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Students’ Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology
Open accessAbstract
AI-powered chat technology is an emerging topic worldwide, particularly in areas such as education, research, writing, publishing, and authorship. This study aims to explore the factors driving students' acceptance of ChatGPT in higher education. The study employs the unified theory of acceptance and use of technology (UTAUT2) theoretical model, with an extension of Personal innovativeness, to verify the Behavioral intention and Use behavior of ChatGPT by students. The study uses data from a sample of 503 Polish state university students. The PLS-SEM method is utilized to test the model. Results indicate that Habit has the most significant impact (0.339) on Behavioral intention, followed by Performance expectancy (0.260), and Hedonic motivation (0.187). Behavioral intention has the most significant effect (0.424) on Use behavior, followed by Habit (0.255) and Facilitating conditions (0.188). The model explains 72.8% of the Behavioral intention and 54.7% of the Use behavior variance. While the study is limited by the sample size and selection, it is expected to be a starting point for more research on ChatGPT-like technology in university education, given that this is a recently introduced technology.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This study applies an extended UTAUT2 model, adding Personal Innovativeness, to examine Polish university students’ acceptance and use of ChatGPT. It finds Habit, Performance expectancy, and Hedonic motivation as key drivers of Behavioral intention, while Behavioral intention, Habit, and Facilitating conditions drive Use behavior.
Key findings
- Habit had the strongest impact on Behavioral intention (0.339), followed by Performance expectancy (0.260) and Hedonic motivation (0.187); the model explains 72.8% of Behavioral intention variance.
- Behavioral intention had the strongest effect on Use behavior (0.424), followed by Habit (0.255) and Facilitating conditions (0.188); these explain 54.7% of Use behavior variance.
- Facilitating conditions did not significantly affect Behavioral intention (−0.023, not significant at p < .05), but did significantly affect Use behavior (0.188).
- Effort expectancy (0.079), Social influence (0.093), Price value (0.083), and Personal innovativeness (0.086) had positive but small effects on Behavioral intention, with f² below 0.02.
Methods
- The study used an extended UTAUT2 model with seven UTAUT2 constructs plus Personal innovativeness, measured with a seven-point Likert scale; Use behavior was measured on a seven-option scale from “never” to “several times a day.”
- Data came from 503 valid responses from students at the University of Economics in Katowice, Poland; 13,388 students were invited by email between 13 and 21 March 2023, and a pilot study used 36 students.
- Analysis used PLS-SEM via SmartPLS 4 with path weighting, 3000 iterations, and bootstrapping with 5000 samples; measurement model reliability, convergent validity, HTMT discriminant validity, R², and path coefficients were assessed.
Limitations
- The paper states the sample was limited to students from one state university in Poland, data were collected during a turbulent phase of AI development, academic-year timing or subject of study may affect use, and different study programs were not considered as a moderating variable.
- Evident from the text: the design is cross-sectional and single-institution, limiting generalizability; the study also intentionally did not incorporate age, gender, and experience as moderators.
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26 Own result · 11 Own method · 9 Not checked yet · 9 Own interpretation
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
“Results indicate that Habit has the most significant impact (0.339) on Behavioral intention, followed by Performance expectancy (0.260), and Hedonic motivation (0.187).”
- Also stated in: § Results · plain
“The primary observation reveals that HT has the most prominent impact (0.339) on BI, followed by PE (0.260), and HM (0.187), explaining 72.8% of the”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Abstract
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: § Results · plain
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
“SI (0.093), PI (0.086), PV (0.083), and Effort expectancy (0.079) have positive effect on Behavioral intention, however there is no f 2 effect size for these relationships.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Results
plaindirect- 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
“SI (0.093), PI (0.086), PV (0.083), and Effort expectancy (0.079) have positive effect on Behavioral intention, however there is no f 2 effect size for these relationships.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Results
plaindirect- 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
“With respect to”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Discussion
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
“Behavioral intention has the most significant effect (0.424) on Use behavior, followed by Habit (0.255) and Facilitating conditions (0.188).”
- Also stated in: § Results · plain
“Conversely, BI has the most significant effect (0.424) on UB, with HT (0.255) and FC (0.188) following suit.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Abstract
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: § Results · plain
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
“SI (0.093), PI (0.086), PV (0.083), and Effort expectancy (0.079) have positive effect on Behavioral intention, however there is no f 2 effect size for these relationships.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Results
plaindirect- 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
“In our study,”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > 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.
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 model explains 72.8% of the Behavioral intention and 54.7% of the Use behavior variance.”
- Also stated in: § Results · plain
“These three variables account for 54.7% of the”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Abstract
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.
- Also stated in: § Results · plain
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
“SI (0.093), PI (0.086), PV (0.083), and Effort expectancy (0.079) have positive effect on Behavioral intention, however there is no f 2 effect size for these relationships.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Results
plaindirect- 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
“Behavioral intention has the most significant effect (0.424) on Use behavior, followed by Habit (0.255) and Facilitating conditions (0.188).”
- Also stated in: § Results · plain
“Conversely, BI has the most significant effect (0.424) on UB, with HT (0.255) and FC (0.188) following suit.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Abstract
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: § Results · plain
- It may state two separate claims as one. 13
- Probably not a substantial claim. 18
- The quote only partly states this claim. 1
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
“SI (0.093), PI (0.086), PV (0.083), and Effort expectancy (0.079) have positive effect on Behavioral intention, however there is no f 2 effect size for these relationships.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Results
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
“The reliability and validity criteria were met for each construct, and discriminant validity was confirmed (Hair et al., 2013).”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Sample Characteristics
What the sources say
- Not checked yetcites Hair et al., 2013 source
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.
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
“After removing 3 responses with a variance of zero, 503 valid responses were collected, which is enough, according to a minimal sample size calculation.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Sample Characteristics
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: 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.
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
“To ensure unbiased data, the students were assured of the confidentiality, voluntary participation, and anonymity of their responses.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Sample Characteristics
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
“After removing 3 responses with a variance of zero, 503 valid responses were collected, which is enough, according to a minimal sample size calculation.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Sample Characteristics
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.
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
“Loadings are presented in Appendix Table 3 and exceed the lower bond except the FC4, which was removed from additional processing within the model and is not taken into account.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Results
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.
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
“All measurement instruments have met the quality criteria (Table 1).”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A 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 finding could be since students have not fully experimented with ChatGPT and may have insufficient knowledge on how to use it.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Discussion
hedgedconditional- 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 questionnaire was circulated during a turbulent phase related to AI, with each month profoundly influencing the tool's evolution.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Limitations
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: 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. It may state two separate claims as one.
In the paper
“UTAUT2 is a prominent theoretical model that aims to understand the factors that influence individuals' adoption and use of new technologies in organizational and personal contexts (Tamilmani et al., 2021).”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Methodology
What the sources say
- Not checked yetcites Tamilmani et al., 2021 source
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.
Probably not a substantial claim.
In the paper
“Additionally, Dwivedi et al. (2019) noted that previous research in this field typically utilized only a portion of the UTAUT2 model and frequently neglected moderators.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Methodology
What the sources say
- Not checked yetcites Dwivedi et al., 2019 source
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.
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
“Additionally, factors like specific times in the academic year, like assignment submissions or the specific subject of study, might affect how the tool is used.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Limitations
hedgedconditional- 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. 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
“However, the sample was diverse in terms of study experience.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Limitations
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: 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.
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
“While the study is limited by the sample size and selection, it is expected to be a starting point for more research on ChatGPT-like technology in university education, given that this is a recently introduced technology.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Abstract
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
“while others did not find this significance in areas like mobile learning (Nikolopoulou et al., 2020) and e-learning (Osei et al., 2022).”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Discussion
What the sources say
- Not checked yetcites Nikolopoulou et al., 2020
- Not checked yetcites Osei et al., 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.
In the paper
“Even as a considerable volume of studies on AI in education emerged in the past years, signifying a booming interest, the actual effects of applications like ChatGPT on higher education institutions and stakeholders remain enigmatic (Zawacki-Richter et al., 2019).”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Introduction
What the sources say
- Not checked yetcites Zawacki-Richter et al., 2019 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Venkatesh et al. (2016) recommended using UTAUT2 as a fundamental model to establish hypotheses regarding the connections between proposed variables and user adoption of technology.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Methodology
What the sources say
- Not checked yetcites Venkatesh et al., 2016 source
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.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The likelihood of adopting the AI-enabled chat is higher among early adopters who have a higher level of education and are less swayed by external factors.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Discussion
plaincorrelational- 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. 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 demonstrates that using this technology in higher education requires very little effort and does not affect”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Discussion
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: 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. 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
“It is possible that different study programs, such as IT, business, management, and finance, have different levels of AI chat use.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Limitations
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.
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
“We did not consider different study programs as a moderating variable.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Limitations
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.
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
“Although the ChatGPT plus version with the latest GPT-4 language model is available for $20 per month, the difference in the ChatGPT's use, powered by the GPT-3.5 version, for everyday student tasks is unlikely to be noticeable.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Discussion
hedged- 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.
- Rests on an assumption: ChatGPT is currently free of charge during the research preview.
In the paper
“The original study by Venkatesh et al. (2012) did not provided how the actual use was measured.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Methodology
What the sources say
- Not checked yetcites Venkatesh et al., 2012 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
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
“With respect to”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Discussion
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. The quote only partly states this claim.
The paper relies on this without support.
Research into how ChatGPT is perceived and utilized by university students remains sparse.
“Though ChatGPT has swiftly gained popularity as a cutting-edge tool in natural language processing, research into how it's perceived and utilized by university students remains sparse.”
The paper relies on this without support.
The sample of students from the University of Economics in Katowice is representative of Polish state university students.
“The study uses data from a sample of 503 Polish state university students.”
“A total of 13,388 students from the University of Economics in Katowice, Poland, were invited to participate via email in the period of 13th to 21st March 2023.”
The paper relies on this without support.
The relationships estimated by PLS-SEM represent causal effects of the predictors on Behavioral intention and Use behavior.
“Our study argues that”
The paper relies on this without support.
ChatGPT is currently free of charge during the research preview.
“This may be because the use of ChatGPT is currently free of charge during the tool's research preview.”
The paper relies on this without support.
ChatGPT doesn't have adaptive capabilities and isn't tailored for academic use.
“It's pertinent to mention that ChatGPT doesn't have adaptive capabilities and isn't tailored for academic use, indicating a need for more research on its potential role in learning environments.”
The paper relies on this without support.
ChatGPT use in higher education is not yet explored.
“Since ChatGPT use in higher education is not yet explored, there are many recommendations for future studies.”
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?
In the citing paper
“This finding suggests that university students, in particular, value ChatGPT for its utility in completing academic tasks, such as writing assistance, research, and learning new concepts [1,7,20,65].”
From ChatGPT in Learning: Assessing Students’ Use Intentions through the Lens of Perceived Value and the Influence of AI Literacy (2024) · cites this paper as [7]
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 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
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Discussion
“This demonstrates that using this technology in higher education requires very little effort and does not affect "Behavioral intention".”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Conclusions
“| 2 | EE-> BI | 0.079 | 2.061 | 0.010 | Yes |”
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
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Discussion
“"Effort expectancy", "Social influence" and "Price value" have been found to have a positive and significant effect on "Behavioral intention", despite having f 2 values that are lower than 0.02.”
§ Students' Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology > Conclusions
“| 3 | SI-> BI | 0.093 | 2.880 | 0.021 | Yes |”
Citations
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Sorted by citations
Yusuf, A., Pervin, N., & Román-González, M. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00453-6 · Cited by 554
“Consequently, there has been an outright discouragement of their usage by some academic journals and HEIs (Chan, 2023; Strzelecki, 2023; Vincent, 2023).”
BackgroundA multinational study on the factors influencing university students’ attitudes and usage of ChatGPT
Abdaljaleel, M., Barakat, M. M., Alsanafi, M., Salim, N. A., Abazid, H., Malaeb, D. N., Mohammed, A. H., Hassan, B. A. R., Wayyes, A. M., Farhan, S. S., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-52549-8 · Cited by 391
Higher education students’ perceptions of ChatGPT: A global study of early reactions
Ravšelj, D., Keržič, D., Tomaževič, N., Umek, L., Brezovar, N., A. Iahad, N., Abdulla, A. A., Akopyan, A., Aldana Segura, M. W., AlHumaid, J., et al. (2025) · PLOS ONE · doi:10.1371/journal.pone.0315011 · Cited by 376
ChatGPT awareness, acceptance, and adoption in higher education: The role of trust as a cornerstone
Shahzad, M. F., Xu, S., & Javed, I. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00478-x · Cited by 274
“Second, the direct effect of CGPTAW on CGPTAI has not been discussed in prior studies (Maheshwari, 2023; Strzelecki, 2023); thus, this study examined the awareness of ChatGPT as a novel technology and its effect on students’ ultimate intention to adopt the technology.”
BackgroundMethodInfluentialShahzad, M. F., Xu, S., & Zahid, H. (2025) · Education and Information Technologies · doi:10.1007/s10639-024-12949-9 · Cited by 204
“Past studies (Shahzad et al., 2024 ; Strzelecki, 2024 ) focused on different theo - ries, such as UTAUT and TAM models.”
Mah, D.-K., & Groß, N. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00490-1 · Cited by 218
Generative AI in education and research: A systematic mapping review
Yusuf, A., Pervin, N., Román‐González, M., & Noor, N. M. (2024) · Review of Education · doi:10.1002/rev3.3489 · Cited by 182
Schei, O. M., Møgelvang, A., & Ludvigsen, K. (2024) · Education Sciences · doi:10.3390/educsci14080922 · Cited by 166
“Similarly, Strzelecki [28] researched students’ acceptance of ChatGPT, finding that the constructs of habit and performance expectancy in UTAUT had the most impact on the behavioral intention for use.”
Νικολοπούλου, Κ. (2024) · International Journal of Changes in Education · doi:10.47852/bonviewijce42022489 · Cited by 208
Zheng, Y., Wang, Y., Liu, K. S.-X., & Jiang, M. Y. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12763-3 · Cited by 153
“However, in other studies (e.g., Habibi et al., 2023; Strzelecki, 2023b), SI was found to be only a weak predictor of BI.”
InfluentialKong, S. C., Yang, Y., & Hou, C. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100328 · Cited by 125
Al-Abdullatif, A. M., & Alsubaie, M. A. (2024) · Behavioral Sciences · doi:10.3390/bs14090845 · Cited by 126
“This technology stands out for its ability to understand and generate language-based responses, making it a valuable tool in the educational sector [7].”
Mohamed, A. M., Shaaban, T. S., Bakry, S. H., Guillén-Gámez, F. D., & Strzelecki, A. (2025) · Innovative Higher Education · doi:10.1007/s10755-024-09747-z · Cited by 115
“Moreover, it can serve as a dynamic assessor and an adaptive indicator of student performance on assignments (Ivanov & Soliman, 2023 ; Strzelecki, 2024).”
Self-citationShahzad, M. F., Xu, S., & Asif, M. (2025) · British Educational Research Journal · doi:10.1002/berj.4084 · Cited by 103
Acceptance and use of ChatGPT in the academic community
Strzelecki, A., Cicha, K., Rizun, M., & Rutecka, P. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12765-1 · Cited by 105
“Each construct in the study satisfied the reliability and validity standards, and the discriminant validity was verified, with the scale tested recently in a study of ChatGPT use among students (Strzelecki, 2024).”
Self-citationExamining faculty and student perceptions of generative AI in university courses
Kim, J., Klopfer, M., Grohs, J. R., Eldardiry, H., Weichert, J., Cox, L. A., & Pike, D. (2025) · Innovative Higher Education · doi:10.1007/s10755-024-09774-w · Cited by 109
“The survey instrument used in this study was developed based on existing studies (e.g., Amani et al., 2023; Strzelecki, 2023, 2024; Strzelecki et al., 2024; White et al., 2024) that investigated individuals’ perceptions of the use of generative AI tools in learning environments.”
InfluentialCai, L., Msafiri, M. M., & Kangwa, D. (2025) · Education and Information Technologies · doi:10.1007/s10639-024-13112-0 · Cited by 104
Al-kfairy, M. (2024) · Applied System Innovation · doi:10.3390/asi7060110 · Cited by 123
“This consistent finding underscores the need for educational technologies to clearly demonstrate their practical benefits to encourage widespread use among students and educators [5,27–30].”
InfluentialMastering knowledge: the impact of generative AI on student learning outcomes
Pallant, J. L., Blijlevens, J., Campbell, A., & Jopp, R. (2026) · Studies in Higher Education · doi:10.1080/03075079.2025.2487570 · Cited by 141
“Other studies have considered best practices for working in collaboration with GenAI, suggesting opportunities at various levels of study (Kasneci et al. 2023), as well as the acceptance of students in adopting this new technology (Strzelecki 2024).”
Sallam, M., El‐Sayed, W., Al‐Shorbagy, M. Y., Barakat, M. M., Khatib, S. E., Ghach, W., Alwan, N. H., Hallit, S. R., & Malaeb, D. N. (2024) · Frontiers in Education · doi:10.3389/feduc.2024.1414758 · Cited by 151
Iqbal, J., Hashmi, Z. F., Asghar, M. Z., & Abid, M. N. (2025) · Scientific Reports · doi:10.1038/s41598-025-01676-x · Cited by 82
Balaskas, S., Tsiantos, V., Chatzifotiou, S., & Rigou, M. (2025) · Information · doi:10.3390/info16020082 · Cited by 81
“However, the weaknesses are that cultural nuances have not been explored much, especially in studies like [36,39], which could have benefited from a deeper analysis of how sociocultural factors influence adoption.”
Bhat, M. A., Tiwari, C. K., Bhaskar, P., & Khan, S. T. (2024) · Journal of Information Communication and Ethics in Society · doi:10.1108/jices-03-2024-0033 · Cited by 97
Oc, Y., Gonsalves, C., & Quamina, L. T. (2024) · Journal of Marketing Education · doi:10.1177/02734753241302459 · Cited by 99
Annamalai, N., Bervell, B., Mireku, D. O., & Andoh, R. P. K. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100346 · Cited by 111
Acosta-Enriquez, B. G., Várgas, C. G. A. P., Jordan, O. H., Ballesteros, M. A. A., & Morales, A. E. P. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100320 · Cited by 99
Yakubu, M. N., David, N., & Abubakar, N. H. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13441-8 · Cited by 112
“Considering CG-AI applications such as ChatGPT are relatively new tools, the factors that influence new users’ perception of such technologies are not yet properly understood.”
InfluentialAbdalla, A. A., Bhat, M. A., Tiwari, C. K., Khan, S. T., & Wedajo, A. D. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100257 · Cited by 77
Hu, L., Wang, H., & Xin, Y. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13353-7 · Cited by 77
“Previous studies indicate that behav - ioral intention is the most significant factor in explaining students’ usage behavior of ChatGPT in higher education (Strzelecki, 2024; Romero-Rodríguez et al., 2024).”
InfluentialAnalyzing student prompts and their effect on ChatGPT’s performance
Sawalha, G., Taj, I., & Shoufan, A. (2024) · Cogent Education · doi:10.1080/2331186x.2024.2397200 · Cited by 93
“Similarly, Strzelecki ’ s research (Strzelecki, 2023) indicates that individual differences, including gender, moderate the acceptance of ChatGPT according to the Unified Theory of Acceptance and Use of Technology (UTAUT).”
Getenet, S. T. (2024) · International Electronic Journal of Mathematics Education · doi:10.29333/iejme/14141 · Cited by 55
“The rapid evolution of technology, such as ChatGPT, and changes in the educational environment (Wardat et al., 2023a) are compelling teachers globally at all levels to reassess their teaching and learning approaches, especially regarding the use of AI (Strzelecki, 2023).”
C., S. B. G., Bhandari, P., Gurung, S. K., Srivastava, E., Ojha, D., & Dhungana, B. R. (2024) · Cogent Education · doi:10.1080/2331186x.2024.2403287 · Cited by 78
“…the widespread influence of effort expectancy on intention to use ChatGPT in recent studies (Budhathoki et al., 2024; Duong et al., 2024; Grassini et al., 2024; Strzelecki, 2024), ease of use does not always impact intention to use (Bervell et al., 2021;; Habibi et al., 2023; Sudan et al., 2024).”
García-Alonso, E. M., León-Mejía, A. C., Sánchez-Cabrero, R., & Guzmán-Ordaz, R. (2024) · Behavioral Sciences · doi:10.3390/bs14070612 · Cited by 53
“This finding diverges somewhat from other studies, where students have displayed more favorable perceptions towards using this tool [72,75,89,96,105,109].”
ResultFaraon, M., Rönkkö, K., Milrad, M., & Tsui, E. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13492-x · Cited by 50
“Following Strzelecki (2024), this study operationalized the use of ChatGPT by adopting a seven-point scale, ranging from “never” to “several times a day” to capture the varying levels of user engagement with the AI-service.”
InfluentialDriving Factors of Generative AI Adoption in New Product Development Teams from a UTAUT Perspective
Xia, Y., & Chen, Y. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2375686 · Cited by 58
“Regarding effort expectancy, earlier studies showed it was positively associated with attitudes toward AI (Gansser & Reich, 2021; Strzelecki, 2023), but its association with behavioral intentions was inconsistent in different studies using AI for general purposes.”
InfluentialMaxwell, D., Oyarzun, B., Kim, S., & Bong, J. Y. (2025) · TechTrends · doi:10.1007/s11528-025-01109-6 · Cited by 80
“Perceived value is one of the many variables that may influence a student's acceptance and use of GenAI in higher education (Chan & Zhou, 2023; Strzelecki, 2024).”
Anani, G. E., Nyamekye, E., & Baffour-Koduah, D. (2025) · Discover Education · doi:10.1007/s44217-025-00434-5 · Cited by 72
Xu, X., & Thien, L. M. (2024) · Journal of Applied Research in Higher Education · doi:10.1108/jarhe-12-2023-0555 · Cited by 56
Wang, C., & Yin, H. (2025) · Computers & Education · doi:10.1016/j.compedu.2025.105250 · Cited by 48
Moradi, H. (2025) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-025-00530-4 · Cited by 43
“Additionally, bootstrapping was implemented to ascertain the statistical significance of the PLS-SEM estimates (Ringle et al., 2022; Strzelecki, 2024).”
InfluentialRehman, A. U., Behera, R. K., Islam, M. S., Abbasi, F. A., & Imtiaz, A. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102655 · Cited by 51
Model of AI acceptance in higher education: arguing teaching staff and students perspectives
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Social influence and information quality on Generative AI use among business students
Changalima, I. A., Amani, D., & Ismail, I. J. (2024) · The International Journal of Management Education · doi:10.1016/j.ijme.2024.101063 · Cited by 40
Nurtanto, M., Nawanksari, S., Sutrisno, V. L. P., Syahrudin, H., Kholifah, N., Rohmantoro, D., Utami, I. S., Mutohhari, F., & Hamid, M. A. (2025) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2025.101638 · Cited by 73
Ballesteros, M. A. A., Acosta-Enriquez, B. G., Farroñán, E. V. R., Juárez, H. D. G., Salinas, L. E. C., Sánchez, J. E. B., Castillo, J. C. A., Licapa-Redolfo, G. S., & Chilicaus, G. C. F. (2024) · Sustainability · doi:10.3390/su162310707 · Cited by 51
Ma, T. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100336 · Cited by 65
Kozak, J., & Fel, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-80305-5 · Cited by 43
“This practice of expanding the UTAUT model by incorporating new variables has been acknowledged in the literature, indicating its adoption by other researchers and potential to produce valuable insights 32 .”
Ayyoub, A., Khlaif, Z., Hamamra, B., Bensalem, E., Mitwally, M., Sanmugam, M., Fteiha, A., Joma, A., Bsharat, T. R. K., Eidah, B. A., et al. (2025) · Human Behavior and Emerging Technologies · doi:10.1155/hbe2/6265087 · Cited by 35
Almenara, J. C., Palacios‐Rodríguez, A., Guzmán, H. D. L. Á. R., & Scagliusi, M. V. F. (2025) · Applied Sciences · doi:10.3390/app15063363 · Cited by 49
Sallam, M., Al-Mahzoum, K., Alaraji, H., Albayati, N., Alenzei, S., AlFarhan, F., Alkandari, A., Alkhaldi, S., Alhaider, N., Al-Zubaidi, D., et al. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1542769 · Cited by 43
Börekci, C., & Çelik, Ö. (2024) · Sakarya University Journal of Education · doi:10.19126/suje.1468866 · Cited by 42
ChatGPT acceptance and use through UTAUT and TPB: A big survey in five Indonesian Universities
Habibi, A., Mukminin, A., Octavia, A., Wahyuni, S., Danibao, B. K., & Wibowo, Y. G. (2024) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2024.101136 · Cited by 58
He, A. J., Zhang, Z., Anand, P., & McMinn, S. (2024) · Journal of Asian Public Policy · doi:10.1080/17516234.2024.2447195 · Cited by 44
“Concepts related to performance expectancy, such as outcome expectations, job fit, and the like, can predict learners’ intention to use ChatGPT (Kelly et al., 2023; Menon & Shilpa, 2023; Strzelecki, 2023).”
Acosta-Enriquez, B. G., Ballesteros, M. A. A., Pérez, C. R. V., Jordan, O. H., Vergara, J. A. M., Acosta, R. M., Vargas, C. G. A. P., & Castillo, J. C. A. (2025) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2025.101274 · Cited by 41
Strzelecki, A. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-13222-9 · Cited by 46
“Wijaya et al. (2024), Grassini et al. (2024), and Yee et al. (2024) reported a nonsignificant impact of social influence, whereas Zheng et al. (2024), Strzelecki (2023), and Du and Lv (2024) found this factor to be significant.”
Self-citationShahzad, M. F., Xu, S., An, X., Zahid, H., & Asif, M. (2025) · European Journal of Education · doi:10.1111/ejed.70050 · Cited by 33
Perceptions, adoption intentions, and impacts of generative AI among Chinese university students
Zhang, R., & Wang, J. (2025) · Current Psychology · doi:10.1007/s12144-025-07928-3 · Cited by 35
Klímová, B., & Campos, V. (2024) · Cogent Education · doi:10.1080/2331186x.2024.2373512 · Cited by 47
“As other research studies (Firaina & Sulisworo, 2023; Montenegro-Rueda et al., 2023; Strzelecki, 2023) indicate, ChatGPT can thus contribute to promoting autonomous learning, curiosity, and innovative and critical thinking in students.”
InfluentialTaufiq-Hail, G. A.-M., Yusof, S. A. M., Rashid, A., El-Shekeil, I., & Lutfi, A. (2024) · Emerging Science Journal · doi:10.28991/esj-2024-08-03-02 · Cited by 28
“…the extensive literature on AI tools in education, which predominantly examines the perspectives of academic instructors, scientists, and researchers, there exists a notable gap in research regarding students' perceptions of incorporating AI tools like ChatGPT into higher education settings [10].”
BackgroundResultInfluentialUniversity Educators Perspectives on ChatGPT: A Technology Acceptance Model-Based Study
Barakat, M. M., Salim, N. A., & Sallam, M. (2025) · Open Praxis · doi:10.55982/openpraxis.17.1.718 · Cited by 50
“This finding is indicative of ChatGPT’s growing popularity as a tool in higher education as indicated by several recent studies (Abdaljaleel et al., 2024; Faruk et al., 2023; Ibrahim et al., 2023; Strzelecki, 2023; Tiwari et al., 2023; von Garrel & Mayer, 2023; Weidener & Fischer, 2024).”
Guo, S., Song, Y., Chen, G., Han, H., Wu, H. Y., & Ma, J. (2025) · Digital Health · doi:10.1177/20552076251374121 · Cited by 30
Evaluating the quality of responses generated by ChatGPT
Mandić, D., Miščević, G., & Bujišić, L. (2024) · Metodicka praksa · doi:10.5937/metpra27-51446 · Cited by 27
Géring, Z., Fehér, K., Harmat, V., & Tamássy, R. (2025) · International journal of organizational analysis · doi:10.1108/ijoa-09-2024-4850 · Cited by 46
Drivers of Chatbot Adoption among K–12 Teachers in Saudi Arabia
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Unlocking innovation: how enjoyment drives GenAI use in higher education
Cano, J. R., & Núñez, N. A. (2024) · Frontiers in Education · doi:10.3389/feduc.2024.1483853 · Cited by 39
Agyare, B. O., Asare, J., Kraishan, A., Nkrumah, I., & Adjekum, D. K. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100365 · Cited by 45
Alotaibi, H. M., Sonbul, S., & El‐Dakhs, D. A. S. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-04945-2 · Cited by 46
“These results are consistent with those of Liu and Ma (2024), Qu and Wu (2024), Strzelecki (2024), and Zou and Huang (2023).”
Adoption of AI in construction design: Insights from UTAUT2 and TOE frameworks
Katebi, A., & Tehrani, M. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.104981 · Cited by 27
Subhani, F., Khan, S. A., Sandhu, M. A., & Shahzad, M. F. (2025) · TechTrends · doi:10.1007/s11528-025-01096-8 · Cited by 26
“For ChatGPT to seamlessly integrate across multiple domains, it is important to consider these complicated aspects of pricing value (Strzelecki, 2023).”
InfluentialModeling learners' behavioral intention toward using artificial intelligence in education
Valle, N. N., Kilat, R. V., Lim, J., General, E., Cruz, J. D., Colina, S. J., Batican, I., & Valle, L. C. (2024) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2024.101167 · Cited by 30
Abdi, A.-N. M., Omar, A. M., Ahmed, M. H., & Ahmed, A. A. (2025) · Cogent Education · doi:10.1080/2331186x.2025.2460250 · Cited by 30
“Similarly, Strzelecki et al. (2024) found that HM predicts the learners ’ behavioral intention to utilize ChatGPT for educational reasons.”
InfluentialHow do pre-service language teachers perceive generative AIs’ affordance?:A case of ChatGPT
Butler, Y. G., & Jiang, S. (2025) · System · doi:10.1016/j.system.2025.103606 · Cited by 34
Navigating ChatGPT: catalyst or challenge for Indonesian youth in digital entrepreneurship?
Herani, R., & Angela, J. (2024) · Journal of Entrepreneurship in Emerging Economies · doi:10.1108/jeee-05-2024-0181 · Cited by 22
Koteczki, R., & Balassa, B. E. (2025) · Education Sciences · doi:10.3390/educsci15081044 · Cited by 39
Understanding Student Attitudes toward GenAI Tools: A Comparative Study of Serbia and Austria
Adžić, S., Tot, T. S., Vuković, V., Radanov, P., & Avakumović, J. (2024) · International Journal of Cognitive Research in Science Engineering and Education · doi:10.23947/2334-8496-2024-12-3-583-611 · Cited by 23
Generative AI as a Cognitive Co-Pilot in English Language Learning in Higher Education
Zaim, M., Arsyad, S., Waluyo, B., Ardi, H., Hafizh, M. A., Zakiyah, M., Syafitri, W., Nusi, A., & Hardiah, M. (2025) · Education Sciences · doi:10.3390/educsci15060686 · Cited by 36
“Evidence in New Zealand (Du & Alm, 2024), Sweden (Ou et al., 2024), and Poland (Belda-Medina & Calvo-Ferrer, 2022; Strzelecki, 2024) also confirms the extent of research on GenAI adoption and student attitudes within higher education.”
InfluentialLuo, Y., & Day, M. (2026) · Education and Information Technologies · doi:10.1007/s10639-026-13931-3 · Cited by 28
Abdalla, S. Z. S. (2025) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2025.101310 · Cited by 31
Bohara, D. K., & Rana, K. (2024) · SN Social Sciences · doi:10.1007/s43545-024-01003-7 · Cited by 26
“Strzelecki (2023) suggests that wise use of AI tools, such as ChatGPT, would ease the job of providing immediate feedback to students’ learning activities, creating tailored content, and designing assessments.”
Konca, A. S., Simsar, A., Alhajji, R., & Almansoori, A. G. A. M. (2025) · Interactive Learning Environments · doi:10.1080/10494820.2025.2488143 · Cited by 20
“In the context of AI in education, this implies that if AI tools are perceived as useful in improving teaching methods or learning processes, their adoption and continued use are more likely (Strzelecki, 2023; Strzelecki & ElArabawy, 2024).”
InfluentialA Systematic Review of UTAUT and UTAUT2 for AI Adoption in Education
Xue, L., Ghazali, N., & Mahat, J. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2552867 · Cited by 34
“Unlike users in other regions, mainland Chinese users lack access to international AI tools such as ChatGPT or Claude and instead rely on China-specific AI tools like DeepSeek and Doubao.”
InfluentialSaifi, S., Tanveer, S., Arwab, M., Lal, D., & Mirza, N. (2025) · Education and Information Technologies · doi:10.1007/s10639-024-13282-x · Cited by 17
“ChatGPT, created by OpenAI, was officially introduced on November 30, 2022, and has subsequently garnered significant interest (Strzelecki, 2024).”
InfluentialRashid, S. (2025) · Education Sciences · doi:10.3390/educsci15060756 · Cited by 26
Caner, S. (2025) · SAGE Open · doi:10.1177/21582440251343340 · Cited by 26
“UTAUT2 framework is particularly appropriate for academic settings, as it has been empirically validated across contexts and is adaptable to the nuanced dynamics of higher education technology use (Al Farsi, 2023; Strzelecki, 2024).”
InfluentialYu-peng, L., & Yu, Z. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-03861-1 · Cited by 23
“The extended UTAUT (UTAUT2) model was recently validated in explaining higher education students ’ ChatGPT utilization, with substantial explanatory power (Strzelecki 2024).”
Ardyansyah, A., Yuwono, A. B., Rahayu, S. L., Alsulami, N. M., & Sulistina, O. (2024) · Journal of Chemical Education · doi:10.1021/acs.jchemed.4c00220 · Cited by 26
Fuller, M., & Barnes, N. H. (2024) · New Directions for Higher Education · doi:10.1002/he.20507 · Cited by 28
Student self-reflection as a tool for managing GenAI use in large class assessment
Combrinck, C., & Loubser, N. (2025) · Discover Education · doi:10.1007/s44217-025-00461-2 · Cited by 31
Sabeh, H. N. (2024) · International Multi-Conference on Systems, Signals & Devices (SSD) · doi:10.1109/ssd61670.2024.10548826 · Cited by 16
“Nevertheless, in other studies, FC was not found to significantly impact IU [13],[23].”
BackgroundResultInfluentialDeshen, M., & Aharony, N. (2024) · Proceedings of the Association for Information Science and Technology · doi:10.1002/pra2.1128 · Cited by 22
“Various studies have implemented it within different AI industries, indicating a high correlation between the different sub-scales (Ma & Huo, 2023; Strzelecki, 2023a, 2023b).”
Adoption of chatgpt in educational institutions in Botswana: A customer perspective
Indrawati, I., Letjani, K. P., Kurniawan, K., & Muthaiyah, S. (2024) · Asia Pacific Management Review · doi:10.1016/j.apmrv.2024.100346 · Cited by 35
Jasrai, L. (2025) · The TQM Journal · doi:10.1108/tqm-12-2024-0498 · Cited by 19
Truong, V. L., & Pham, L. (2025) · Computer Applications in Engineering Education · doi:10.1002/cae.70019 · Cited by 16
Malik, I. A., Adem, S. A., & Rasool, A. (2025) · Cogent Education · doi:10.1080/2331186x.2025.2546126 · Cited by 22
A tutorial for integrating generative AI in mixed methods data analysis
Combrinck, C. (2024) · Discover Education · doi:10.1007/s44217-024-00214-7 · Cited by 32
“We are challenged to use new tools in all educational disciplines [1–4], adapt to rapidly shifting technological land-scapes, share what we have learned to support one another and engage in intellectual curiosity [5, 6].”
InfluentialHowlader, M. H., Tohan, M. M., Zaman, S., Chanda, S. K., Jiaxin, G., & Rahman, M. A. (2025) · Cogent Education · doi:10.1080/2331186x.2025.2504224 · Cited by 22
“The chatbot ’ s ability to generate human-like text and provide rapid, contextually relevant responses positions it as a groundbreaking innovation in academia, particularly in facilitating personalized learning, brainstorming, content creation, and academic support (Strzelecki, 2024; Walter, 2024).”
Parviz, M., & Arthur, F. (2025) · Human Behavior and Emerging Technologies · doi:10.1155/hbe2/5582099 · Cited by 14
Dietrich, L. K., & Grassini, S. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13658-7 · Cited by 14
“Facilitating Conditions mostly do not seem to impact the intention to use Chat - GPT (Grassini et al., 2024; Strzelecki, 2023, 2024; Strzelecki & ElArabawy, 2024).”
InfluentialKaya, M. H., & Adıgüzel, T. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1652292 · Cited by 24
Physics instructors’ acceptance and implementation of generative AI
Wattanakasiwich, P., Kaewkhong, K., & Katwibun, D. (2025) · Physical Review Physics Education Research · doi:10.1103/r2fn-kdy4 · Cited by 19
Metadata
- Authors
- Strzelecki, A.
- Affiliations
- University of Economics in Katowice (PL)
- Corresponding author
- Strzelecki, Artur
- ORCID
- Strzelecki, Artur
- Venue
- Innovative Higher Education
- Published
- 2023-11-30
- Volume
- 49
- Issue
- 2
- Pages
- 223-245
- ISSN
- 0742-5627 (ISSN-L)
- 1573-1758
- OpenAlex
- W4389203481
- Cited by
- 477
- References
- 78
- Citations by year
- 2026: 195 · 2025: 179 · 2024: 103
- FWCI
- 121.61
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Innovative Higher Education (publishedVersion, cc-by)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Keywords
- ChatGPT, technology acceptance, UTAUT2, personal innovativeness, habit, behavioral intention, use behavior, performance expectancy, hedonic motivation, facilitating conditions, university students, artificial intelligence in higher education
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.
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