The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index; none of the 17 sources its claims cite is checked for support yet.
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.
Overview
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.
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Claims the paper makes
43 Own result · 14 Not checked yet · 11 Own interpretation · 6 Own method · 4 No basis given · 3 Sources not found
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Summary of Results
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Students and faculty use generative AI tools at similar rates (W = 33,630; p = 0.542).”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Comparing the Frequency of Use of Generative AI Tools in Courses
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Comparing Views on Learning, Student Competencies, and Use of Generative AI Tools
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Summary of Results
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Students were also significantly more interested in exploring new technologies (Personal Innovativeness; p < 0.01) compared to faculty.”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Comparing Attitudes about the Use of Generative AI Tools in Courses
plain- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Moreover, non-STEM male students use generative AI tools at significantly higher rates than non-STEM female students (p < 0.05).”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Comparing the Frequency of Use of Generative AI Tools in Courses
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Despite recognizing the potential benefits to academic performance, students and faculty were concerned about negative impacts on student learning competencies and about ethical considerations.”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Summary of Results
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Both groups agreed that policies or guidelines are needed to navigate the inclusion of generative AI tools in educational settings.”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Summary of Results
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Comparing Attitudes on Ethics of Using Generative AI Tools in Courses
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Students reported greater enjoyment and ease of learning with generative AI tools and were more comfortable exploring new technologies than faculty.”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Summary of Results
plain- Asserted: passes — The authors put it forward as true.
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- It may state two separate claims as one. 10
- The quote only partly states this claim. 1
- Probably not a substantial claim. 17
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Comparing Attitudes about the Use of Generative AI Tools in Courses
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Comparing the Frequency of Use of Generative AI Tools in Courses
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Rests on an assumption: Participants' self-reported attitudes and behaviors accurately reflect their actual attitudes and behaviors regarding generative AI.
In the paper
“However, the study also uncovered significant apprehension about generative AI's impact on foundational learning competencies”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Alignment with Past Research
What the sources say
- Not checked yetcites Amani et al., 2023; White et al., 2024
plain- Asserted: passes — The authors put it forward as true.
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The quote only partly states this claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Alignment with Past Research
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Alignment with Past Research
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Students majoring in STEM fields, however, are further toward acceptance, with more positive attitudes and fewer ethical qualms about generative AI.”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Alignment with Past Research
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Alignment with Past Research
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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It may state two separate claims as one.
In the paper
“Khlaif et al. (2024) found that performance expectancy and other factors also affected educators' behavioral intentions to integrate generative AI into their teaching practices.”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Applications of UTAUT in Educational Contexts
What the sources say
- Not checked yetcites Khlaif et al. (2024)
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“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.”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Applications of UTAUT in Educational Contexts
What the sources say
- Not checked yetcites Strzelecki (2023)
boosteddirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Summary of Results
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“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.”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Conceptual Framework
What the sources say
- Not checked yetcites Venkatesh and colleagues (2012, 2016)
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Statistical Analyses
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Statistical Analyses
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Summary of Results
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Eight hundred fifty observations were used for the student subgroup analysis because of missing values regarding gender.”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Statistical Analyses
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Statistical Analyses
hedgedconditionaln = 76- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Summary of Results
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“The low usage rate also corroborates a nationwide study that reports only 14% of adults used ChatGPT (Vogels, 2023).”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Alignment with Past Research
What the sources say
- Not checked yetcites Vogels, 2023
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“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.”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Alignment with Past Research
What the sources say
- Not matchedcites 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.
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Alignment with Past Research
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
In the paper
“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).”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Policy Recommendations
What the sources say
- Not checked yetcites Khlaif et al., 2024
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Summary of Results
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“First, as a cross-sectional study, we did not explore how students' and faculty's perceptions of generative AI may evolve over time.”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Limitations and Future Research
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“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.”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Alignment with Past Research
What the sources say
- Not matchedcites Venkatesh & Xu, 2012;Venkatesh et al., 2016
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“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).”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Policy Recommendations
What the sources say
- Not checked yetcites Cacciamani et al., 2023 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“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).”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Policy Recommendations
What the sources say
- Not checked yetcites Edelman & Abraham, 2023;Edwards, 2023
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“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).”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Policy Recommendations
What the sources say
- Not checked yetcites Harper, 2024
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“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).”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Policy Recommendations
What the sources say
- Not checked yetcites Chan & Tsi, 2024 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“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]
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.
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Abstract
“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.”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Comparing Attitudes on Ethics of Using Generative AI Tools in Courses
“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).”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Comparing Attitudes on Ethics of Using Generative AI Tools in Courses
“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).”
§ Examining Faculty and Student Perceptions of Generative AI in University Courses > Alignment with Past Research
“Students majoring in STEM fields, however, are further toward acceptance, with more positive attitudes and fewer ethical qualms about generative AI.”
Citations
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“This study was conducted within a large land-grant institution in the southeastern United States; the university offers a diverse array of academic programs from broad disciplines, including both STEM (Science, Technology, Engineering, and Math) and non-STEM degree programs.”
InfluentialDwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., et al. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2023.102642 · Cited by 4,584
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“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…”
Students' voices on generative ai: perceptions, benefits, and challenges in higher education
Chan, C. K. Y., & Hu, W. (2023) · arXiv · doi:10.48550/arxiv.2305.00290 · Cited by 2,504 · cited 1×
“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.”
Students’ voices on generative AI: perceptions, benefits, and challenges in higher education
Chan, C. K. Y., & Hu, W. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00411-8 · Cited by 2,301 · cited 1×
“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.”
What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature
Lo, C. K. (2023) · Education Sciences · doi:10.3390/educsci13040410 · Cited by 2,024 · cited 1×
“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.”
A comprehensive AI policy education framework for university teaching and learning
Chan, C. K. Y. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00408-3 · Cited by 1,577 · cited 2×
“This study highlights a critical juncture for universities in deciding how to navigate the future of generative AI in education (Chan, 2023).”
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“Additionally, there are differences in judgments between faculty and students about ease, enjoyment, habitual use, and appropriate boundaries (Kim et al., 2025).”
Sarmiento, J. B., & Jalani, N. T. (2026) · International Journal of Research on Multidisciplinary Studies · doi:10.67903/ijroms0405
After Integration: A Framework for Designed AI Withdrawal in Higher Education
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Mapping generative AI in higher education teaching and learning: a scoping review
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“…may make them less receptive to desirable difficulties that support deeper learning, while instructors’ caution about integrity and assessment validity may protect educational quality but also discourage productive experimentation (Dai & Chan, 2026; Gruenhagen et al., 2024; Kim et al., 2025).”
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Doctoral researchers’ Generative AI use and research-integrity concerns: a cross-context study
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Metadata
- 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
- Social Sciences › Education › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-06, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-06 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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