
Perceptions of female non-STEM students were significantly different from those of male STEM students.
“Perceptions of female non-STEM students were significantly different from those of male STEM students.”
Quote in the paperNot a substantial claim - Findingcitation
Gender is important in students' attitudes and behaviors of generative AI use in higher education settings.
“We also identified the importance of gender in students' attitudes and behaviors of generative AI use in higher education settings, which aligns with results from the nationwide survey (Vogels, 2023).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Vogels, 2023
Not checked in the free run.
- Findingreasoning
Apprehension about generative AI's inappropriateness in an academic setting is stronger among students than faculty.
“Interestingly, the apprehension surrounding generative AI's inappropriateness in an academic setting is stronger among students than faculty.”
Quote in the paperNot a substantial claim - Interpretationown data
The overall results highlight the important role of gender and academic majors in students' use of generative AI tools.
“The overall results highlight the important role of gender and academic majors in students' use of generative AI tools, which corroborates the findings reported above.”
Quote in the paperSubstantial claim - Study design and conductnone given
The study's faculty sample size was limited.
“Second, our study's limited faculty sample size suggests that future research should aim to include larger, more diverse faculty populations.”
Quote in the paperNot a substantial claim - Interpretationcitation
The pattern of low usage demonstrates a resistance common in the historical introduction of new technology.
“This pattern demonstrates a resistance common in the historical introduction of new technology (Waits & Demana, 2000;Foer, 2012), suggesting that generative AI is in its early stages of integration into education.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Waits & Demana, 2000;Foer, 2012
Not checked in the free run.
- Premisecitation
Many universities have actively engaged in formulating guidelines for generative AI.
“Although many universities have actively engaged in formulating guidelines (e.g., Harvard University, 2024; The Ohio State University, 2024; University of Michigan, 2023; University of Pittsburgh, 2023) student and faculty perspectives could be incorporated more.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Harvard University, 2024; The Ohio State University, 2024; University of Michigan, 2023
Not checked in the free run.
- Interpretationcitation
The finding that apprehension about generative AI's inappropriateness is stronger among students than faculty aligns with the theory of ontological security.
“This finding aligns with the theory of ontological security, which focuses on the importance of having a stable identity in a predictable world (Browning & Joenniemi, 2017;Lupovici, 2023).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Browning & Joenniemi, 2017;Lupovici, 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationown data
According to the UTAUT framework, the low usage of generative AI reflects the interaction of multiple factors observed in this study.
“According to the UTAUT framework, this low usage reflects the interaction of multiple factors that we observed.”
Quote in the paperNot a substantial claim - Interpretationreasoning
STEM students use generative AI more than non-STEM majors.
“This leads to greater use of generative AI among STEM students compared to non-STEM majors.”
Quote in the paperSubstantial claim - Interpretationown data
The study's focus on one large land-grant institution in the southeastern United States limits the ability to generalize the results to other settings.
“Our study focused on one large land-grant institution in the southeastern United States, which limits the ability to generalize the results to other settings.”
Quote in the paperNot a substantial claim - Interpretationcitation
The low usage rate in this study corroborates the nationwide study.
“The low usage rate also corroborates a nationwide study that reports only 14% of adults used ChatGPT (Vogels, 2023).”
Quote in the paperNot a substantial claim - Interpretationreasoning
Generative AI is in its early stages of integration into education.
“This pattern demonstrates a resistance common in the historical introduction of new technology (Waits & Demana, 2000;Foer, 2012), suggesting that generative AI is in its early stages of integration into education.”
Quote in the paperNot a substantial claim - Interpretationcitation
The finding about gender importance aligns with results from the nationwide survey.
“We also identified the importance of gender in students' attitudes and behaviors of generative AI use in higher education settings, which aligns with results from the nationwide survey (Vogels, 2023).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Vogels, 2023
Not checked in the free run.
- May combine two claims. 10
- The quote only partly states this claim. 1
- Classified as probably not a substantial claim. 17
May combine two claims.
Findingown dataIn many constructs, males in STEM fields were the most positive about generative AI tools, followed by STEM females and non-STEM males, with non-STEM females being the least positive.
“In many of the constructs, we observed similar patterns, where males in STEM fields were the most positive about generative AI tools, followed by STEM females and non-STEM males, with non-STEM females being the least positive about generative AI tools (Fig. 3).”
Quote in the paperNot a substantial claim- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
May combine two claims.
Findingown dataOnly 6% of students and 9% of faculty used generative AI tools daily in Fall 2023.
“Approximately 70% of students and faculty used generative AI tools less than once per week (Fig. 8); only 6% of students and 9% of faculty used generative AI tools daily in Fall 2023.”
Quote in the paperSubstantial claim- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
The quote only partly states this claim.
Findingown dataThe study uncovered significant apprehension about generative AI's impact on foundational learning competencies, including critical thinking and problem solving.
“However, the study also uncovered significant apprehension about generative AI's impact on foundational learning competencies”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Amani et al., 2023; White et al., 2024
Not checked in the free run.
May combine two claims.
Findingown dataBoth students and faculty generally feel that generative AI use in courses is appropriate and ethical.
“Respondents' views on the ethical issues are somewhat contradictory; while recognizing that generative AI use may promote academic dishonesty, both students and faculty generally feel its use in courses is appropriate and ethical and should not be banned.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThere is an overarching positive attitude toward generative AI in education, highlighted by perceived ease of use, potential benefits, and enjoyment of engaging with new technology.
“Our investigation aligns with the Unified Theory of Acceptance and Use of Technology (Venkatesh & Xu, 2012;Venkatesh et al., 2016), revealing an overarching positive attitude toward generative AI in education, highlighted by its perceived ease of use, potential benefits, and the enjoyment of engaging with new technology.”
Quote in the paperSubstantial claimMay combine two claims.
Findingnone givenStudents majoring in STEM fields are further toward acceptance of generative AI, with more positive attitudes and fewer ethical qualms than non-STEM students.
“Students majoring in STEM fields, however, are further toward acceptance, with more positive attitudes and fewer ethical qualms about generative AI.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataActual use of generative AI is low, with more than half of respondents reporting infrequent or no use of generative AI tools.
“Despite the recognized potential of generative AI in education (Alasadi & Baiz, 2023;Atkins et al., 2024;Chan & Hu, 2023;Lo, 2023;Wang et al., 2023), its actual use is low, with more than half of respondents reporting infrequent or no use of generative AI tools.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationKhlaif et al. (2024) found that performance expectancy and other factors affected educators' behavioral intentions to integrate generative AI into their teaching practices.
“Khlaif et al. (2024) found that performance expectancy and other factors also affected educators' behavioral intentions to integrate generative AI into their teaching practices.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Khlaif et al. (2024)
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationStrzelecki (2023) demonstrated that Performance Expectancy was a strong predictor of intention to use ChatGPT.
“Strzelecki (2023) demonstrated that Performance Expectancy (i.e., students' belief that ChatGPT would improve their productivity and academic performance) was a strong predictor of intention to use the tool.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Strzelecki (2023)
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
InterpretationreasoningPersonal innovativeness might be influenced by the greater emphasis on experimentation and technology adoption in STEM fields.
“Similarly, personal innovativeness might be influenced by the greater emphasis on experimentation and technology adoption in STEM fields, where students are encouraged to explore new tools for problem-solving and research.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationThe UTAUT model identifies seven factors that explain an individual's intention to use technology: Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic Motivation, Price Value, and Habit.
“The model identifies seven factors that explain an individual's intention to use technology: Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic Motivation, Price Value, and Habit.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Venkatesh and colleagues (2012, 2016)
Not checked in the free run.
May combine two claims.
Study design and conductreasoningThe study was conducted at a single large public university in the southeastern United States, which may limit the broader applicability of the results to other institutions.
“Second, the study was conducted at a single large public university in the southeastern United States, which may limit the broader applicability of the results to other institutions with different student populations, educational environments, or technological policies.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataFor analysis regarding views on learning, student competencies, and the use of generative AI tools, five observations from the faculty sample were excluded because of missing values.
“For analysis regarding views on learning, student competencies, and the use of generative AI tools, five observations from the faculty sample were excluded because of missing values.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningThe observed differences in performance expectancy and personal innovativeness might result from contextual factors such as different academic environments and disciplinary cultures in STEM and non-STEM fields.
“The specific differences observed in performance expectancy and personal innovativeness might result from a combination of contextual factors, such as the different academic environments and disciplinary cultures in STEM and non-STEM fields.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataEight hundred fifty observations were used for the student subgroup analysis because of missing values regarding gender.
“Eight hundred fifty observations were used for the student subgroup analysis because of missing values regarding gender.”
Quote in the paperNot a substantial claimMay combine two claims.
Study design and conductown dataThe faculty sample size (n = 76) is relatively small compared to the student sample (n = 982), which may introduce a potential imbalance in the data and limit the robustness of comparisons between these groups.
“First, the faculty sample size (n = 76) is relatively small compared to the student sample (n = 982), which may introduce a potential imbalance in the data and limit the robustness of comparisons between these groups.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
InterpretationreasoningPerformance expectancy might be higher among STEM students due to their more frequent interaction with generative AI tools in technical disciplines.
“For example, performance expectancy might be higher among STEM students due to their more frequent interaction with generative AI tools in technical disciplines, where these technologies are often integrated into coursework and professional practice.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationA nationwide study reports only 14% of adults used ChatGPT.
“The low usage rate also corroborates a nationwide study that reports only 14% of adults used ChatGPT (Vogels, 2023).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Vogels, 2023
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationGenerative AI has recognized potential in education.
“Despite the recognized potential of generative AI in education (Alasadi & Baiz, 2023;Atkins et al., 2024;Chan & Hu, 2023;Lo, 2023;Wang et al., 2023), its actual use is low, with more than half of respondents reporting infrequent or no use of generative AI tools.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Alasadi & Baiz, 2023; Atkins et al., 2024; Chan & Hu, 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
InterpretationreasoningThe disruptive nature of new technologies like generative AI may pose a greater threat to the identity and stability of students, who are at formative stages of personal and professional development.
“The disruptive nature of new technologies, like generative AI, may pose a greater threat to the identity and stability of students, who are at formative stages of personal and professional development.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationEducators are more likely to adopt generative AI tools when supported by systematic strategies for assignment design and institutional guidance on ethical use.
“Research indicates that educators are more likely to adopt generative AI tools when supported by systematic strategies for assignment design and institutional guidance on ethical use (Khlaif et al., 2024).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Khlaif et al., 2024
Not checked in the free run.
May combine two claims.
InterpretationreasoningGroup disparities in perceptions of generative AI reflect the nature of the disciplines and differing levels of exposure to and familiarity with emerging technologies.
“These group disparities reflect not only the nature of the disciplines but also differing levels of exposure to and familiarity with emerging technologies, which shape students' and faculty's perceptions of generative AI's utility and their willingness to adopt it.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningAs a cross-sectional study, this study did not explore how students' and faculty's perceptions of generative AI may evolve over time.
“First, as a cross-sectional study, we did not explore how students' and faculty's perceptions of generative AI may evolve over time.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationown dataThe investigation aligns with the Unified Theory of Acceptance and Use of Technology.
“Our investigation aligns with the Unified Theory of Acceptance and Use of Technology (Venkatesh & Xu, 2012;Venkatesh et al., 2016), revealing an overarching positive attitude toward generative AI in education, highlighted by its perceived ease of use, potential benefits, and the enjoyment of engaging with new technology.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Venkatesh & Xu, 2012;Venkatesh et al., 2016
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThe CANGARU initiative is an example of an AI policy developed with substantial stakeholder input.
“One example of an AI policy developed with substantial stakeholder input is the ChatGPT, Generative Artificial Intelligence, and Natural Large Language Models for Accountable Reporting and Use Guidelines (CANGARU) initiative (Cacciamani et al., 2023).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Cacciamani et al., 2023 source
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable).
PremisecitationThe ability to effectively use generative AI will become a critical skill needed by industry and in the academic domain.
“The ability to effectively use generative AI will become a critical skill that will be needed by industry, as well as in the academic domain (Edelman & Abraham, 2023;Edwards, 2023).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Edelman & Abraham, 2023;Edwards, 2023
Not checked in the free run.
May combine two claims.
PremisecitationFaculty with limited experience using generative AI tools often lack confidence to implement these technologies and express a need for additional training and education.
“Faculty with limited experience using generative AI tools often lack the confidence to implement these technologies and express a need for additional training and education (Harper, 2024).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Harper, 2024
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
PremisecitationRecent research recommends a balanced approach where universities support educators in leveraging AI's strengths while maintaining unique qualities of human teaching.
“Recent research recommends a balanced approach, emphasizing that universities should support educators in leveraging AI's strengths while maintaining the unique qualities of human teaching, such as fostering critical thinking and social-emotional skills (Chan & Tsi, 2024).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Chan & Tsi, 2024 source
Not checked in the free run.
The paper relies on this without support.
Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
“Data were collected through an anonymous online survey that was distributed to faculty and students at the university through multiple outlets, such as the university's online learning platform pop-up message, the research team's personal e-mail list, and flyers.”
“The survey used Likert-scale categories for responses, allowing participants to express their level of agreement or disagreement with statements related to predictors identified by Strzelecki (2023) and Strzelecki et al. (2024).”
The paper relies on this without support.
The ethics associated with generative AI are still contentious, and guidelines for its use are vague.
“The ethics associated with this technology are still contentious, and any guidelines for use are vague.”
The paper relies on this without support.
There are limited formal education opportunities (e.g., semester-long classes) on generative AI tools for non-STEM students provided by higher education schools in the United States.
“However, to our best knowledge, there are limited formal education opportunities (e.g., semester-long classes) on generative AI tools for non-STEM students provided by higher education schools in the United States.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Institutions are establishing cross-departmental AI governance committees to ensure that policymaking reflects the diverse needs of various academic disciplines.
“institutions are establishing cross-departmental AI governance committees to ensure that policymaking reflects the diverse needs of various academic disciplines (Gonsalves, 2025; Kim et al., 2025; Kutty et al., 2024).”
From Six Institutional Intervention Areas to Support Ethical and Effective Student Use of Generative AI in Higher Education: A Narrative Review (2026) · cites this paper as Kim et al., 2025
The Policy Recommendations section proposes task forces or working groups with representatives from STEM and non-STEM departments to draft generative AI policies. It recommends this approach; it does not report that institutions are already establishing such committees. (LLM, conf=0.88)
Experimental — accuracy not yet measuredDemographic and disciplinary differences exist in student perceptions.
“Kim et al. (2025) identify demographic and disciplinary differences in student perceptions.”
From From AI Literacy to AI Fluency: A Developmental Framework for Higher Education (2026) · cites this paper as Kim et al., 2025
Evaluation of faculty development programmes is often restricted to faculty satisfaction, with fewer studies examining behavioural change and long-term research productivity.
“Moreover, evaluation of faculty development programmes is often restricted to faculty satisfaction, with fewer studies examining behavioural change and long-term research productivity [12, 13].”
From Ethical engagement with artificial intelligence in faculty research education: evaluation of the New Knowledge Help (NKH) approach (2026) · cites this paper as [13]
The Survey Instrument and Data Collection and Statistical Analyses sections examine perceptions of generative AI, not evaluations of faculty development programmes. A search for faculty satisfaction, behavioural change, and long-term research productivity found no passage addressing the claimed pattern of evaluation. (LLM, conf=0.77)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Abstract omits significant student-faculty differences on ethics
MajorThe abstract says the only significant differences in attitudes between students and faculty concern four named factors. The results also report significant differences in whether using generative AI for coursework is inappropriate or unethical.
“We found that students and faculty did not differ significantly in their attitudes toward GenAI in higher education, except regarding ease of use, hedonic motivation, habit, and interest in exploring new technologies.”
“students were significantly more likely than faculty to find the use of generative AI for coursework to be inappropriate (W = 35,334; p = 0.015) or unethical (W = 34,934; p = 0.025).”
Discussion claims disciplinary differences in ethical qualms despite reported agreement
MinorThe results say student groups did not differ on ethical issues by gender or major, apart from views on the need for a university policy. The discussion instead says STEM students have fewer ethical qualms than non-STEM students.
“Student groups agreed on the ethical issues associated with generative AI use in courses regardless of their gender and major, except for the need to develop a university policy (Fig. 7).”
“Students majoring in STEM fields, however, are further toward acceptance, with more positive attitudes and fewer ethical qualms about generative AI.”
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InfluentialUsing the UTAUT model to understand students’ usage of e-learning systems in developing countries
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InfluentialGenerative AI in Education and Research: Opportunities, Concerns, and Solutions
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InfluentialOntological security, self-articulation and the securitization of identity
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“Despite the recognized potential of generative AI in education (Alasadi & Baiz, 2023; Atkins et al., 2024; Chan & Hu, 2023; Lo, 2023; Wang et al., 2023), its actual use is low, with more than half of respondents reporting infrequent or no use of generative AI tools.”
Generative AI tools can enhance climate literacy but must be checked for biases and inaccuracies
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“…Jang et al., 2024), courses and workshops can also instruct on generative AI’s weaknesses, such as hallucinations, inaccuracies, and biases (e.g., Atkins et al., 2024; Kim & Lee, 2023; Kim et al., 2024b), as well as the ethical issues associated with the use of tools (e.g., Kim et al., 2024a,…”
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“…et al., 2024), courses and workshops can also instruct on generative AI’s weaknesses, such as hallucinations, inaccuracies, and biases (e.g., Atkins et al., 2024; Kim & Lee, 2023; Kim et al., 2024b), as well as the ethical issues associated with the use of tools (e.g., Kim et al., 2024a, 2024b).”
Self-citationPlace identity: a generative AI’s perspective
Jang, K.-M., Chen, J., Kang, Y.-H., Kim, J., Lee, J., Duarte, F., & Ratti, C. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-03645-7 · Cited by 53 · cited 1× · see citing works
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Self-citationMultimodal Large Language Models as Built Environment Auditing Tools
Jang, K. M., & Kim, J. (2024) · The Professional Geographer · doi:10.1080/00330124.2024.2404894 · Cited by 37 · cited 1× · see citing works
“Perhaps as important as teaching the effective use of generative AI tools (e.g., Dubey et al., 2024; Jang & Kim, 2024; Jang et al., 2024), courses and workshops can also instruct on generative AI’s weaknesses, such as hallucinations, inaccuracies, and biases (e.g., Atkins et al., 2024; Kim & Lee,…”
Self-citationAI-generated visuals of car-free US cities help improve support for sustainable policies
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“Perhaps as important as teaching the effective use of generative AI tools (e.g., Dubey et al., 2024; Jang & Kim, 2024; Jang et al., 2024), courses and workshops can also instruct on generative AI’s weaknesses, such as hallucinations, inaccuracies, and biases (e.g., Atkins et al., 2024; Kim & Lee,…”
How does ChatGPT Introduce Transport Problems and Solutions in North America?
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“…et al., 2024), courses and workshops can also instruct on generative AI’s weaknesses, such as hallucinations, inaccuracies, and biases (e.g., Atkins et al., 2024; Kim & Lee, 2023; Kim et al., 2024b), as well as the ethical issues associated with the use of tools (e.g., Kim et al., 2024a, 2024b).”
Self-citationKim, D., Zhu, Q., & Eldardiry, H. (2024) · IEEE Transactions on Technology and Society · doi:10.1109/tts.2024.3430940 · Cited by 18 · cited 2× · see citing works
“…et al., 2024), courses and workshops can also instruct on generative AI’s weaknesses, such as hallucinations, inaccuracies, and biases (e.g., Atkins et al., 2024; Kim & Lee, 2023; Kim et al., 2024b), as well as the ethical issues associated with the use of tools (e.g., Kim et al., 2024a, 2024b).”
Self-citationOntological security, cyber technology, and states’ responses
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“This finding aligns with the theory of ontological security, which focuses on the importance of having a stable identity in a predictable world (Browning & Joenniemi, 2017; Lupovici, 2023).”
White, L. L. A., Balart, T., Amani, S., Shryock, K. J., & Watson, K. L. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2403.01538 · Cited by 5 · cited 4× · see citing works
“…perceived impacts of generative AI on the overall learning environments and eight specific learning goals: critical thinking, problem-solving, teamwork, self-efficacy, test anxiety, academic performance, intrinsic motivation, and student engagement (Amani et al., 2023; White et al., 2024).”
InfluentialHarper, M. (2024) · Digital Commons - USU (Utah State University) · doi:10.26076/949b-6210 · Cited by 3 · see citing works
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Alsharefeen, R., & Sayari, N. A. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1621743 · Cited by 48 · see citing works
Cui, Y. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100391 · Cited by 26 · see citing works
Jayasinghe, S., Gamage, K. A. A., Yang, D., Cheng, C., Disanayake, C., & Apeji, U. D. (2026) · Education Sciences · doi:10.3390/educsci16010137 · Cited by 23 · see citing works
Jwair, A. A. B. (2025) · Cogent Education · doi:10.1080/2331186x.2025.2589495 · Cited by 16 · see citing works
“Unlike prior research focused on demographics like gender (Kim et al., 2025) and discipline (Al-Huwail et al., 2025), it highlights digital competency as a key factor shaping attitudes toward AI.”
InfluentialAssessing computer science student attitudes towards AI ethics and policy
Weichert, J., Kim, D., Zhu, Q., Kim, J., & Eldardiry, H. (2025) · AI and Ethics · doi:10.1007/s43681-025-00817-2 · Cited by 17 · see citing works
“Finally, the survey of U.S. college students by Kim et al. [29] serves as an important baseline for our study, as it includes questions related to AI use.”
InfluentialSelf-citationMukhamedkarimova, D. F., & Umurkulova, M. M. (2025) · European Journal of Educational Research · doi:10.12973/eu-jer.14.4.1373 · Cited by 12 · see citing works
Paredes-Aguirre, M., Bolaños-Mendoza, C., Ortíz-Rojas, M., Martinez-Tovar, J., Sanchez-Mendoza, M., & Moreno-Abramowicz, M. (2026) · Cogent Education · doi:10.1080/2331186x.2026.2614093 · Cited by 12 · see citing works
Asamoah, M. K., & Amarteifio, J. (2025) · SN Social Sciences · doi:10.1007/s43545-025-01184-9 · Cited by 12 · see citing works
“Similarly, Kim et al. (2025) noted low adoption of ChatGPT by faculty for thesis reviews and plagiarism detection, due to doubts about its reliability and ethical alignment—findings that echo faculty hesitance in African settings lacking clear AI guidelines.”
Faculty as street-level bureaucrats: discretionary decision-making in the era of generative AI
Alsharefeen, R. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1662657 · Cited by 7 · see citing works
Joseph, M. M., & Areepattamannil, S. (2025) · Sustainability · doi:10.3390/su172210381 · Cited by 8 · see citing works
Harrington, B., Zlotnikova, I., Nadarajan, G., & Ekundayo, S. (2025) · ACM Transactions on Computing Education · doi:10.1145/3776558 · Cited by 8 · see citing works
“Similarly, Kim et al. [25] and Stone [55] reported that Canadian and US students showed discipline-specific ethical concerns: STEM students tended to view GenAI as a legitimate tool for innovation, while humanities students emphasized authorship and originality.”
Wu, C., & Carroll, J. M. (2025) · TechTrends · doi:10.1007/s11528-025-01150-5 · Cited by 6 · see citing works
Al-Shamrani, A. S. A., Alhomayani, R. E. S., & Ibrahim, A. M. (2026) · Sustainability · doi:10.3390/su18052450 · Cited by 6 · see citing works
Lv, P., Wang, X., Nie, Y., Wu, R., & Wang, L. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1633686 · Cited by 5 · see citing works
“However, both perceived GenAI as having more negative than positive effects on learning (Kim et al., 2025a).”
Exploring students’ perceptions of GenAI tools in higher education: a case study
Tbaishat, D. M., & Elfadel, M. W. (2025) · Cogent Education · doi:10.1080/2331186x.2025.2560613 · Cited by 7 · see citing works
“Furthermore, students ’ happiness with GenAI technologies is significantly influenced by their perceived utility, usability and degree of alignment with their academic requirements (J. Kim, Klopfer, et al., 2025).”
Alkabaa, A. S., & Alamri, N. M. H. (2025) · Computer Applications in Engineering Education · doi:10.1002/cae.70110 · Cited by 6 · see citing works
“This finding is supported by the studies that show that the male students tend to be more enthusiastic regarding AI technologies and are considered as heavily users [61, 68, 88].”
Cunningham, E., Kings, I., Robbins, P. T., & Keith, M. J. (2026) · Education for Chemical Engineers · doi:10.1016/j.ece.2026.100503 · Cited by 5 · see citing works
Artificial intelligence in higher education: stakeholder perceptions and policy implications
Lawrence, S. C. (2026) · Journal of Applied Research in Higher Education · doi:10.1108/jarhe-10-2025-0872 · Cited by 5 · see citing works
Yang, J., Öge, K., von Mühlenen, A., Akbulut, A. B., Carey, T. S., & Okorro, C. (2026) · Education Sciences · doi:10.3390/educsci16060838 · Cited by 4 · see citing works
“Although some cross-disciplinary evidence exists for students, including reported differences across STEM and non-STEM cohorts and gendered patterns of attitudes (Arowosegbe et al., 2024; Kim et al., 2025), systematic empirical comparisons among staff remain limited.”
Karakitsou, C., Karayianni, I., Leftheriotis, K., Logotheti, A., & Avgerinou, M. D. (2025) · Journal of Visual Literacy · doi:10.1080/1051144x.2025.2538256 · Cited by 3 · see citing works
“Interdisciplinary committees can engage faculty, students, and administrators in exploring the opportunities and challenges GenAI presents (Kim et al., 2025).”
InfluentialBai, Q., & Nam, B. H. (2025) · International Journal of Qualitative Methods · doi:10.1177/16094069251409086 · Cited by 3 · see citing works
“Hence, a persistent scholarly debate has been ongoing in this area (Kim et al., 2025).”
Quince, Z., Nikolic, S., & Goh, S. C.-N. (2025) · International Journal of Mechanical Engineering Education · doi:10.1177/03064190251407621 · Cited by 3 · see citing works
Pérez-Portabella, A., de Andrés-Sánchez, J., Arias-Oliva, M., & Castillo, G. P. (2026) · Innovative Higher Education · doi:10.1007/s10755-026-09885-6 · Cited by 3 · see citing works
Chea, P., & Deng, F. (2026) · Innovative Higher Education · doi:10.1007/s10755-026-09886-5 · Cited by 4 · see citing works
Exploring faculty adoption of ChatGPT: a stimuli-organism-response approach
Cambra‐Fierro, J., García, O. C., Blasco, M. F., López-Pérez, M., Ruz-Mendoza, M. Á., & Trifu, A. (2025) · Interactive Learning Environments · doi:10.1080/10494820.2025.2483415 · Cited by 3 · see citing works
“Furthermore, emotional and cognitive factors such as anxiety, trust, and perceived control mediate these responses, influencing the extent to which faculty members embrace technological innovations (Kim et al., 2025).”
Alieto, E. O., Dumagay, A. H., Eijansantos, A. M., Estigoy, E. B., Vallejo, R. G., & Serdenia, J. R. C. (2025) · Information systems engineering and management · doi:10.1007/978-3-032-05306-0_11 · Cited by 4 · see citing works
Reynolds, S. S., Kauschinger, E. D., Cadavero, A. A., Conrad, S., McMillian-Bohler, J. M., & Webb, M. A. (2025) · Nurse Education in Practice · doi:10.1016/j.nepr.2025.104648 · Cited by 2 · see citing works
Generational Divide in AI Adoption for Academic Writing: Evidence From Serbian Social Scientists
Galjak, M., & Budić, M. (2025) · Social science computer review · doi:10.1177/08944393251413796 · Cited by 2 · see citing works
Zilundu, P. L. M., Mazengenya, P., Jayaraj, K. N., & Zhou, L. (2026) · JMIR Medical Education · doi:10.2196/87418 · Cited by 3 · see citing works
“…findings show that high workload and limited support reduce effort expectancy and weaken facilitating conditions, thereby suppressing behavioral intention, whereas structured experimentation, repeated exposure, and organizational reinforcement can eventually consolidate AI use into a habit [20,53].”
Scoping the AI curriculum: key competencies for future AI practitioners
Weichert, J., & Eldardiry, H. (2026) · AI and Ethics · doi:10.1007/s43681-026-01021-6 · Cited by 2 · see citing works
“A comparison of AI use frequency between the CS students in our study and the cross-section of the students in the Kim et al. study [12] shows significantly higher AI adoption among CS students.”
Self-citationStudents’ Artificial Intelligence (AI) literacy: An exploratory study
Deshen, M., & Aharony, N. (2026) · Journal of Librarianship and Information Science · doi:10.1177/09610006261442178 · Cited by 2 · see citing works
Li, X., Xian, X., & Du, P. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1903784 · Cited by 3 · see citing works
“…rapidly from acceptance and usefulness (Davis, 1989; Venkatesh et al., 2003) to AI literacy, academic integrity, institutional policies, and student/faculty perceptions (Almassaad et al., 2024; Baek et al., 2024; Bearman et al., 2023; Kim et al., 2025; Perkins, 2023; Von Garrel and Mayer, 2023).”
Kurnia, N., Asy’ari, M., Wirzal, M. D. H., Nordin, N. A. H. M., & Bilad, M. R. (2026) · Education for Chemical Engineers · doi:10.1016/j.ece.2026.100521 · Cited by 2 · see citing works
A survey of American public health faculty AI competency & readiness
Lord, E., Heboyan, V., Huang, H., Tracy, D., & De Leo, G. (2026) · Cogent Education · doi:10.1080/2331186x.2026.2654902 · Cited by 1 · see citing works
Mengesha, Z., Estellore, J., Smith, J., George, M. S., McFarlane, R. A., & Parmar, J. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1825937 · Cited by 1 · see citing works
AI and GIS Education: The Last Holdout Against AI Cheating (For Now)
Hagge, P. D. (2025) · Journal of Geography · doi:10.1080/00221341.2025.2550948 · Cited by 1 · see citing works
“Shortly after, a Fall 2023 survey found that while faculty were somewhat apprehensive, university students rated AI as very positive for their own academic performance (Kim et al. 2025).”
Pérez, C. S. P. (2025) · Revista Española de Educación Médica · doi:10.6018/edumed.679941 · Cited by 1 · see citing works
Comparing Faculty Attitudes Toward Self-Use and Student-Use of AI Technologies in Higher Education
Rodafinos, A., Velli, K., & Barkoukis, V. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2630090 · Cited by 2 · see citing works
“Most existing literature, with few exceptions (Kim et al., 2025), tends to examine faculty and student roles separately, which can obscure the ethical and practical tensions that arise when educators evaluate AI use across both roles.”
Concerns and Ethical Awareness of University Students Towards the Use of Generative AI
Yaacob, Y., Mahmud, M. M., Ahmad, R., A’seri, M. S. M., & Mustamam, N. I. (2025) · International Conference on Education Technology and Computer · doi:10.1109/icetc66579.2025.11387607 · Cited by 1 · see citing works
“The study revealed significant concerns among university students regarding ethical issues, emphasizing the need to integrate generative AI responsibly in academia [23].”
Mezzadri, D. (2026) · Philosophy & Technology · doi:10.1007/s13347-026-01072-x · Cited by 1 · see citing works
Undergraduate mathematics faculty attitudes and experiences with generative AI integration
Joung, E., Brinkerhoff, R., Lee, I., Wong, A., & Ioane, O. (2026) · Eurasia Journal of Mathematics Science and Technology Education · doi:10.29333/ejmste/18126 · Cited by 1 · see citing works
“With GenAI use for teaching still relatively rare and few clear university guidelines currently in place, faculty face challenges in rethinking assessment practices without adequate support (Baidoo-Anu & Owusu Ansah, 2023; Kim et al., 2025).”
InfluentialWang, Y., & Fussell, S. R. (2026) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3799448 · Cited by 2 · see citing works
Generative AI in the eyes of the academy: Comparative analysis of faculty and student perceptions
Miranda, F. J., & Mera, A. C. (2026) · EDUWEB · doi:10.46502/issn.1856-7576/2026.20.01.12 · Cited by 1 · see citing works
Yan, G., Zhu, C., Li, T., Yip, J., & Ni, J. (2026) · PLoS ONE · doi:10.1371/journal.pone.0347653 · Cited by 1 · see citing works
Yilmaz, A. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1853790 · Cited by 1 · see citing works
“In parallel, Kim et al. (2025) showed that students’ and faculty members’ attitudes toward generative artificial intelligence converge across many dimensions; however, students find artificial intelligence easier to use and more appealing.”
Development and content validation of the Quality in Psychology Simulation Instrument (QPSI)
Yap, K., Gooi, C. H., Oxlad, M., Paparo, J., Correia, H., Chur‐Hansen, A., Rice, K., Rooney, R. M., & Sheen, J. A. (2026) · Australian Psychologist · doi:10.1080/00050067.2026.2683185 · Cited by 1 · see citing works
“Furthermore, with the increased use of generative AI in student assessment submissions, educators need to develop authentic learning activities and assessments that ensure genuine development in student competencies (Kim et al., 2025).”
For Factors of AI Integration in Research: Case Study of Russian Laboratories
Sazonov, A., Popova, E. V., & Matsepuro, D. M. (2026) · Высшее образование в России · doi:10.31992/0869-3617-2026-35-6-149-168 · Cited by 1 · see citing works
Ren, S. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1926110
“…feedback, and support for writing or brainstorming, while also expressing concerns about factual accuracy, academic integrity, overreliance, privacy, and the devaluation of independent learning (Chan and Hu, 2023; Blahopoulou and Ortiz-Bonnin, 2025; Kim et al., 2025; Maxwell et al., 2025).”
From AI Literacy to AI Fluency: A Developmental Framework for Higher Education
Zentner, A. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7014818
Naeem, N.-I.-K., Naeem, Z.-E.-F., & Anwer, A. (2026) · BMC Medical Education · doi:10.1186/s12909-026-09405-2
“Moreover, evaluation of faculty development programmes is often restricted to faculty satisfaction, with fewer studies examining behavioural change and long-term research productivity [12, 13].”
Tang, M. (2025) · Lecture Notes in Education Psychology and Public Media · doi:10.54254/2753-7048/2025.cb24365
Agente conversacional: ¿medio tecnológico para innovar el entorno educativo?
Salas-Rueda, R.-A., Luna-García, I., Santiago-de-la-Torre, J., & Cervantes-de-la-Cruz, K.-E. (2025) · Revista Ecos de la Academia · doi:10.53358/ecosacademia.v11i21.1227
Luo, Y. G., Liu, H., & Chen, E. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5385927
Artificial Intelligence (AI) Integration and Higher Education Faculty
Williams, J. E., Luongo, N., Finetti, M., & Garrels, J. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0396-3.ch007
Bridging AI Adoption and Governance in Higher Education: A Longitudinal Study on Dual-Use Challenges
López, F., Angulo-Alvarez, M., Navarrete, W. V. L., Ushiña, A. S., & Gutiérrez, H. R. B. (2025) · Information systems engineering and management · doi:10.1007/978-3-032-00202-0_13
Serdenia, J. R. C. (2025) · EthAIca · doi:10.56294/ai2025443
Jiang, Y., & Chen, H. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5934297
Bridging the AI Gap: Developing AI Literacy in Programming Education
Kohen-Vacs, D., Usher, M., & Jansen, M. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-05070-0_18
Salwani, U. D., Lesmana, J. A., Laura, S. M., & Hutabarat, A. S. (2025) · International Conference on Digital Transformation and Applications (ICDXA) · doi:10.1109/icdxa69105.2025.11330076
“This moderation contextualizes the model for higher education and highlights discipline-specific post-adoption behaviors [4,11].”
InfluentialReconsidering Innovation in Higher Education: Co-construction of a Concept
Major, C. H., & Braxton, J. M. (2026) · Innovative Higher Education · doi:10.1007/s10755-025-09871-4
“Our published articles that focused on technology (Kim et al., 2025; Liu et al., 2025; Radović & Seidel, 2025), our attention to dashboards, metrics, and bibliometrics (Belizon et al., 2025; Bergom & Look, 2025), and our scrutiny of policies and discourses all suggest that technology is inseparable…”
Higher-Education Faculty Adoption of Generative AI in Lesson Planning and Assessment
Siyam, N., Turki, R., Tamimi, H., Khdour, T., Atassi, R., & Almiani, K. (2025) · International Conference on Information Technology Trends (ITT) · doi:10.1109/itt69610.2025.11352915
“Comparative work finds faculty and students are often similarly positive overall, with differences emerging around ease of use and exploratory motivation [24], [25], [26].”
Designing and Assessing AI-Enabled Programs in the Helping Professions
Norton, J. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-4362-4.ch008
From Curiosity to Practice: Exploring Generative AI Adoption in Non-STEM Higher Education
Patricia, A., Limios, B., Tanadi, J., & Limantara, N. (2025) · International Conference on Emerging Trends in Information Systems and Informatics (ICETISI) · doi:10.1109/icetisi67983.2025.11406061
“The predominance of female respondents in our sample also increases the likelihood of this pattern, as previous studies show that female students in non-STEM disciplines demonstrate more influence of social expectations in the areas of community conformity and collaboration [1].”
InfluentialCarmona, M. L., Solís, M. E. C., Martínez, E. L., Carbajal-Degante, E., & Lastra, J. A. S. (2026) · D Perspectivas Siglo XXI · doi:10.53436/kfwvx649
Jeya, S., & Saravanakumar, A. (2026) · Innovare journal of education · doi:10.22159/ijoe.2026v14i2.58362
Chapman, R. M., Chapman, C., Johnson, H. E., & Chapman, D. D. (2026) · AI · doi:10.3390/ai7030106
Pereira, C. S., Durão, N., Ferreira, M. J., Sobral, S. R., Lobo, C., & Moreira, F. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.03.032
Do perceived benefits influence scholars' intention to use generative artificial intelligence?
Lenart, R., Sułkowski, Ł., Kaczorowska–Spychalska, D., Mazurek, G., Kotula, N., & Cassar, V. (2026) · Central European Management Journal · doi:10.1108/cemj-08-2025-0254
Factors Associated with AI Use in a Norwegian Sample
Thorp, S. O., Rimol, L. M., Fleten, M. K., & Hoel, S. K. B. (2026) · Behavioral Sciences · doi:10.3390/bs16040537
“Higher education mirrors this divide, with STEM students and faculty reporting higher adoption and comfort with GenAI than non-STEM peers (Freeman, 2025; J. Kim et al., 2025; Parviz, 2024).”
The Role of Habit in Promoting Generative AI Adoption Among Social Science Students and Researchers
Spilbergs, A. (2026) · International Journal of Education and Information Technologies · doi:10.46300/9109.2026.20.8
Comparative Study of Student and Faculty Experiences with Generative AI in Higher Education
Uukkivi, A., Maksimova, N., Labanova, O., Retšnoi, V., Safiulina, E., Petjärv, B., & Vilms, M. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-18888-5_8
Students’ Use of Generative AI Tools in Multiple Choice Exams
Serban, N., Ketenci, T., & Sokol, J. (2026) · INFORMS Transactions on Education · doi:10.1287/ited.2025.0166
LEARNING GAINS AND STUDENT RESERVATIONS: EVALUATING A GPT-BASED AI ASSISTANT IN GEOGRAPHY TEACHING
Drápela, E. (2026) · Public recreation and landscape protection ... · doi:10.11118/978-80-7701-087-0-0252
Khlaif, Z. N., Hamamra, B., & Farran, H. (2026) · Research Square · doi:10.21203/rs.3.rs-9192785/v1
Gonçalves, A. D. S., Pereira, S., Junior, J. D. O., & Ulfa, S. U. (2026) · International Conference on Educational and Information Technology · doi:10.1109/iceit68991.2026.11521406
“Additionally, user perceptions and attitudes toward Generative AI, including trust, perceived usefulness, and ease of use, are possibly more dominant factors than the mere availability of the technology itself [11].”
InfluentialAI tools in higher education gains and challenges across global south and global north
Olohunfunmi, I. A., Sabri, K., Khairuddin, A. Z., & Bamiro, N. B. (2026) · Discover Education · doi:10.1007/s44217-026-01606-7 · Cited by 1 · see citing works
“…[11, 68, 88] Generally reliable infrastructure, though gaps in institutional support and platform fa-miliarity may still hinder optimal use [44, 73] Perceived Usefulness & Performance Expectancy AI viewed as helpful for paraphrasing, grammar checks, summarizing, and assignment completion…”
Yan, J., Wu, J., & Wei, X. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-6571-8.ch004
Lee, T., Lee, S., & Kim, S. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-29791-4_30
Enhancing mental health and cognitive research with generative artificial intelligence
Joshi, G., Ghai, B., Goutam, N., & Joshi, N. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44344-2.00008-x
Generative AI and Educational Inequality
Lei, L., Zhaoying, H., Huang, L., Chen, X., & Sijia, M. (2026) · Education as Change · doi:10.25159/1947-9417/21808
A rubric to assess generative AI-based feedback on student writing assignments
Rankins, D. R., Tran, E. N., La, V. T., Huang, D. M., & Barry, R. M. (2026) · Journal of Microbiology and Biology Education · doi:10.1128/jmbe.00350-25
Romero‐Martínez, Á. (2026) · Research Square · doi:10.21203/rs.3.rs-10599376/v1
Radovan, M., & Radovan, D. M. (2026) · Education Sciences · doi:10.3390/educsci16081259
“Kim et al. (2025), collecting data in late 2023, found that roughly 70% of students used GenAI less than once per week.”
From traditional feedback literacy to AI feedback literacy: the mediating role of AI acceptance
Yıldız, M., Yazıcı, Z., & Peker, A. (2026) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2026.2714393
“Whereas some learners perceive its use as innovative and supportive (Kim et al. 2025), others have concerns about reliability, ethics, overdependency, and academic integrity (Alasadi and Baiz 2023; Karal & Ozdemir Sarialioglu, 2025).”
Global perspectives in generative AI integration in higher education
Wen, Y. (2026) · Higher Education · doi:10.1007/s10734-026-01736-9
Henttonen, A., Christidis, M., Kullenberg, H., Sormunen, T., Westerbotn, M., & Hägg‐Martinell, A. (2026) · BMC Medical Education · doi:10.1186/s12909-026-10113-0
Fadly, D., Husain, H., & Setyawan, D. (2026) · Venn Journal of Sustainable Innovation on Education Mathematics and Natural Sciences · doi:10.53696/venn.v5i4.636
Tsutsui, W., Huang, Y., Chen, J., Guariniello, C., Sundaram, S., Panchal, J. H., & DeLaurentis, D. A. (2026) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--61006
Artificial Intelligence in Changing Classroom Management and Teaching Support
Qamar, N., Sohail, T. B., Zaib, M., Nazir, F., & Salleh, N. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-3663-1.ch002
Papaneophytou, C. P., & Nicolaou, S. A. (2026) · Trends in Higher Education · doi:10.3390/higheredu5030084
“Verify that students can demonstrate competence independently and can critically evaluate AI, then feed the findings back into the next needs analysis [70].”
InfluentialZhang, Y., Shi, Y., & Lu, J. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1918525
“At the same time, they may face algorithmic opacity, blurred boundaries of academic integrity, technological dependence, perceived competence replacement and uncertainty about future careers (Chan and Hu, 2023; Crompton and Burke, 2023; Hu, 2026; Kim et al., 2025).”
Yang, Y., Cheng, Z., Meng, H., & Shen, J. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1920168
Student Use of and Views on GenAI for Writing
Kuznetsov, A., Sheely, A., & Baker, J. (2026) · Teaching Sociology · doi:10.1177/0092055x261478622
Terranova, E. R. H. (2026) · Revista Simón Rodríguez · doi:10.62319/simonrodriguez.v.6i12.216
Engaging undergraduates with urban planning and sustainable mobility: GenAI vs. human co-creation
Ito, K., Challa, S., Song, J., Gosling, S. D., Biljecki, F., & Kang, Y. (2026) · Journal of Geography in Higher Education · doi:10.1080/03098265.2026.2733022
“In geography education, ChatGPT has been assessed as a support tool for instructors (Redican et al., 2025), and a university-wide survey reports that students and faculty use and view it at similar rates (Kim et al., 2025).”
ChatGPT for interdisciplinary learning: Does it equally benefit STEM and non-STEM students
Zhu, G., Fan, X., Hou, C., Zhong, T., Zhu, W., SEOW, P. S. K., Chen, S. A., Rajalingam, P., Low, K. Y., & Tan, L. P. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100681
A survey study of Cambodian university lecturers’ experiences and perceptions of AI use
Heng, K., Sok, S., Sol, K., & Sor, R. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02313-w
“…drawing from the perspectives and attitudes of lecturers and students toward its adoption in higher education across various contexts [13, 26, 39, 69, 79, 91], relatively little is known about how university lecturers in developing countries like Cambodia experience and perceive the adoption…”
InfluentialWu, C., & Lam, R. (2026) · Assessing Writing · doi:10.1016/j.asw.2026.101125
Beyond publish-or-perish: Policy ambiguity and the paradox of “de-quantification” in faculty
Liu, T. (2026) · Research in Comparative and International Education · doi:10.1177/17454999261492666
Amponsah, C., Kyiewu, B., & Bessa-Simons, L. (2026) · Journal of Educational Technology and Online Learning · doi:10.31681/jetol.1991829
- Authors
- Kim, J., Klopfer, M., Grohs, J. R., Eldardiry, H., Weichert, J., Cox, L. A., & Pike, D.
- Affiliations
- Virginia Tech (US)
- Corresponding author
- Kim, Junghwan
- Venue
- Innovative Higher Education
- Published
- 2025-01-24
- Volume
- 50
- Issue
- 4
- Pages
- 1281-1313
- ISSN
- 0742-5627 (ISSN-L)
- 1573-1758
- OpenAlex
- W4406743598
- Semantic Scholar
- 04e3fadaff0a809a09473c0406c45101b4495074
- Cited by
- 109
- References
- 43
- Citations by year
- 2026: 75 · 2025: 34
- FWCI
- 113.90
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Innovative Higher Education (publishedVersion, cc-by)
- VTechWorks (Virginia Tech) (submittedVersion, cc-by)
- Funders
- Virginia Polytechnic Institute and State University (Technology-Enhanced Learning, Online Strategies (TLOS))
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Education › Technology-Enhanced Education Studies
- Keywords
- generative AI, ChatGPT, student perceptions, teacher perceptions, perceived ease of use, ethical considerations, gender differences, STEM majors, disciplinary differences, higher education, AI policy
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Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-06.
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