The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index; none of the 25 sources its claims cite is checked for support yet.
Generative artificial intelligence: University student awareness, experience, and confidence in use across disciplines
Open accessAbstract
The global higher education sector has been significantly disrupted by the proliferation of generative artificial intelligence tools such as ChatGPT, especially in relation to its implications for assessment. However, few studies to date have explored student perspectives on these tools. This article reports on one of the first large-scale quantitative studies of student views on generative artificial intelligence at an Australian university (n = 1,135). When the survey was conducted, most students had low knowledge, experience, and confidence in using these tools. These results varied across disciplines and across some student sub-groups, such as mature-age students and international students. Confidence appeared to increase with experience, although the data also revealed a portion of students that have never used these tools yet still felt confident in using them. In exploring these results, this article aims to shed new light on this fast-evolving landscape and inform the future direction of supporting students to engage with generative artificial intelligence tools appropriately.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
35 Own result · 20 Not checked yet · 10 Own method · 7 Own interpretation · 4 No basis given
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
“When the survey was conducted, most students had low knowledge, experience, and confidence in using these tools.”
§ and confidence in use across disciplines and confidence in use across disciplines > Abstract
plainn = 1,135- 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: The survey respondents are representative of the university's student population.
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
“There was no difference between age groups in having heard about GenAI (p = .443).”
§ and confidence in use across disciplines and confidence in use across disciplines > Age
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.
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
“Engineering (n = 91) was also significantly higher than Nursing (p = .004),Medical and Health (p = .011)and Arts (p = .003).”
§ and confidence in use across disciplines and confidence in use across disciplines > Disciplinary Area
plaincorrelationaln = 91- 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: The survey respondents are representative of the university's student population.
- Rests on an assumption: The discipline categories used by the university meaningfully group students for comparison.
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
“There was no difference in how much students had heard about GenAI based on their level of study (p = .391).”
§ and confidence in use across disciplines and confidence in use across disciplines > Study Level
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.
- 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
“Participants' confidence in their ability to use GenAI ethically increased with experience using GenAI (p = .001,rho = .494,Figure 2).”
§ and confidence in use across disciplines and confidence in use across disciplines > Use and Confidence
plaindirect- 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: The survey respondents are representative of the university's student population.
- Rests on an assumption: The survey instrument validly measures students' knowledge, experience, and confidence regarding generative artificial intelligence.
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
“These results varied across disciplines and across some student sub-groups, such as mature-age students and international students.”
§ and confidence in use across disciplines and confidence in use across disciplines > Abstract
plaincorrelationaln = 1,135- 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: The survey respondents are representative of the university's student population.
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
“There were also differences between disciplines in use of GenAI (p = .001).”
§ and confidence in use across disciplines and confidence in use across disciplines > Disciplinary Area
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: The survey respondents are representative of the university's student population.
- Rests on an assumption: The discipline categories used by the university meaningfully group students for comparison.
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
“When it came to confidence, postgraduate students were more confident than undergraduates (p = .033).”
§ and confidence in use across disciplines and confidence in use across disciplines > Study Level
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.
- 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
“These results varied across disciplines and across some student sub-groups, such as mature-age students and international students.”
§ and confidence in use across disciplines and confidence in use across disciplines > Abstract
plaincorrelationaln = 1,135- 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: The survey respondents are representative of the university's student population.
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 results suggested that student confidence in using GenAI ethically increased with experience.”
- Also stated in: § Conclusion · plain
“Student confidence increased with experience, although these rates also vary across disciplines and across some student sub-groups.”
§ and confidence in use across disciplines and confidence in use across disciplines > Discussion
plaindirect- 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.
- Also stated in: § Conclusion · plain
- It may state two separate claims as one. 24
- The quote only partly states this claim. 1
- Probably not a substantial claim. 23
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
“Engineering was higher than Nursing (p = .001)Medical and Health (p = .005),and Arts (p = .006);while Science was significantly higher than Nursing (p < .000),Medical and Health (p = .001),and Arts (p = .001).”
§ and confidence in use across disciplines and confidence in use across disciplines > Disciplinary Area
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.
- Rests on an assumption: The survey respondents are representative of the university's student population.
- Rests on an assumption: The discipline categories used by the university meaningfully group students for comparison.
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
“Differences in confidence in using AI ethically (p = .001)were similar.”
§ and confidence in use across disciplines and confidence in use across disciplines > Disciplinary Area
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.
The quote only partly states this claim.
- Rests on an assumption: The survey respondents are representative of the university's student population.
- Rests on an assumption: The discipline categories used by the university meaningfully group students for comparison.
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
“Science students (n = 199) had heard more about GenAI than Medical and Health Sciences (p < .000),Business and Law (n = 167, p = .001),and Arts and Humanities (n = 201, p = .005).”
§ and confidence in use across disciplines and confidence in use across disciplines > Disciplinary Area
plaincorrelationaln = 199- 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.
It may state two separate claims as one.
- Rests on an assumption: The survey respondents are representative of the university's student population.
- Rests on an assumption: The discipline categories used by the university meaningfully group students for comparison.
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
“International students were equally likely to find out about GenAI from social media (p = .178)and other students (p = .418),but less likely to use news media (p = .001,phi = .197;39% of domestic students compared to 16% of international students) or work (p = .001,phi = .146;20% of domestic students compared to 7% of international students).”
§ and confidence in use across disciplines and confidence in use across disciplines > International and Domestic Status
plainn = 239- 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.
- Rests on an assumption: The survey respondents are representative of the university's student population.
None of the sources cited for this claim have been checked yet.
In the paper
“However, most students lacked confidence with GenAI regardless of age and the differences were not as large as those between Schools, confirming Smith and Storrs' (2020) argument that digital literacy should focus on professional disciplinary competencies rather than age stereotypes.”
§ and confidence in use across disciplines and confidence in use across disciplines > Discussion
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.
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
“International students, for example, reported lower awareness of GenAI and were less likely than domestic students to learn about GenAI through work or the news.”
§ and confidence in use across disciplines and confidence in use across disciplines > Discussion
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.
- 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
“Students from different Schools had significantly different sources of information about GenAI for all sources, such as social media (p = .001,Cramer's V = .179),news (p = .001,Cramer's V = .142),other students (p = .003,Cramer's V = .137),and work (p = .001,Cramer's V = .147).”
§ and confidence in use across disciplines and confidence in use across disciplines > Figure 3
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.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The survey respondents are representative of the university's student population.
- Rests on an assumption: The discipline categories used by the university meaningfully group students for comparison.
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
“Of the 959 respondents who had heard about GenAI, most of them (64%) had found out about it through social media, followed by news media (41%), other students (32%), and work (20%).”
§ and confidence in use across disciplines and confidence in use across disciplines > Results
plainn = 959- 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.
It may state two separate claims as one.
- Rests on an assumption: The survey respondents are representative of the university's student population.
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
“However, there were small differences in whether they had used GenAI (p = .007),with 67% of higher degree by research student respondents using GenAI (n = 53) compared to 41% of postgraduate students (n = 296, p = .007)and nearly significant compared to 48% of undergraduates (n = 746, p = .55).”
§ and confidence in use across disciplines and confidence in use across disciplines > Study Level
plaincorrelationaln = 53- 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.
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
“Science was higher than Nursing (p < .000),Medical and Health (p < .000),Arts (p < .000),Business and Law (p = .004)and Education (p = .003).”
§ and confidence in use across disciplines and confidence in use across disciplines > Disciplinary Area
plaincorrelationaln = 199- 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.
It may state two separate claims as one.
- Rests on an assumption: The survey respondents are representative of the university's student population.
- Rests on an assumption: The discipline categories used by the university meaningfully group students for comparison.
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 able to provide other sources of information, in which they mostly listed university staff and communications (n = 78), friends and family (n = 53), or unspecified (n = 34).”
§ and confidence in use across disciplines and confidence in use across disciplines > Results
plainn = 78- 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. It may state two separate claims as one.
- Rests on an assumption: The survey respondents are representative of the university's student population.
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
“However, there were no significant differences in their use (p = .08)or confidence (p = .113).”
§ and confidence in use across disciplines and confidence in use across disciplines > International and Domestic Status
plainn = 239- 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.
It may state two separate claims as one.
- Rests on an assumption: The survey respondents are representative of the university's student population.
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
“A key finding from this study was that students had relatively low knowledge, experience, and confidence with using GenAI.”
- Also stated in: § Conclusion · plain
“As this research has explored, most students surveyed in March 2023 had low knowledge, experience, and confidence with GenAI.”
§ and confidence in use across disciplines and confidence in use across disciplines > Discussion
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.
- Also stated in: § Conclusion · plain
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
“As Figure 3 highlights, students from science and engineering disciplines generally reported higher awareness, experience, and confidence in using GenAI, whereas the healthcare disciplines generally reported the lowest rates.”
§ and confidence in use across disciplines and confidence in use across disciplines > Discussion
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
“However, students are known to overrate their abilities in multiple areas of digital literacy (Smith & Storrs, 2023)”
§ and confidence in use across disciplines and confidence in use across disciplines > Discussion
What the sources say
- Not checked yetcites Smith & Storrs, 2023
boosted- 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.
None of the sources cited for this claim have been checked yet.
In the paper
“GPT-4, has already comfortably passed some of the most challenging professional accreditation exams, including the Uniform Bar Exam for legal practice and the Certified Public Accountant Exam for accounting practice (Gaetano, 2023;Koetsier, 2023).”
§ and confidence in use across disciplines and confidence in use across disciplines > Introduction
What the sources say
- Not checked yetcites Gaetano, 2023
- Not checked yetcites Koetsier, 2023
boosted- 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
“although it should be noted that exposure to technology does not necessarily lead to understanding (Murray & Perez, 2014).”
§ and confidence in use across disciplines and confidence in use across disciplines > Discussion
What the sources say
- Not checked yetcites Murray & Perez, 2014
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.
None of the sources cited for this claim have been checked yet.
In the paper
“Others have correctly pointed out that GenAI tools also have several weaknesses, including the possibility of generating inaccurate information and fabricating references (Farrokhnia et al., 2023;Rudolph et al., 2023).”
§ and confidence in use across disciplines and confidence in use across disciplines > Introduction
What the sources say
- Not checked yetcites Farrokhnia et al., 2023
- Not checked yetcites Rudolph et al., 2023
hedged- 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. 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
“The methodology used a conservative method of calculating significance for pairwise comparisons, which may mean that differences between small groups (e.g., higher degree by research students) and small differences between large groups may be underestimated.”
§ and confidence in use across disciplines and confidence in use across disciplines > Limitations and Future Research
hedged- 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.
It may state two separate claims as one.
In the paper
“Finally, the authors acknowledge the general limitations of online surveys with respect to self-selection bias and potentially limited internet access of target participants during the time in which the survey was conducted (Bethlehem, 2010).”
§ and confidence in use across disciplines and confidence in use across disciplines > Limitations and Future Research
What the sources say
- Not checked yetcites Bethlehem, 2010
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. 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
“This capability limits the usefulness of using images to mitigate the risk of students using GenAI to complete assessment tasks.”
§ and confidence in use across disciplines and confidence in use across disciplines > Literature
plaindirect- 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.
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
“Unfinished and incomplete responses were excluded from the data analysis (n = 450).”
§ and confidence in use across disciplines and confidence in use across disciplines > Data Analysis
plainn = 450- 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.
- Rests on an assumption: Excluding incomplete responses did not substantially bias the results.
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
“Similarly, students across all disciplinary areas did not respond at equal rates.This may also skew the results towards disciplinary groups that completed the survey at higher rates than those that did not.”
§ and confidence in use across disciplines and confidence in use across disciplines > Limitations and Future Research
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.
- 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
“The survey closed on 23 March, with 1,135 total responses gathered.”
§ and confidence in use across disciplines and confidence in use across disciplines > Survey Distribution
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.
None of the sources cited for this claim have been checked yet.
In the paper
“Some student views have been explored in social media posts, though the current sample sizes are reasonably small and do not explore different perspectives across disciplines (Haensch, 2023;Tlili et al., 2023).”
§ and confidence in use across disciplines and confidence in use across disciplines > Literature
What the sources say
- Not checked yetcites Haensch, 2023
- Not checked yetcites Tlili et al., 2023
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.
In the paper
“These principally relate to summative written assessments such as essays and reports, though as Susnjak (2022) pointed out, the ubiquity of GenAI tools also calls into question the integrity of online exam tasks.”
§ and confidence in use across disciplines and confidence in use across disciplines > Literature
What the sources say
- Not checked yetcites Susnjak (2022)
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. 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
“The University Preparation course students were also excluded from the analysis of course level.”
§ and confidence in use across disciplines and confidence in use across disciplines > Data Analysis
plainn = 37- 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.
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
“The University's human research ethics office considered a waiver of explicit consent for the research and granted ethics approval (REMS number: 2023-04278).”
§ and confidence in use across disciplines and confidence in use across disciplines > Method Research Context and Scope
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. It may state two separate claims as one.
In the paper
“Non-parametric statistics were used since the data was primarily nominal and ordinal (Pallant, 2016).”
§ and confidence in use across disciplines and confidence in use across disciplines > Data Analysis
What the sources say
- Not checked yetcites Pallant, 2016
plaindirect- 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.
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 the analysis of discipline-based data, the University's Preparation Course (n = 37) and the School of Performing Arts (n = 28) were excluded from the analysis due to low sample size.”
§ and confidence in use across disciplines and confidence in use across disciplines > Data Analysis
plainn = 37- 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.
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
“Not all students that started the survey completed it, and students who indicated that they knew nothing about GenAI were less likely to complete the survey.”
§ and confidence in use across disciplines and confidence in use across disciplines > Limitations and Future Research
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.
- Based: passes — The paper gives a reason why it holds.
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
“However, at the time of writing, there has been no equivalent study to date that explores student views and compares findings across disciplinary boundaries.”
§ and confidence in use across disciplines and confidence in use across disciplines > Introduction
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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
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
“the student sample was drawn from only one Australian university and focused only on the quantitative results.”
§ and confidence in use across disciplines and confidence in use across disciplines > Limitations and Future Research
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.
It may state two separate claims as one.
In the paper
“Limna et al. (2023) also surveyed a small number of students (n = 15) and found a generally positive perception of GenAI tools, yet students also raised concerns about data privacy and storing of personal information.”
§ and confidence in use across disciplines and confidence in use across disciplines > Literature
What the sources say
- Not checked yetcites Limna et al. (2023)
plainn = 15- 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. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Studies to date reflect the need to rethink the typical types of university assessment tasks which could now easily be completed by GenAI tools (Cotton et al, 2023, Crawford et al 2023;Farrokhnia et al, 2023;Perkins 2023;Rudolph et al, 2023).”
§ and confidence in use across disciplines and confidence in use across disciplines > Literature
What the sources say
- Not checked yetcites Cotton et al, 2023
- Not checked yetcites Crawford et al 2023
- Not checked yetcites Farrokhnia et al, 2023
boosted- 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
“confirming Smith and Storrs' (2020) argument that digital literacy should focus on professional disciplinary competencies rather than age stereotypes.”
§ and confidence in use across disciplines and confidence in use across disciplines > Discussion
What the sources say
- Not checked yetcites Smith and Storrs' (2020)
boosted- 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
“Bhatt and Mackenzie (2019) argue that most students are relatively passive consumers of online content, and do not truly understand how online information is curated, especially in new platforms that are not transparent or”
§ and confidence in use across disciplines and confidence in use across disciplines > Discussion
What the sources say
- Not checked yetcites Bhatt and Mackenzie (2019)
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. It may state two separate claims as one.
In the paper
“For instance, international students in Australia have reported challenges in building local social connections upon arrival (Khanal & Gaulee, 2019), which may limit the possibilities to learn about GenAI through friends or student peers.”
§ and confidence in use across disciplines and confidence in use across disciplines > Discussion
What the sources say
- Not checked yetcites Khanal & Gaulee, 2019
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
“For instance, social norms can impact the likelihood of student cheating (Hutton, 2006)”
§ and confidence in use across disciplines and confidence in use across disciplines > Discussion
What the sources say
- Not checked yetcites Hutton, 2006
plainconditional- 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.
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
“Student awareness and use of GenAI is likely to vary considerably depending on how much their university, local news media, and social circles have engaged with the technology.”
§ and confidence in use across disciplines and confidence in use across disciplines > Limitations and Future Research
hedgedcorrelational- 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: fails — The paper may give no reason why it holds.
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
“This is similar to a study by Smith and Storrs (2023), which found that students in communications and health science disciplines had higher digital literacy when using social media because it was integrated into their professional standards curricula.”
§ and confidence in use across disciplines and confidence in use across disciplines > Discussion
What the sources say
- Not checked yetcites Smith and Storrs (2023)
plaindirect- 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.
The paper relies on this without support.
The release of generative artificial intelligence tools in late 2022 and early 2023 caused major disruption to university teaching and learning worldwide.
“The open-access release of generative artificial intelligence (GenAI) tools in late 2022 and early 2023 prompted a major disruption to the teaching and learning practices of universities worldwide.”
The paper relies on this without support.
The survey respondents are representative of the university's student population.
“The survey was sent to all students to understand their awareness, perceptions, and use of GenAI.”
The paper relies on this without support.
The survey instrument validly measures students' knowledge, experience, and confidence regarding generative artificial intelligence.
“The tenitem survey was designed with a mix of closed-ended questions, open-ended questions, and Likert questions clustered into five categories: demographics; knowledge, experience, confidence, and perceptions about GenAI.”
The paper relies on this without support.
Excluding incomplete responses did not substantially bias the results.
“Unfinished and incomplete responses were excluded from the data analysis (n = 450).”
The paper relies on this without support.
The discipline categories used by the university meaningfully group students for comparison.
“These have been grouped into each of the University's Schools.”
The paper relies on this without support.
There is a lack of research into student perspectives on GenAI to date.
“the lack of research into student perspectives of these tools to date limits the conclusions that can be drawn about how students will engage in practice.”
The paper relies on this without support.
This study is one of the first published to explore student perspectives on GenAI.
“While this study is one of the first published that explores student perspectives on GenAI”
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
“Despite the apparent generalisability of these sentiments, cross-disciplinary research cautions that attitudes and perceptions towards AI are not uniform across subjects, with humanities and arts students less positive than peers in STEM and related disciplines (Irfan et al., 2023; Kelly et al., 2023).”
From Artificial Intelligence for Academic Purposes (AIAP): Integrating AI literacy into an EAP module (2025) · cites this paper as Kelly et al., 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“This may be particularly relevant within EAP contexts; research from Australia cautions that international students may have less familiarity, awareness, and experience of GenAI than their domestic peers, highlighting the urgency of integrating AI literacy within EAP curricula to ensure a base equality of opportunity (Kelly et al., 2023).”
From Artificial Intelligence for Academic Purposes (AIAP): Integrating AI literacy into an EAP module (2025) · cites this paper as Kelly et al., 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Further, sufficient student support is not yet evident, and students report feeling unprepared to use GenAI (Freeman, 2024; Kelly et al., 2023).”
From The AI Assessment Scale (AIAS) in action: A pilot implementation of GenAI supported assessment (2024) · cites this paper as Kelly et al., 2023
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“There are many positive applications of GenAI in HE, including its potential role in supporting students in understanding complex academic concepts and providing accommodations for students with communication disabilities (Kelly et al., 2023) or those learning in a second language.”
From The Artificial Intelligence Assessment Scale (AIAS): A Framework for Ethical Integration of Generative AI in Educational Assessment (2024) · cites this paper as Kelly et al., 2023
The cited source says what the claim attributes to it.
In the citing paper
“In one of the few studies in this area, Kelly et al.'s (2023) survey of HE students suggests a relatively low experience of the technologies and little confidence in their applications for learning and assessment.”
From The Artificial Intelligence Assessment Scale (AIAS): A Framework for Ethical Integration of Generative AI in Educational Assessment (2024) · cites this paper as Kelly et al.'s (2023)
The cited source says what the claim attributes to it.
In the citing paper
“Studies have suggested that more experience in hands-on use of GenAI leads to an increase in con fi dence in its ethical and appropriate utilisation (Kelly et al., 2023), and that fostering ethical awareness can positively impact student use of GenAI (Zhu et al., 2024).”
From Artificial Intelligence for Academic Purposes (AIAP): Integrating AI literacy into an EAP module (2025) · cites this paper as Kelly et al., 2023
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
§ and confidence in use across disciplines and confidence in use across disciplines > Results
“At the time of administering the survey, most students had either heard nothing (13%) or very little (28%) about ChatGPT and other GenAI tools.”
§ and confidence in use across disciplines and confidence in use across disciplines > International and Domestic Status
“with 72% of international students saying that they had heard "nothing" or "a little" about GenAI compared to 56% of domestic students.”
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
§ and confidence in use across disciplines and confidence in use across disciplines > Disciplinary Area
“Nursing students (n = 113) had heard about GenAI significantly less than students in all other disciplinary areas”
§ and confidence in use across disciplines and confidence in use across disciplines > Disciplinary Area
“(p > .05)except Medical and Health Sciences (n = 121, p = .347).”
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
§ and confidence in use across disciplines and confidence in use across disciplines > Study Level
“and nearly significant compared to 48% of undergraduates”
§ and confidence in use across disciplines and confidence in use across disciplines > Study Level
“(n = 746, p = .55).”
Citations
Cites
Digital Natives, Digital Immigrants Part 1
Prensky, M. (2001) · On the Horizon · doi:10.1108/10748120110424816 · Cited by 11,104
ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · doi:10.35542/osf.io/5er8f · Cited by 7,552
ChatGPT for good? On opportunities and challenges of large language models for education
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · Learning and Individual Differences · doi:10.1016/j.lindif.2023.102274 · Cited by 7,326 · cited 1×
“One recent study has even begun to consider the use of GenAI in marking student work (Kasneci et al 2023), though such use needs to be handled cautiously given student privacy implications and the propensity of these tools to generate incorrect information.”
Dwivedi, 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
Chatting and cheating: Ensuring academic integrity in the era of ChatGPT
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024) · Innovations in Education and Teaching International · doi:10.1080/14703297.2023.2190148 · Cited by 3,087
Chatting and Cheating. Ensuring academic integrity in the era of ChatGPT
Cotton, D., Cotton, P., & Shipway, J. R. (2023) · doi:10.35542/osf.io/mrz8h · Cited by 3,079
SPSS survival manual: a step by step guide to data analysis using IBM SPSS
(2013) · Australian and New Zealand Journal of Public Health · doi:10.1111/1753-6405.12166 · Cited by 2,739
Exploring the impact of artificial intelligence on teaching and learning in higher education
Popenici, S. A. D., & Kerr, S. (2017) · Research and Practice in Technology Enhanced Learning · doi:10.1186/s41039-017-0062-8 · Cited by 2,019
Artificial intelligence in higher education: the state of the field
Crompton, H., & Burke, D. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00392-8 · Cited by 2,010
What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023) · Smart Learning Environments · doi:10.1186/s40561-023-00237-x · Cited by 2,003 · cited 2×
“There are some studies that have begun to explore social media data on GenAI (Haensch, 2023; Tlili et al 2023), and the ways in which these tools are socialized in social media platforms will be important to investigate further.”
Research works printed but not linked: 1
- Butarbutar, R., Sauhenda, A. F., Marnina, H. R., & Wahyuniar, S. M. R. L. (2021). Challenges and opportunities of accelerated digital literacy during the COVID-19 pandemic. Hong Kong Journal of Social Sciences, 57, 160-168. http://hkjoss.com/index.php/journal/article/view/424/420
Cited by
Sorted by citations
Items 101–182 of 182
Benavides, A. M., Tineo, G. E. I., & Olivos, A. V. (2023) · Revista Internacional de Ciencias Sociales · doi:10.57188/ricso.2023.012 · Cited by 3
Çankaya, G. Z. E., Kurgun, H., Aktaş, E., & Kurgun, A. (2024) · Uluslararası Sosyal ve Ekonomik Çalışmalar Dergisi · doi:10.62001/gsijses.1480930 · Cited by 5
Mwaniki, S., Araka, E., Kituku, B., & Maina, E. M. (2025) · IST-Africa Conference (IST-Africa) · doi:10.23919/ist-africa67297.2025.11060553 · Cited by 2
Generative AI in Multicultural Classrooms
Panmei, B. (2025) · International Journal of Multicultural Education · doi:10.18251/ijme.v27i2.5165 · Cited by 3
Rico, A., Palacios-Agúndez, I., & Gonzalez-Larrañaga, G. (2025) · Research in Science Education · doi:10.1007/s11165-025-10286-6 · Cited by 2
Filipova, A. G., Abrosimova, E. E., & Abdiraiymova, G. (2025) · Ойкумена. Регионоведческие исследования · doi:10.63973/1998-6785/2025-3/40-51 · Cited by 3
Analyzing the Influence of Generative AI on B2B Marketing and Business Development
Tiwari, D., Wankhede, H., & Tiwari, M. (2025) · IEEE International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET) · doi:10.1109/acroset66531.2025.11281179 · Cited by 2
Mrowka, S., Beardsley, A. K., Sobo, E. J., & Goldberg, D. M. (2025) · Anthropological Forum · doi:10.1080/00664677.2025.2596669 · Cited by 2
Abolle-Okoyeagu, C. J., Ezenkwu, C. P., Ibeke, E., Onoja, O. P., & Ezeonwumelu, V. U. (2026) · Cogent Education · doi:10.1080/2331186x.2026.2614148 · Cited by 5
Bozkurt, R., & Özdoğan, M. C. (2026) · Journal of Nursing Management · doi:10.1155/jonm/7221445 · Cited by 2
Yuqian, L., Rui, L., Panpan, D., & Fu, W. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2026.2648733 · Cited by 4
Li, X., Xian, X., & Du, P. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1903784 · Cited by 3
Twenty Years of Disability Research: A Systematic Review
Szucs, B., Jones, S. C., & Harpur, P. D. (2024) · Journal of University Teaching and Learning Practice · doi:10.53761/d6cd0h14 · Cited by 2
Pesce, M., & Blanco, D. F. (2024) · European Journal of Education Studies · doi:10.46827/ejes.v11i12.5687 · Cited by 4
Students’ Attitude towards the Use of AI in Higher Education
Gorgorova, M., Gaftandzhieva, S. N., Yotov, K., & Hadzhikolev, E. N. (2025) · International Symposium INFOTEH-JAHORINA (INFOTEH) · doi:10.1109/infoteh64129.2025.10959172 · Cited by 2
Exploring Students' Familiarity, Usage Patterns, and Perceptions of Generative AI Tools in Education
Epaminonda, E., Ktorifou, D., Michailidis, M. P., & Efthymiou, L. (2025) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon62633.2025.11016320 · Cited by 2
Partnering to enhance the postgraduate student experience
Maldon, J., & Pedlow, M. (2023) · ASCILITE Publications · doi:10.14742/apubs.2023.644 · Cited by 2
Generative AI in Higher Education: Evidence from an Elite College
Reyes, G., & Contractor, Z. (2025) · doi:10.2139/ssrn.5375804 · Cited by 3
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
Sobo, E. J., Goldberg, D. M., Hauze, S. W., & Frazee, J. P. (2026) · Annals of Anthropological Practice · doi:10.1111/napa.70029 · Cited by 1
El-Saman, A. H. (2025) · Media Practice and Education · doi:10.1080/25741136.2025.2475045 · Cited by 1
Pablo, K. D., Cadorna, K. G., Cabanag, R., Berganio, A. L., Roberto, R. P., Guieb, J. M., Barbieto, E., Dapiawen, K. J., & Peña, L. V. C. D. (2025) · Psychology and Education A Multidisciplinary Journal · doi:10.70838/pemj.350304 · Cited by 1
Analyzing the Drivers of Adoption in Higher Education E-Learning
Srivastava, K., Kumar, M., Verma, R., Singh, S., & Maurya, P. (2025) · Advances in web technologies and engineering book series · doi:10.4018/979-8-3693-2973-3.ch003 · Cited by 1
MacLennan, H., Malka, S. C., & Queiroz, H. J. D. S. D. (2025) · SAM Advanced Management Journal · doi:10.1108/samamj-04-2025-0023 · Cited by 2
Maphosa, M., Khoza, L. T., Tlomatsana, C., & Pitjo, W. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-11733-5_27 · Cited by 3
Alvarado, A., & Portillo, J. R. (2025) · World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC) · doi:10.1109/weef-gedc66748.2025.11256341 · Cited by 1
Augustine, S. E., & Owate, C. N. (2026) · Discover Education · doi:10.1007/s44217-026-01195-5 · Cited by 2
Preparing Graduates for the Impact of Generative Artificial Intelligence in Future Work
Dean, B. A., Plumb, M., & Rook, L. (2026) · Springer eBooks · doi:10.1007/978-981-95-1875-3_4 · Cited by 1
AI in education through the learners’ eyes: practical experience, perceptions, and challenges
Yotov, K., Gaftandzhieva, S. N., Hadzhikolev, E. N., Hadzhikoleva, S., & Gorgorova, M. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1717886 · Cited by 4
Parada, E. J. V. (2026) · Revista Internacional de Pedagogía e Innovación Educativa · doi:10.51660/ripie61360 · Cited by 1
Nyagoga, L. M., Malik, L., Lukoko, H. O., & Liu, M. (2026) · Artificial Intelligence in Education · doi:10.1108/aiie-02-2025-0029 · Cited by 2
Moody-Marshall, R., Mack, A. J., Stephens, A., McCaig, M., & Morrison, B. (2026) · Caribbean Educational Research Journal · doi:10.1108/cerj-03-2025-0005 · Cited by 1
The effect of graphic organizers on cultivating higher-order thinking: Evidence from a meta-analysis
Lei, H., Ding, Y., & Chiu, M. M. (2026) · Educational Research Review · doi:10.1016/j.edurev.2026.100822 · Cited by 2
Apata, O. E., Kwok, O., & Ajose, S. T. (2026) · Journal of Computer Assisted Learning · doi:10.1002/jcal.70309 · Cited by 3
Australian Higher Education in Perpetual Crisis? A Narrative of Crisis Leadership in Universities
Crawford, J., Allen, K., & Cowling, M. (2024) · Springer eBooks · doi:10.1007/978-3-031-54509-2_11 · Cited by 2
Alqarni, A. (2025) · International Journal of Educational Sciences and Arts · doi:10.59992/ijesa.2025.v4n4p17
Empowering Students From Assumptions to Knowledge
Gribble, L. C., & Wardrop, J. (2024) · Advances in educational marketing, administration, and leadership book series · doi:10.4018/979-8-3693-0240-8.ch014
Artificial Intelligence for Academic Purposes (Aiap): Integrating Ai Literacy into an Eap ModulePreprint
Smith, D. L., & Ngo, T. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4891061
Chat GPT Wrote it: What HCI Educators Can Learn from their Students?
Sikorski, M. (2024) · Proceedings of the International Conference on Information Systems Development · doi:10.62036/isd.2024.25
Assessment Process in the Light of GenAI: Prohibiting Vs Endorsing
Barmada, B., Chitalia, A., & Kabbar, E. F. (2024) · IEEE International Conference on Teaching, Assessment, and Learning for Engineering · doi:10.1109/tale62452.2024.10834386
Malka, S. C., MacLennan, H., & Queiroz, H. D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5108051
Alexeeva-Alexeev, I., & Comesaña, P. C. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5162897
Gunawardana, H. M. R. S. S. (2025) · Springer eBooks · doi:10.1007/978-981-96-6001-8_7
Malka, S. C., MacLennan, H., & Queiroz, H. J. D. S. D. (2025) · Journal of Higher Education Theory and Practice · doi:10.33423/jhetp.v25i2.7669 · Cited by 1
Transforming English Language Learning in South Asia
Indapurkar, K., & Arya, A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5092-9.ch013
ChatGPT in the context of competence-based education in Tanzania higher learning institutions
Chandafa, M. J., & Fang, H. (2025) · The Education and science journal · doi:10.17853/1994-5639-2025-9551 · Cited by 1
Universidad Politécnica Salesiana, E., Llerena-Izquierdo, J., Ayala-Carabajo, R., & Universidad Politécnica Salesiana, E. (2025) · doi:10.17163/abyaups.141.4 · Cited by 1
Revisión bibliométrica sobre la adopción de inteligencia artificial en la educación superior
Jiménez, M. U., López-Mallama, O. M., & Espinosa, A. A. (2025) · Revista Guillermo de Ockham · doi:10.21500/22563202.7780
Bowen, T., Maddalena, K., & Whithaus, C. (2025) · Writing & Pedagogy · doi:10.3138/wap-2025-0004
Use of Generative AI Tools in Software Quality Assurance Work
Maxim, B. R., Song, Z., de Hurtado, B. G., Hassan, F., & Shijo, S. (2025) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie63693.2025.11328290
Using Generative AI at the University
Chatzopoulos, A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9074-1.ch005
The normalization of unease: the case study of GenAI use by iSchool students
Lopatovska, I., Mack, C., Connors, E., Zhu, P., Keller, N., Kaur, S., Koonamparampath, M., Everett, M., Zapata, Z., Giordano, P., et al. (2026) · Information and Learning Sciences · doi:10.1108/ils-08-2025-0125 · Cited by 1
How Has ChatGPT Been Positioned as a Threat to Academic Integrity? A Scoping Review
Liu, Q., Gladman, T., Gallagher, S., & Hu, A. (2026) · Springer eBooks · doi:10.1007/978-981-95-1875-3_3 · Cited by 2
Analyzing Teacher and Student Awareness of Artificial Intelligence at Schools in Kyrgyzstan
Beishenaliev, A., & Eminova, A. (2026) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-95-4423-3_45
Analyzing the Influence of Generative AI on B2B Marketing and Business Development
Veigas, K. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-13555-1_12
Gordon, C. K., Browne, L. D., Sweet, T. G., Zemke-Smith, C., Blaauw-Smith, F. J., Chan, S. V., Waterland, M., & Davis, N. J. L. K. (2026) · Discover Education · doi:10.1007/s44217-026-01479-w
Human-AI Collaboration in Entrepreneurship
Pareek, K., Farooqi, A., & Goyal, N. K. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-1353-3.ch008
Tashtoush, M. A., & Khasawneh, M. A. (2026) · International Journal of ADVANCED AND APPLIED SCIENCES · doi:10.21833/ijaas.2026.04.022
Yao, J., Al-Hashimy, H. N. H., & Kasuma, S. A. A. (2026) · Education + Training · doi:10.1108/et-12-2025-1131
Li, D., & Li, H. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2026.2684742
Lord, E., Heboyan, V., Tracy, J. D., Huang, H., & Leo, G. D. (2026) · doi:10.2196/preprints.104521
Enhancing Ethical Generative AI Literacy in Early Learners Through Educational Intervention
Tariq, R., & Masood, A. (2026) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon67543.2026.11574498
Edam-Agbor, I. B., Orim, F. S., Ovat, S. V., Ofem, U. J., Ukatu, J. O., Ekpang, P. O., Igajah, M. N., Ebaye, A. S., & Asukwuo, P. (2026) · Cogent Education · doi:10.1080/2331186x.2026.2690902
Development and Validation of AI Acceptance Scale (AI-AS) for Learners in Higher Education
Kayalı, B., Yavuz, M., Üstün, A. G., Uçar, H., Erdoğdu, E., & Bozkurt, A. (2026) · TechTrends · doi:10.1007/s11528-026-01209-x
Luchkevych, M., & Luchkevych, V. (2026) · Acta Paedagogica Vilnensia · doi:10.15388/actpaed.2026.56.15
Singh, R. G., Chow, K. M., Dong, H., Wong, H., Fung, M., Myers, P. S., Wong, L. L. C., Lai, P., Ngai, C. S. B., Koon, A. C., et al. (2026) · Journal of English for Academic Purposes · doi:10.1016/j.jeap.2026.101729 · Cited by 2
Noor, A. A. M., Hussein, S. S., Lokman, A. M., Yahya, F., & Bakar, N. A. A. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-34189-1_26
Incoming Engineering Students’ GenAI Literacy: Differences Across Experience Levels
Das, U. K., Thompson, A. K., & Bego, C. R. (2026) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--59963
Maxim, B. R., Ghammam, A., Roy, M., Gandi, S., & Gujarathi, A. (2026) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--60806
Artificial Intelligence in International Students' Learning: A Bibliometric Analysis
Wongpinpech, P. (2026) · International Conference on Distance Education and Learning (ICDEL) · doi:10.1109/icdel70250.2026.11663682
From AI Literacy to Epistemic Agency
Schnatz, N. I., & Bieri, A. (2026) · MedienPädagogik Zeitschrift für Theorie und Praxis der Medienbildung · doi:10.21240/mpaed/71/2026.08.20.x
Mirsanjari, Z. (2026) · Language Testing in Asia · doi:10.1186/s40468-026-00475-z
Adelana, O. P., Ebubedike, M., Crabb, E. M., & Rienties, B. (2026) · Computers and Education Open · doi:10.1016/j.caeo.2026.100423
Sing O Muse: a restricted language model for GenAI in classics teaching
Riebling, E., & Riebling, F. (2026) · The journal of classics teaching · doi:10.1017/s2058631026101226
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
Tan, W. H. E., Yeo, M. M. L., Toh, A. L. L., & Wansing, E. S. (2026) · Pedagogies An International Journal · doi:10.1080/1554480x.2026.2733074
Psychological Factors of Artificial Intelligence Literacy of Arab Higher Education Students
Okela, A. H., Mansoor, H. M. H., Shamseldien, F. M., & Abdelkareem, A. N. Y. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9190-8.ch015
Tan, S. F., Chan, J. I. L., Chew, C. M., & Eak, A. D. (2026) · ASEAN Journal of Open and Distance Learning · doi:10.64233/xzya2444
Al-Kaabi, A., & Qutran, Y. A. (2026) · Discover Education · doi:10.1007/s44217-026-02204-3
Greinke, L., Lange, L., & Richter, J. (2026) · World sustainability series · doi:10.1007/978-3-032-34574-5_26
Designing for AI Ethics Pedagogy Through Multiliteracies Framework
Avcı, D., Durgut, G., & Leila, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-0373-2.ch006
Generative AI acceptance among students in LIS programs in Türkiye
Öztürk, H. (2026) · Reference Services Review · doi:10.1108/rsr-04-2026-0070
Metadata
- Authors
- Kelly, A., Sullivan, M., & Strampel, K.
- Affiliations
- Edith Cowan University (AU)
- Venue
- Journal of University Teaching and Learning Practice
- Publisher
- Open Access Publishing Association
- Published
- 2023-08-03
- Volume
- 20
- Issue
- 6
- ISSN
- 1449-9789 (ISSN-L)
- OpenAlex
- W4385953071
- Semantic Scholar
- 3e359c062efb95c1c323989a83d590e5ba482820
- Cited by
- 177
- References
- 43
- Citations by year
- 2026: 51 · 2025: 73 · 2024: 49 · 2023: 4
- FWCI
- 41.54
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- other-oa
- Locations
- Journal of University Teaching and Learning Practice (publishedVersion, other-oa)
- Australasian Journal of Paramedicine (submittedVersion, cc-by-nd)
- Research Online (University of Wollongong) (submittedVersion)
- Funders
- Deakin University
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- generative AI, ChatGPT, student awareness, student self-confidence, technology experience, university students, mature age students, international students, disciplinary differences, student perceptions, AI-assisted assessment
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 ›
Are you an author, or do you see a problem with this page? Request a review
