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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
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · cited 1×
“However, a marked improvement in AI’s capabilities, particularly in the realm of generative AI, became apparent in last decade (Adiwardana et al., 2020; Vaswani et al., 2017).”
The theory of planned behavior
Ajzen, I. (1991) · Organizational Behavior and Human Decision Processes · doi:10.1016/0749-5978(91)90020-t · Cited by 90,587
User Acceptance of Information Technology: Toward A Unified View1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003) · MIS Quarterly · doi:10.2307/30036540 · Cited by 45,035 · cited 6×
“One of the most prominent theories used to explain and predict technology acceptance is the “Unified Theory of Acceptance and Use of Technology” (UTAUT) (Venkatesh et al., 2003).”
InfluentialA new criterion for assessing discriminant validity in variance-based structural equation modeling
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-014-0403-8 · Cited by 38,459 · cited 2×
“If they are more distinct, a limit of 0.85 should be used (Henseler et al., 2015).”
Influential(1975) · PhilPapers (PhilPapers Foundation) · Cited by 36,737
Pls-Sem: indeed a silver bullet
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011) · Journal of Marketing Theory and Practice · doi:10.2753/mtp1069-6679190202 · Cited by 21,631 · cited 1×
“According to Hair et al. (2011), R 2 values can generally be interpreted as weak (0.25), moderate (0.50), or substantial (0.75).”
InfluentialA primer on partial least squares structural equation modeling (PLS-SEM)
Leguina, A. (2015) · International Journal of Research & Method in Education · doi:10.1080/1743727x.2015.1005806 · Cited by 18,921
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012) · MIS Quarterly · doi:10.2307/41410412 · Cited by 15,756 · cited 10×
“The original study by Venkatesh et al. (2012) did not provided how the actual use was measured.”
InfluentialChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · doi:10.35542/osf.io/5er8f · Cited by 7,552
ChatGPT for good? On opportunities and challenges of large language models for education
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · Learning and Individual Differences · doi:10.1016/j.lindif.2023.102274 · Cited by 7,326
Research works printed but not linked: 3
- Adiwardana, D., Luong, M.-T., So, D. R., Hall, J., Fiedel, N., Thoppilan, R., Yang, Z., Kulshreshtha, A., Nemade, G., Lu, Y., & Le, Q. V. (2020). Towards a human-like open-domain chatbot. http://arxiv.org/abs/2001.09977 · arXiv
- Ringle, C. M., Wende, S., & Becker, J.-M. (2022). SmartPLS 4. SmartPLS GmbH.
- Rudolph, J., Tan, S., & Tan, S. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education? Journal of Applied Learning & Teaching, 6(1), 1–22.
Cited by
Sorted by citations
Items 101–200 of 473
Bhuiyan, M. R. I., Uddin, S., Sultana, T., Amin, A. A. A., & Islam, M. M. (2026) · SAGE Open · doi:10.1177/21582440251413008 · Cited by 17
“The final structured questionnaire was produced based on the results of the pilot testing and the expert advice relevant to the study topic (Strzelecki, 2024).”
Assessing computer science student attitudes towards AI ethics and policy
Weichert, J., Kim, D., Zhu, Q., Kim, J., & Eldardiry, H. (2025) · AI and Ethics · doi:10.1007/s43681-025-00817-2 · Cited by 17
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) · Research Square · doi:10.21203/rs.3.rs-3905717/v1 · Cited by 13
Caffaratti, L. B., Longobardi, C., Badenes‐Ribera, L., & Marengo, D. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1614993 · Cited by 22
Assessing ChatGPT Adoption in Higher Education: An Empirical Analysis
Dorobăț, I., & Corbea, A. M. I. (2025) · Electronics · doi:10.3390/electronics14234739 · Cited by 16
Liu, X., Zhang, H., & Liu, L. (2024) · Frontiers in Education · doi:10.3389/feduc.2024.1346467 · Cited by 14
Okela, A. H. (2024) · Journalism & Mass Communication Educator · doi:10.1177/10776958241296502 · Cited by 14
“…(Venkatesh et al., 2003); studies have demonstrated that effort expectancy is a crucial factor in predicting technology acceptance and directly influences individuals’ intention to use technology (Strzelecki, 2023); and impacts teachers’ readiness to use generative AI (Zhang & Wareewanich, 2024).”
MARKETING STUDENTS’ PERCEPTIONS TOWARDS ChatGPT: AN AI-ASSISTED THEMATIC ANALYSIS
Hazari, S. (2025) · Marketing Education Review · doi:10.1080/10528008.2025.2470198 · Cited by 17
“Strzelecki (2023) found students comfortable with AI adoption, though results are preliminary due to AI in education’s newness.”
Acceptance of AI applications among teachers and student teachers
Brandhofer, G., & Tengler, K. (2025) · Discover Education · doi:10.1007/s44217-025-00637-w · Cited by 18
“For example, the Unified Theory of Acceptance and Use of Technology (UTAUT) represents an attempt to empirically compare eight of the most prominent models, including the Theory of Planned Behavior and the TAM, and to form a coherent, unified theory from them [30].”
Understanding of ChatGPT adoption among business school students
Al‐Okaily, M., Mater, W., Matar, N. A., & Shiyyab, F. S. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100441 · Cited by 12
Yang, B., Sun, Y., Zeng, Z., & Li, Q. (2025) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2025.103002 · Cited by 11
Tbaishat, D. M., Alfandi, O., Hamad, F. F., Bukhari, S. M. S., & AlMuhaissen, S. A. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100541 · Cited by 19
Kétyi, A., Géring, Z., & Dén‐Nagy, I. (2024) · Society and Economy · doi:10.1556/204.2024.00007 · Cited by 11
Mandai, K., Tan, M. J. H., Padhi, S., & Pang, K. T. (2024) · Education Sciences · doi:10.3390/educsci14060614 · Cited by 11
“Reiterating the threats of ChatGPT to student learning, it is essential that students are taught how to use them appropriately and what to use them for, not least because “behavioral intention” and “habit” are among the leading factors for why students turn to ChatGPT [39,40].”
BackgroundOnal, S., Kulavuz‐Onal, D., & Childers, M. (2025) · Journal of Educational Technology Systems · doi:10.1177/00472395251341214 · Cited by 17
“Similarly, Strzelecki (2024) found that among Polish HE students, habit was the most signi fi cant predictor of BI, followed closely by performance expectancy and hedonic motivation.”
InfluentialUnraveling the influential factors driving persistent adoption of ChatGPT in learning environments
Moghavvemi, S., & Jam, F. A. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13662-x · Cited by 16
“More recently, studies have increasingly applied the UTAUT framework (Venkatesh et al., 2003) to investigate the acceptance and use of emerging chatbot technologies, such as ChatGPT (Bouteraa et al., 2024; Strzelecki, 2024; Strzelecki & ElArabawy, 2024).”
Yin, X. F., & Dou, K. (2025) · Studies In Educational Evaluation · doi:10.1016/j.stueduc.2025.101480 · Cited by 21
Assessing students’ intention to adopt generative artificial intelligence
Bouebdallah, N., & Youssef, W. A. B. (2025) · Journal of Accounting Education · doi:10.1016/j.jaccedu.2025.100984 · Cited by 16
Development and validation of the Generative AI Literacy for Learning Scale (GenAI-LLs)
Gümüş, M. M., & KARA, M. (2025) · Australasian Journal of Educational Technology · doi:10.14742/ajet.10236 · Cited by 31
Bekturova, M., Tulepova, S., & Zhaitapova, A. (2025) · Education and Information Technologies · doi:10.1007/s10639-024-13306-6 · Cited by 17
“However, the integration of such AI tools in educational settings raises critical questions and concerns (Gill et al., 2024; Memarian & Doleck, 2023; Sok & Heng, 2023; Strzelecki, 2024; Zou & Houang, 2023).”
Martha, A. S. D., Widowati, S., & Rahayu, D. P. (2025) · Journal of Information Technology Education Research · doi:10.28945/5599 · Cited by 24
Empowering business research with ChatGPT: academic and student insights through the UTAUT lens
Al‐Hattami, H. M. (2025) · Discover Computing · doi:10.1007/s10791-025-09692-1 · Cited by 15
“Research by Camilleri [14] and Strzelecki [53] supports that ease of use fosters technology adoption in education.”
Valentine Joseph Owan, P. D., Mohammed, I. A., Bello, A., & Shittu, T. A. (2025) · Contemporary Educational Technology · doi:10.30935/cedtech/17243 · Cited by 18
Investigating the attitude of university students towards the use of ChatGPT as a learning resource
Estrada-Araoz, E. G., Valverde, Y. P., Quispe-Herrera, R., Ramos, N. A. G., Rivera-Mamani, F. A., & Claros, A. R. (2024) · Data & Metadata · doi:10.56294/dm2024268 · Cited by 13
Barakat, M. M., Salim, N. A., & Sallam, M. (2024) · Research Square · doi:10.21203/rs.3.rs-3919524/v1 · Cited by 10
Acceptance of generative AI in higher education: A latent profile analysis of policy guidelinesPreprint
Ioku, T., Kondo, S., & Watanabe, Y. (2024) · Research Square · doi:10.21203/rs.3.rs-4515787/v1 · Cited by 18
Ofem, U. J., Ene, E. I., Ajuluchukwu, E. N., Neji, H. A., Edam-Agbor, I. B., Orim, F. S., Nwogwugwu, C. E., Ovat, S. V., Ukatu, J. O., Ekpang, P. O., et al. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100337 · Cited by 14
Rahimi, A. R., & Mosalli, Z. (2025) · Smart Learning Environments · doi:10.1186/s40561-025-00368-3 · Cited by 18
“However, Strzelecki (2023) reported that the price value of ChatGPT seems reasonable to postgraduate Polish students, leading them to intend to use ChatGPT in the future.”
Gilmore, J. N., Whims, T., Blair, B. W., Katarzynski, B., & Steffen, L. (2025) · Convergence The International Journal of Research into New Media Technologies · doi:10.1177/13548565251337576 · Cited by 12
“Strzelecki (2024) suggests undergraduate students at a Polish university demonstrate comfort in adopting this technology.”
Rana, S., & Rai, P. (2025) · Journal of Applied Research in Higher Education · doi:10.1108/jarhe-09-2024-0519 · Cited by 6
ChatGPT-Assisted Decision-Making: An In-Depth Exploration of the Human–AI Interaction
Mustata, M. A., Iliescu, D., Mavriș, E., Jude, C.-R., Bojor, L., Tudorache, P., Cîrdei, I. A., Hrab, D.-E., Aluculesei, A.-C., Răpan, I., et al. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2499165 · Cited by 10
Yang, F., & Stefaniak, J. E. (2025) · Educational Technology Research and Development · doi:10.1007/s11423-025-10509-7 · Cited by 12
“…of ChatGPT, including concerns related to data privacy (Abadie et al., 2024; Choudhury & Shamszare, 2023; Ma & Huo, 2023), the dissemination of misleading or biased information (Liu et al., 2024; Romero-Rodríguez et al., 2023), and other ethical considerations (Strzelecki, 2023; Zou & Huang, 2023).”
Mariñas, K. A., Saflor, C. S., Alvarado, P., Uminga, J. M., & Verde, N. A. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100435 · Cited by 18
Gökçearslan, Ş., Eşiyok, E., & Küçükergi̇n, K. G. (2025) · Journal of Computational Social Science · doi:10.1007/s42001-025-00387-7 · Cited by 14
“Although different studies have explored the acceptance of AI chatbots in higher education settings [45, 47, 97, 105, 106, 121], most have been conducted within the framework of the general technology acceptance model, focusing broadly on AI-supported educational tools [29].”
A Comprehensive Review of ChatGPT in Teaching and Learning Within Higher Education
Phokoye, S. P., Dlamini, S., Mthalane, P. P., Luthuli, M., & Moyane, S. P. (2025) · Informatics · doi:10.3390/informatics12030074 · Cited by 17
“This gap in governance has prompted concerns regarding academic integrity and the ethical application of artificial intelligence within educational domains [29,30].”
Kalmus, J.-E., & Nikiforova, A. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-93409-4_9 · Cited by 9
Generative AI’s Impact on Graduate Student Professional Writing Productivity and Quality
Connell Pensky, A. E., Usdan, J. H., & Chang, H. (2025) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-025-00528-z · Cited by 9
“The ease of use and advanced capabilities of genAI have led to its widespread adoption (Strzelecki, 2024 ).”
Chopra, G., Bhaskar, P., Purohit, A., & Strzelecki, A. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13536-2 · Cited by 12
“Literature has indicated that effort expectancy influences students’ behavioral inten - tions to adopt ChatGPT (Arthur et al., 2024; Bouteraa et al., 2024; Gulati et al., 2024 ; Strzelecki, 2024a).”
Self-citationGuan, Q., & Yang-xi, H. (2025) · Innovations in Education and Teaching International · doi:10.1080/14703297.2025.2511062 · Cited by 11
“Our research aligns with the findings of Strzelecki (2024), which indicate that habits in adopting ChatGPT play a significant role in shaping students’ behavioural intentions to use the platform.”
The Role of ARCS Motivation in Predicting ChatGPT Adoption Based on UTAUT3 Framework
Parveen, K., Alghamdi, A. A., Samee, N. M. A., & Shafiq, M. (2025) · Journal of Educational Computing Research · doi:10.1177/07356331251362425 · Cited by 8
“Studies suggest that students are more likely to adopt ChatGPTwhen they perceive it as a valuable tool that enhances their academic performance and provides immediate, relevant, and accurate information (Strzelecki, 2024).”
InfluentialIsmail, N., & Joshy, L. M. (2025) · Management and Labour Studies · doi:10.1177/0258042x251357415 · Cited by 11
AlNajdi, S. M., Salem, M. A., & Elshaer, I. A. (2025) · Bioengineering · doi:10.3390/bioengineering12101095 · Cited by 10
“These dimensions were measured on a five-point Likert scale and were derived from previous studies [56,57], formerly coined as the “Unified Theory of Acceptance and Use of Technol-ogy” (UTAUT) framework [38,58].”
Generative AI in Action: Acceptance and Use Among Higher Education Staff Pre- and Post-training
Møgelvang, A., Cipriani, E., & Grassini, S. (2025) · Technology Knowledge and Learning · doi:10.1007/s10758-025-09915-w · Cited by 20
Tang, L., Cui, Y. H., & Zhang, X. (2025) · Innovation in Language Learning and Teaching · doi:10.1080/17501229.2025.2582751 · Cited by 8
“This aligns with previous research based on the UTAUT framework (Hsu 2022; Huang and Chueh 2022; Strzelecki 2024).”
Mapping artificial intelligence research in higher education toward sustainable development
Hong, T. T. M., Tung, N. T. T., & Thanh, N. T. P. (2025) · Discover Sustainability · doi:10.1007/s43621-025-02162-0 · Cited by 16
Amin, M. A., Noh, M., Haque, A., Rupa, R. A., & Kim, Y. S. (2026) · Beijing international review of education · doi:10.1177/25902547261421646 · Cited by 8
“Furthermore, students exhibiting higher levels of innovativeness are more intrinsically motivated to adopt new technologies than their peers (Mueller et al., 2024; Strzelecki, 2024).”
Studying with GenAI: Student views on the opportunities and risks of GenAI in higher education
Cena, E., McParland, A., Toivo, W., Dalton, B., Mundy, M., O'Connor, P. A., Robertson, A., Swingler, M. V., Wilson, P. C., & Duncan, C. (2026) · Education and Information Technologies · doi:10.1007/s10639-026-13923-3 · Cited by 11
“These GenAI tools have recently gained significant popularity in academic settings due to their capabilities of assisting with literature reviews, essay writing, research, and other academic projects (Bahroun et al., 2023; Stojanov, 2023 ; Strzelecki, 2023 ).”
Implementation of generative AI tools in accounting education context
Al-Okaily, M. (2025) · Journal of Applied Research in Higher Education · doi:10.1108/jarhe-08-2024-0422 · Cited by 9
Pantazatos, D., Grammatikou, M., & Maglaris, V. (2024) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon60312.2024.10578597 · Cited by 9
Kubullek, A.-K., Kumaç, N., & Dogangün, A. (2024) · Proceedings of Mensch und Computer 2024 · doi:10.1145/3670653.3677507 · Cited by 10
Enhancing academic writing in English language education through generative AI integration
Zamorano, C. (2025) · Research Studies in English Language Teaching and Learning · doi:10.62583/rseltl.v3i3.87 · Cited by 10
“Results show that increased awareness, customisation, and familiarity vastly augment adoption by students and educators (Strzelecki et al., 2024).”
Ramadan, E., Abdalla, S. Z. S., Ahbabi, A. A., Gibreel, T., & Hosani, N. A. (2025) · City and Environment Interactions · doi:10.1016/j.cacint.2025.100222 · Cited by 7
Yadegaridehkordi, E., Foroughi, B., & Ghobakhloo, M. (2025) · Innovative Higher Education · doi:10.1007/s10755-025-09835-8 · Cited by 7
“Users find ChatGPT enjoyable and entertaining because its user interface facilitates smooth interactions and a variety of conversational styles (Strzelecki, 2023).”
Learners’ Acceptance of ChatGPT in School
Conrad, M., & Nuebel, H. (2025) · Education Sciences · doi:10.3390/educsci15070904 · Cited by 7
Mudenda, S., Mufwambi, W., Mwale, R. S., Kathewera, B. S., & Lubanga, A. F. (2025) · BMC Medical Education · doi:10.1186/s12909-025-07833-0 · Cited by 14
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Educators’ Acceptance and Use of AI Software: UTAUT2 Model
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“…quality, interactivity, and content preparation tend to show higher intention to adopt them (An et al., 2023; Bazelais et al., 2024; Duong et al., 2023; Foroughi et al., 2023; Guggemos et al., 2020; Habibi et al., 2023; Strzelecki, 2023; Strzelecki & ElArabawy, 2024; Yao & Abd Halim, 2023).”
InfluentialCollaguazo, N. C. A., Jaramillo, R. A. A., & Bermúdez, Á. E. G. (2024) · Revista Científica Enfoques del Conocimiento · doi:10.55813/gaea/revistacec/v1/n1/7 · Cited by 8
Ramadan, E., Abdalla, S. Z. S., & Elshaiekh, N. E. (2025) · Transportation Research Interdisciplinary Perspectives · doi:10.1016/j.trip.2025.101377 · Cited by 11
Luo, X., & Adelopo, I. A. (2024) · Journal of International Education in Business · doi:10.1108/jieb-05-2024-0060 · Cited by 6
Swartz, S., Luck, S. L., & Sharma, S. (2025) · Higher Education Skills and Work-based Learning · doi:10.1108/heswbl-09-2024-0266 · Cited by 12
Mwakapesa, D. S. (2025) · Information Development · doi:10.1177/02666669251366666 · Cited by 9
Ambivalence and emotion in the age of AI: how students navigate ChatGPT in higher education
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Alruthaya, A. S. M., Khan, S., Saini, A., & Muthaly, S. (2025) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2025.100824 · Cited by 12
Thi, X. H. N., Thien, H. V. H., Nhung, V. K., & Nguyen, T. T. (2025) · Discover Education · doi:10.1007/s44217-025-00905-9 · Cited by 25
“Recent studies [3, 37, 47, 48] support this finding.”
How are faculty and college students embracing AI? — A multi-informant mixed method study
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Riding into the future: What drives the use of robotaxis in San Francisco?
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Multidimensional determinants of generative AI acceptance in foreign language education
Xu, T., & Xiong, Y. (2026) · Scientific Reports · doi:10.1038/s41598-026-36700-1 · Cited by 9
“This finding aligns with previous studies targeting different groups of participants, including EFL learners 29 , Poland university students 110 and so on.”
Adoption of Generative AI and Large Language Models in Education: A Short Review
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“[41] proposed interesting new factors of Hedonic Motivation (HM), Price Value (PV), and Habit (HT).”
Wicaksono, A., Barunastra, F. I., Suhari, M. C. J. G., & Jingga, F. (2024) · International Conference on Contemporary Computing and Informatics (IC3I) · doi:10.1109/ic3i61595.2024.10829196 · Cited by 7
“[9], [10].”
Assessing Tourism and Hospitality Students’ Intention to Use ChatGPT
Wong, C. U. I., Ren, L., Chen, P., Chen, X., & Zhang, H. (2025) · Journal of Hospitality & Tourism Education · doi:10.1080/10963758.2025.2499942 · Cited by 6
“As ChatGPT is rooted in neural network transforming technology and supported by Large Language Models (Strzelecki, 2024), it can produce responses, iterate and refine “answers” that closely resemble the evolving cognitive understanding reactions of humans.”
InfluentialHan, F., & Guo, J. (2025) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-025-00543-z · Cited by 5
“…the present study were the commonly examined personal factors in the TAM, future studies may expand on these factors by including additional personal factors, such as levels of digital literacy (He et al., 2021), hedonic motivation (Almogren, 2022), and personal innovativeness (Strzelecki, 2024).”
Factors affecting Indonesian pre-service EFL teachers’ AI acceptance and use
Firdaus, R., Habibi, A., Hendra, R., Fauzee, M. S. O., Oktaria, S. D., Sofwan, M., & Alqahtani, T. M. (2025) · Texto Livre Linguagem e Tecnologia · doi:10.1590/1983-3652.2025.57135 · Cited by 7
Ge, H., & Jae-Wook, S. (2025) · Acta Psychologica · doi:10.1016/j.actpsy.2025.106041 · Cited by 10
Development of the Digital Transformation Scale: Assessment of Measurement Invariance
Zincirli, M., & Polat, E. (2025) · SAGE Open · doi:10.1177/21582440251378827 · Cited by 7
“UTAUT was considered the most appropriate model for this research (Bajunaied et al., 2023; Hewavitharana et al., 2021; Jayawardena et al., 2023; Rejali et al., 2023; Strzelecki, 2024; Tolkun & Tekin, 2022; Yildiz Durak, 2019).”
Elzeky, M. E. H., & Shahine, N. F. M. (2025) · Nurse Education in Practice · doi:10.1016/j.nepr.2025.104696 · Cited by 5
Generative AI and digital literacy: Unravelling user intentions through PLS-SEM and machine learning
Bahçekapılı, E., & Boztaş, G. D. (2025) · Journal of Information Science · doi:10.1177/01655515251396900 · Cited by 6
Impact of generative artificial intelligence feedback on online student satisfaction
Medina, J., Morales, M., Barchino, R., Amado-Salvatierra, H. R., & Hernández-Rizzardini, R. (2026) · Frontiers in Computer Science · doi:10.3389/fcomp.2025.1708114 · Cited by 7
Artificial intelligence in higher education: a bibliometric analysis of research trends (2015–2024)
Mittal, N., Batra, G., & Sijariya, R. (2026) · Artificial Intelligence in Education · doi:10.1108/aiie-04-2025-0076 · Cited by 7
Ren, J., Shahzad, M. F., Abbas, J., Al‐Sulaiti, K. I., & Pilař, L. (2026) · Journal of Further and Higher Education · doi:10.1080/0309877x.2025.2573026 · Cited by 5
“These intricate facets of value pricing must be taken into account for Gen-AI to integrate across domains with ease (Strzelecki 2023).”
InfluentialCabuquin, J. C., Acidre, M. A. S. T., Manabat, M. A. A., Aruta, M. G. H., Sangutan, J., & Yu, R. F. B. (2024) · Salud Ciencia y Tecnología - Serie de Conferencias · doi:10.56294/sctconf2024.1205 · Cited by 15
From the Gender Lens: Student Perceptions of ChatGPT in Higher Education
Fadillah, M. A., & Akbar, M. F. (2025) · Advances in Mobile Learning Educational Research · doi:10.25082/amler.2025.01.015 · Cited by 8
Ruiz, M. U., Gecolea, J. P., Alcala, L. B. M., Gregana-Alcaraz, M. J. V., & Niegas, A. M. (2025) · International Conference on e-Education, e-Business, e-Management and e-Learning · doi:10.1109/ic4e65071.2025.11075295 · Cited by 5
“Whilst Strzelecki (2023) explored factors motivating university students' use of ChatGPT with the UTAUT2 model, identifying performance improvements and enjoyment as drivers.”
Shah, M. H. A., Dharejo, N., Mahar, Z. A., Nazeer, M. I., Khoso, I. A., & Shah, A. (2024) · Pegem Journal of Education and Instruction · doi:10.47750/pegegog.14.04.14 · Cited by 5
Ni, W., & Li, J. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1856854 · Cited by 6
“Cao et al. (2025) and Strzelecki (2024) show that institutional and technological support can be adopted differently whereas Habibi et al. (2023) show that usability and acceptance can be different among student groups.”
Huang, T., Chen, K. Y., Huang, Y. M., & Chiang, Y. H. (2025) · Interactive Learning Environments · doi:10.1080/10494820.2025.2559918 · Cited by 8
“This mapping is consistent with recent TPB applications to ChatGPT that highlight the centrality of attitude, subjective norm, and perceived control in shaping intention (Strzelecki, 2024; Tiwari et al., 2024).”
InfluentialRücker, M. T., Büchting, C., & Kosch, T. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757693 · Cited by 7
“The results of the quantitative survey (cf. Section 4) are consistent with related prior work, which has found the UTAUT2 constructs performance expectancy, effort expectancy, habit, and hedonic motivation to be valid predictors for behavioral intention in that context [5, 37, 40].”
Ali, I., Warraich, N. F., & Butt, K. (2025) · The Electronic Library · doi:10.1108/el-04-2025-0160 · Cited by 5
Al-Atraqchi, O. M. A., Abdulmuhsin, A. A., Rehman, S. U., & Mjlae, S. (2026) · VINE Journal of Information and Knowledge Management Systems · doi:10.1108/vjikms-08-2024-0309 · Cited by 7
Paik, J. E., Choung, H., & Yang, Q. (2026) · Health Communication · doi:10.1080/10410236.2026.2620497 · Cited by 4
“In higher education, where ChatGPT adoption emerged early, studies suggest that performance expectancy and effort expectancy may influence college students’ intentions to use ChatGPT (Budhathoki et al., 2024; Foroughi et al., 2024; Strzelecki, 2024).”
InfluentialMolerov, D., Federiakin, D. A., Zlatkin‐Troitschanskaia, O., Shenavai, K., Trierweiler, L., & Nagel, M. (2026) · Unterrichtswissenschaft · doi:10.1007/s42010-026-00242-2 · Cited by 6
“At the same time, variables derived from the Technology Acceptance Model remained significantly interrelated, supporting earlier research on technology acceptance in economics and social sciences (Yu et al. 2024; Strzelecki 2024).”
Parveen, K., Obidallah, W. J., Phuc, T. Q. B., Alduraywish, Y. A., Alghamdi, A. A., & Shafiq, M. (2026) · BMC Psychology · doi:10.1186/s40359-026-04042-2 · Cited by 4
“Studies have shown that students are more likely to adopt ChatGPT when they perceive it as beneficial to their academic performance and easy to use [31, 32].”
InfluentialTAŞKIRAN, N., Türk, G., & Hacı, A. (2026) · BMC Medical Education · doi:10.1186/s12909-026-08822-7 · Cited by 7
“This finding aligns with previous research suggesting that interactive AI tools such as ChatGPT may support content acquisition while also enhancing users’ technological confidence and critical engagement with digital systems [4, 8, 30].”
University students’ perceptions and adoptions of AI: a cross-national study
Marmolejo‐Ramos, F., Abadia, R., Karakale, Ö., Barrera-Causil, C., Männikkö, N., Ghorbani, B. D., Strzelecki, A., Nwizu, S. C., Tavares, C. R. G., Castillo, M., et al. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01042-4 · Cited by 5
Self-citationExploring self-learning efficacy: a comparative analysis of ChatGPT and Gemini
Colaco, D. M., & Antao, D. (2025) · Interactive Technology and Smart Education · doi:10.1108/itse-12-2023-0251 · Cited by 4
Sabeh, H. N., Kee, D. M. H., Mohammed, R. B., Albarwary, S. A., & Khalilov, S. (2025) · International Multi-Conference on Systems, Signals & Devices (SSD) · doi:10.1109/ssd64182.2025.10989952 · Cited by 5
“This discovery aligns with previous research [63], [64].”
Mao, P., Cai, Z., Sun, X., Niu, G., Zhang, Y., & Liu, J. (2025) · Learning and Individual Differences · doi:10.1016/j.lindif.2025.102756 · Cited by 4
Iqbal, J., Hashmi, Z. F., Seitamaa‐Hakkarainen, P., & Asghar, M. Z. (2025) · Scientific Reports · doi:10.1038/s41598-025-19625-z · Cited by 4
“Pauliukevičienė, Stankevičienė 2 identified SDGs 4, 8, 9, and 16 as key drivers of sustainable FinTech growth, emphasizing the role of public access to FinTech and an educated workforce in Northern Europe, including Finland, Germany, the Netherlands, and Sweden, leading in SC development.”
Overview of Artificial Intelligence Applications in Educational Research
Qu, T., & Yang, Z. (2024) · International Symposium on Artificial Intelligence for Education (ISAIE) · doi:10.1145/3700297.3700315 · Cited by 5
“[ 22 ] • Using ChatGPT in higher education [ 23 ] Generative artificial intelligence has the potential to serve as support tools for teachers and students.”
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
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