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Cited by 181Artificial Intelligence in EducationArtificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Generative artificial intelligence: University student awareness, experience, and confidence in use across disciplines

Open access
Kelly, Andrew, Sullivan, Miriam, Strampel, Katrina·2023·Journal of University Teaching and Learning Practice
DOI 10.53761/1.20.6.12
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Abstract

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.

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Crossref · 10.53761/1.20.6.12

Cites

45 of 45 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 25 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    At the time of the survey, most students had low confidence in using generative artificial intelligence.

    “When the survey was conducted, most students had low knowledge, experience, and confidence in using these tools.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The survey respondents are representative of the university's student population.
  • Findingown data

    Age group was not associated with having heard about GenAI (p = .443).

    “There was no difference between age groups in having heard about GenAI (p = .443).”
    Quote in the paperNot a substantial claim
  • Findingown data

    Engineering students (n = 91) had significantly higher use of generative artificial intelligence than Nursing (p = .004), Medical and Health (p = .011), and Arts (p = .003).

    “Engineering (n = 91) was also significantly higher than Nursing (p = .004),Medical and Health (p = .011)and Arts (p = .003).”
    Quote in the paperSubstantial 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.
  • Findingown data

    Students' level of study was not associated with how much they had heard about GenAI (p = .391).

    “There was no difference in how much students had heard about GenAI based on their level of study (p = .391).”
    Quote in the paperNot a substantial claim
  • Findingown data

    Participants' confidence in their ability to use generative artificial intelligence ethically increased with experience using it (p = .001, rho = .494).

    “Participants' confidence in their ability to use GenAI ethically increased with experience using GenAI (p = .001,rho = .494,Figure 2).”
    Quote in the paperSubstantial claim
    • 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.
  • May combine two claims. 24
  • The quote only partly states this claim. 1
  • Classified as probably not a substantial claim. 23

  • May combine two claims.

    Findingown data

    Engineering students had higher confidence than Nursing (p = .001), Medical and Health (p = .005), and Arts (p = .006); Science students had higher confidence than Nursing (p < .000), Medical and Health (p = .001), and Arts (p = .001).

    “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).”
    Quote in the paperSubstantial 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.

  • The quote only partly states this claim.

    Findingown data

    Differences in confidence in using artificial intelligence ethically (p = .001) were similar across disciplines.

    “Differences in confidence in using AI ethically (p = .001)were similar.”
    Quote in the paperNot a substantial 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.

  • May combine two claims.

    Findingown data

    Science students (n = 199) had heard more about generative artificial intelligence than Medical and Health Sciences (p < .000), Business and Law (n = 167, p = .001), and Arts and Humanities (n = 201, p = .005).

    “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).”
    Quote in the paperNot a substantial 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.

  • May combine two claims.

    Findingown data

    International students were equally likely to find out about generative artificial intelligence 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).

    “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).”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The survey respondents are representative of the university's student population.

  • May combine two claims.

    Findingcitation

    Most students lacked confidence with GenAI regardless of age, and age differences were smaller than differences between Schools.

    “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.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingreasoning

    International students reported lower awareness of GenAI and were less likely than domestic students to learn about it through work or the news.

    “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.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Students from different Schools had significantly different sources of information about generative artificial intelligence for all sources: 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).

    “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).”
    Quote in the paperNot a substantial 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.

  • May combine two claims.

    Findingown data

    Of the 959 respondents who had heard about generative artificial intelligence, 64% found out through social media, 41% through news media, 32% through other students, and 20% through work.

    “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%).”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The survey respondents are representative of the university's student population.

  • May combine two claims.

    Findingown data

    Use of GenAI differed slightly by level of study (p = .007): 67% of higher degree by research respondents had used it, versus 41% of postgraduates and 48% of undergraduates.

    “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).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Science students had higher use of generative artificial intelligence than Nursing (p < .000), Medical and Health (p < .000), Arts (p < .000), Business and Law (p = .004), and Education (p = .003).

    “Science was higher than Nursing (p < .000),Medical and Health (p < .000),Arts (p < .000),Business and Law (p = .004)and Education (p = .003).”
    Quote in the paperNot a substantial 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.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingown data

    Other sources of information listed by students were university staff and communications (n = 78), friends and family (n = 53), or unspecified (n = 34).

    “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).”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The survey respondents are representative of the university's student population.

  • May combine two claims.

    Findingown data

    There were no significant differences between international and domestic students in use of generative artificial intelligence (p = .08) or confidence (p = .113).

    “However, there were no significant differences in their use (p = .08)or confidence (p = .113).”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The survey respondents are representative of the university's student population.

  • May combine two claims.

    Findingown data

    The students surveyed had relatively low knowledge, experience and confidence with GenAI.

    “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.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingreasoning

    Students in science and engineering disciplines generally reported higher awareness, experience and confidence with GenAI, while healthcare disciplines reported the lowest rates.

    “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.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Students are known to overrate their abilities in several areas of digital literacy.

    “However, students are known to overrate their abilities in multiple areas of digital literacy (Smith & Storrs, 2023)”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Smith & Storrs, 2023

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    GPT-4 has passed some of the most challenging professional accreditation exams, including the Uniform Bar Exam and the Certified Public Accountant Exam.

    “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).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites Gaetano, 2023

      Not checked in the free run.

    • cites Koetsier, 2023

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Exposure to technology does not necessarily lead to understanding.

    “although it should be noted that exposure to technology does not necessarily lead to understanding (Murray & Perez, 2014).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Murray & Perez, 2014

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Generative artificial intelligence tools have weaknesses, including generating inaccurate information and fabricating references.

    “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).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites Farrokhnia et al., 2023

      Not checked in the free run.

    • cites Rudolph et al., 2023

      Not checked in the free run.

  • May combine two claims.

    Study design and conductnone given

    A conservative method of calculating significance for pairwise comparisons was used, which may underestimate differences between small groups and small differences between large groups.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductcitation

    Online surveys carry general limitations of self-selection bias and potentially limited internet access among target participants.

    “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).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Bethlehem, 2010

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning

    The capability of GPT-4 to produce image-based outputs limits the usefulness of using images to mitigate the risk of students using generative artificial intelligence to complete assessment tasks.

    “This capability limits the usefulness of using images to mitigate the risk of students using GenAI to complete assessment tasks.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    Unfinished and incomplete responses were excluded from the data analysis (n = 450).

    “Unfinished and incomplete responses were excluded from the data analysis (n = 450).”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Excluding incomplete responses did not substantially bias the results.

  • May combine two claims.

    Study design and conductown data

    Students from different disciplinary areas responded at unequal rates, which may skew results toward disciplines that responded more.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    The survey closed with 1,135 total responses.

    “The survey closed on 23 March, with 1,135 total responses gathered.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Some student views have been explored in social media posts, but sample sizes are small and do not explore different perspectives across disciplines.

    “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).”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites Haensch, 2023

      Not checked in the free run.

    • cites Tlili et al., 2023

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Concerns about generative artificial intelligence principally relate to summative written assessments such as essays and reports, and it also calls into question the integrity of online exam tasks.

    “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.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Susnjak (2022)

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    University Preparation course students were excluded from the analysis of course level.

    “The University Preparation course students were also excluded from the analysis of course level.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductnone given

    The University's human research ethics office granted ethics approval with a waiver of explicit consent (REMS 2023-04278).

    “The University's human research ethics office considered a waiver of explicit consent for the research and granted ethics approval (REMS number: 2023-04278).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductcitation

    Non-parametric statistics were used because the data were primarily nominal and ordinal.

    “Non-parametric statistics were used since the data was primarily nominal and ordinal (Pallant, 2016).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Pallant, 2016

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    The University Preparation Course (n = 37) and the School of Performing Arts (n = 28) were excluded from the discipline-based analysis due to low sample size.

    “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.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductown data

    Not all students who began the survey completed it, and those who said they knew nothing about GenAI were less likely to finish.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Premisenone given

    At the time of writing, no equivalent study explores student views and compares findings across disciplinary boundaries.

    “However, at the time of writing, there has been no equivalent study to date that explores student views and compares findings across disciplinary boundaries.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductown data

    The student sample came from a single Australian university and the study used only quantitative results.

    “the student sample was drawn from only one Australian university and focused only on the quantitative results.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    Limna et al. (2023) surveyed 15 students and found a generally positive perception of generative artificial intelligence tools, but students raised concerns about data privacy and storing personal information.

    “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.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Limna et al. (2023)

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Studies reflect the need to rethink typical university assessment tasks that could now be completed by generative artificial intelligence tools.

    “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).”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites Cotton et al, 2023

      Not checked in the free run.

    • cites Crawford et al 2023

      Not checked in the free run.

    • cites Farrokhnia et al, 2023

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Premisecitation

    Digital literacy should focus on professional disciplinary competencies rather than age stereotypes.

    “confirming Smith and Storrs' (2020) argument that digital literacy should focus on professional disciplinary competencies rather than age stereotypes.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Smith and Storrs' (2020)

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Most students are relatively passive consumers of online content and do not truly understand how online information is curated, especially on new platforms that are not transparent or accountable.

    “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”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Bhatt and Mackenzie (2019)

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    International students in Australia have reported difficulty building local social connections on arrival.

    “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.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Khanal & Gaulee, 2019

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Social norms can affect the likelihood that students cheat.

    “For instance, social norms can impact the likelihood of student cheating (Hutton, 2006)”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Hutton, 2006

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given

    Student awareness and use of GenAI is likely to vary considerably with how much their university, local news media and social circles have engaged with the technology.

    “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.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Students in communications and health science disciplines had higher digital literacy with social media because it was integrated into their professional standards curricula.

    “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.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Smith and Storrs (2023)

      Not checked in the free run.

41 of 41

  • 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”
7 of 7

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?

  • Attitudes and perceptions towards AI are not uniform across subjects, with humanities and arts students less positive than peers in STEM and related disciplines.

    “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

    The Disciplinary Area section reports that Arts and Humanities students had lower GenAI awareness than Science students, and lower use and confidence in ethical use than Science and Engineering students. The Discussion confirms disciplinary variation, but the study measures awareness, use, and confidence—not whether Arts and Humanities students have less positive attitudes towards AI. It also reports that healthcare disciplines generally had the lowest rates. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • 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.

    “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

    The International and Domestic Status section reports lower GenAI awareness among international students, supporting that part of the claim. It found no significant difference in GenAI use, so it does not support lower experience relative to domestic peers. The paper advocates embedding digital literacy in university programs, but does not specifically address EAP curricula. (LLM, conf=0.90)

    Experimental — accuracy not yet measured

  • Sufficient student support is not yet evident, and students report feeling unprepared to use GenAI.

    “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

    The Discussion and Conclusion (pages unavailable) report that most students surveyed in March 2023 had low knowledge, experience, and confidence with GenAI, which supports the characterization that they felt unprepared. The paper calls for teaching students how to use GenAI but does not establish that existing student support was insufficient. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • There are many positive applications of GenAI in HE, including supporting students in understanding complex academic concepts and providing accommodations for students with communication disabilities or those learning in a second language.

    “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

  • Kelly et al.'s survey of HE students suggests a relatively low experience of the technologies and little confidence in their applications for learning and assessment.

    “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)

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.

  • Reported low-awareness percentages do not describe most students

    Major

    The two stated percentages add to 41%, not a majority. The later subgroup figures-56% of domestic students and 72% of international students reporting similarly low awareness-also cannot produce an overall rate of 41% if those are the same categories.

  • Nursing comparison is called significant despite its stated p-value

    Minor

    The paper says the Nursing comparisons are significant but gives p > .05 for them. That exceeds the significance threshold used elsewhere in the results.

  • A p-value of .55 is described as nearly significant

    Minor

    The undergraduate comparison is described as nearly significant, but its reported p-value is .55, far from the .05 threshold used in the paper.

3 of 3

Cites

Flagged first, then by citations

Research works printed but not linked: 1

  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

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Related

Authors
Kelly, A., Sullivan, M., & Strampel, K.
Affiliations
Edith Cowan University (AU)
ORCID
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)
DOI
10.53761/1.20.6.12
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
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
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Safety Research › 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.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-06.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-06.

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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