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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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Martha, A. S. D., Widowati, S., Rahayu, D. P., Nursyawal, M. I., & Hariz, R. R. (2024) · International Conference on Information Technology Systems and Innovation · doi:10.1109/icitsi65188.2024.10929431 · Cited by 4
“ChatGPTiscapableofengagingusersinpersuasive conversations�and�handling�a�variety�of�tasks,�including�essay� writing,�literature�review�compilation,�paper�refinement,�and� computer�code�generation�[3][4].”
Mathematics Student Teachers’ Behavioural Intention Using ChatGPT
Tăng, M. D., Nguyen, V. K., Tri, D. C. M., Quoc, P. L. C., & Ban, B. H. D. (2025) · Canadian Journal of Learning and Technology · doi:10.21432/cjlt28665 · Cited by 3
“In addition, our study unveiled that SI had a low impact on the BI of Mathematics student teachers to use ChatGPT, aligning with findings in similar ChatGPT research by Strzelecki (2023) and in chatbot studies by Rahim et al. (2022) and Tian et al. (2024).”
InfluentialAbidin, Z., Phusavat, K., Arifudin, R., & Sugiharti, E. (2024) · International Conference on Informatics and Computing (ICIC) · doi:10.1109/icic64337.2024.10956977 · Cited by 3
“These three constructs have demonstrated that they influence cognitive and affective attitudes [3], [20], [25].”
Mahfouz, I., & AbdelMohsen, M. M. (2025) · Arab World English Journal · doi:10.24093/awej/ai.2 · Cited by 4
“Strzelecki (2023) examined Polish university students’ acceptance of ChatGPT using the Unified Theory of Acceptance and Use of Technology (UTAUT2).”
Dương, L. T. T., Dũng, L. V., Hà, N. T., Sang, H. M., & Hiền, T. T. (2025) · VNU JOURNAL OF ECONOMICS AND BUSINESS · doi:10.57110/vnu-jeb.v5i3.447 · Cited by 3
Uyanık, G. (2025) · The Journal of Educational Research · doi:10.1080/00220671.2025.2571680 · Cited by 3
“Moreover, ChatGPT has been shown to expedite and enhance problem-solving and reasoning processes with a high degree of accuracy and efficiency across a wide range of academic disciplines (Strzelecki, 2024).”
Korchak, A., Patarakin, E., & Costley, J. (2025) · RUDN Journal of Informatization in Education · doi:10.22363/2312-8631-2025-22-1-37-57 · Cited by 5
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Rakha, A. H. (2026) · Frontiers in Sports and Active Living · doi:10.3389/fspor.2026.1772502 · Cited by 6
“Large-scale and country-level surveys show that learners generally view AI tools positively for efficiency and accessibility, while still expressing concerns about academic integrity, over-reliance, and impacts on critical thinking (35, 36).”
Park, J. J., Brown, S. A., Chu, S. L., Zhou, A. Q., & Abreu, R. L. (2026) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2026.103806 · Cited by 6
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Huang, L., & Luo, W. (2026) · Behavioral Sciences · doi:10.3390/bs16061002 · Cited by 4
“It encompasses factors such as system complexity and perceived ease of use, which influence user acceptance (Strzelecki et al., 2024).”
Zi, Y., 梁舒乔, Zhang, M., Liu, X., & Liu, B. (2026) · Sustainability · doi:10.3390/su18136701 · Cited by 4
Factors that Influence College Students in the use of ChatGPT in Indonesia
Silvano, R., & Gui, A. (2024) · doi:10.1109/it61232.2024.10475770 · Cited by 5
“…in increasing the speed of users in doing tasks, having a positive impact on the information provided by ChatGPT, and effectively assisting in user learning tasks in lectures, in this case PE is related to how much the users believe that the use of ChatGPT can improve their academic results [6].”
BackgroundPrameka, A. S., Kurniawan, D. T., Suwanan, A. F., Rakhmad, A. A. N., & Firmansyah, R. (2024) · International STEM Education Conference (iSTEM-Ed) · doi:10.1109/istem-ed62750.2024.10663097 · Cited by 5
“In previous studies, price have a significant effect on behavior intention in using AI [57], [58], while others did not find this significance [38], [59], [60].”
BackgroundResultInfluentialChinene, B., Mudadi, L.-S., Choto, T. A., Soko, N. D., Gonde, L., Mushosho, E. Y., & Mutandiro, L. C. (2024) · Radiography · doi:10.1016/j.radi.2024.11.004 · Cited by 4
Combrinck, C. (2024) · Research Square · doi:10.21203/rs.3.rs-4176435/v1 · Cited by 3
University Student Readiness and Academic Integrity in Using ChatGPT and AI Tools for Assessments
Chuah, K.-M., & Sumintono, B. (2024) · Lecture notes in educational technology · doi:10.1007/978-981-97-6136-4_4 · Cited by 2
Caffaratti, L. B., & Monchietto, A. (2025) · EDUCATION SCIENCES AND SOCIETY · doi:10.3280/ess2-2024oa18478 · Cited by 2
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An Investigation Into Turkish University Students' Acceptance of ChatGPT in Education
Adanır, G. A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-2612-2.ch010 · Cited by 2
Awashreh, R. A., & Almi, H. (2025) · Information systems engineering and management · doi:10.1007/978-3-031-91660-1_9 · Cited by 2
Teaching Writing in the Time of ChatGPT: Rethinking What Counts as Learning
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Kaur, H., Verma, T., Mago, M., & Phoolka, S. (2025) · Journal of International Education in Business · doi:10.1108/jieb-08-2024-0097 · Cited by 2
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“…examined the rejected Social Influence → Compatibility and Social Influence → Trust relationships, existing literature presents mixed findings on the Social Influence → Behavioral Intention relationship (e.g., Emon et al., 2023; Foroughi et al., 2024; Parveen et al., 2024; Strzelecki, 2024).”
Validity and Trustworthiness of AI-Generated Information in an Online Nutrition Course
Briggs, M., Archibald, A., Connors, P., & Heap, T. (2026) · Journal of Hospitality & Tourism Education · doi:10.1080/10963758.2026.2619164 · Cited by 2
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“To address this gap, this study employs the Unified Theory of Acceptance and Use of Technology (UTAUT) as a theoretical lens, alongside the ABC framework for student engagement [6].”
Yu, A., Ma, Y., Xu, G., Zhou, W., Gao, A., & Trainin, G. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.106451 · Cited by 2
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Gao, H., Kumar, J., Kumari, J., Huang, J., & Kumar, D. (2026) · Scientific Reports · doi:10.1038/s41598-026-46175-9 · Cited by 3
“Emerging AI platforms like DeepSeek are still under-researched in the Pakistani context relative to widely studied systems like ChatGPT 32,33 and awareness-based adoption systems are not well studied.”
InfluentialStudents’ Artificial Intelligence (AI) literacy: An exploratory study
Deshen, M., & Aharony, N. (2026) · Journal of Librarianship and Information Science · doi:10.1177/09610006261442178 · Cited by 2
Asag, M. A., & Mamun, M. A. A. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100614 · Cited by 4
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Navigating Supervisory Conversations About Gen AI : Taboo and Supervisory Power Dynamics
Qi, J., Tuxworth, J., & Gomes, C. (2026) · Higher Education Quarterly · doi:10.1111/hequ.70151 · Cited by 2
Exploring graduate students’ acceptance and use of generative AI: An application of the UTAUT2 model
Yıldırım-Samuk, Ş., & Altay, İ. F. (2026) · Contemporary Educational Technology · doi:10.30935/cedtech/18745 · Cited by 2
Amin, M. A., Noh, M., & Kim, Y. S. (2026) · Quality & Quantity · doi:10.1007/s11135-026-02940-6 · Cited by 2
Garro-Aburto, L. L., Chávez-Díaz, J. M., Salazar, C. A. H., Llerena-Espinoza, E. M., & Sandoval, S. P. S. (2026) · Education Sciences · doi:10.3390/educsci16071125 · Cited by 2
Yauri, H. Y. M., Luyo, S. L. L., Pérez, M. D. R., Arévalo-Aranda, C. P., Guerrero, L. A. O., Reyes, C. J. S., Vásquez, A. M. M., Ballesteros, M. A. A., & Alarcón, R. (2026) · European Journal of Investigation in Health Psychology and Education · doi:10.3390/ejihpe16070105 · Cited by 3
“…AI (GenAI) platforms have become the primary technological disruption in higher education, moving from the margins to the center of students’ academic routines, and a growing literature documents both their perceived benefits and the challenges they introduce (Chan & Hu, 2023; Strzelecki, 2024).”
ChatGPT in Language Education: Meta-Analysis
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Investigating the Driving Factors of the Use of Generative AI Among College Students
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Jafari, H., Naghshineh, N., Alvarado, O., Keshavarz, H., & Lund, B. D. (2024) · InfoScience Trends · doi:10.61186/ist.202401.01.17 · Cited by 4
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Ziemba, E. W., Grabara, D., Renik, K., & Maruszewska, E. W. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-78465-1_6 · Cited by 4
LA INTELIGENCIA ARTIFICIAL Y SU INFLUENCIA EN EL COMPORTAMIENTO DE LOS ESTUDIANTES
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Una revisión sistemática de estudios sobre IA: Aproximaciones desde la perspectiva del estudiantado
Chacón, M., Ramírez, J. C. C., Carrasco, S. A. N., & Encinas, K. L. P. (2025) · European Public & Social Innovation Review · doi:10.31637/epsir-2025-1704 · Cited by 7
Examining University Faculty's Acceptance of ChatGPT Through the Technology Acceptance Model
He, Z., & Chai, S. (2025) · Asia Conference on Information Engineering (ACIE) · doi:10.1109/acie64499.2025.00029 · Cited by 2
“While previous studies focus on students' acceptance of ChatGPT using the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) [13], [14] [15], [16], [17].”
Sehmi, A., Sarfraz, I., & Hussain, M. (2025) · Journal of Advanced Academics · doi:10.1177/1932202x251365312 · Cited by 5
Ożadowicz, A. (2024) · Preprints.org · doi:10.20944/preprints202405.2042.v2 · Cited by 2
Amendi, R., Halim, E., & Hartono, H. (2024) · International Conference on Technology Innovation and Its Applications (ICTIIA) · doi:10.1109/ictiia61827.2024.10761290 · Cited by 1
“Performance Expectancy (PE) : Performance expectancy is a critical factor that can influence an individual's intention based on improvising performance on the job, supported by the finding that higher perceived performance expectancy is capable of increasing the adoption rate of AI chatbots [10].”
InfluentialScientific Mapping of Chatgpt Usage in Education: A Bibliometric Perspective
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Correlates of ChatGPT use intention among freshmen health sciences students
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“Likewise, performance expectancy was found to be associated with behavioral intention to use ChatGPT among students and ChatGPT users in Malaysia [18], [34], and university students in Poland [35], Ghana [30], Spain [36], Saudi Arabia [37], Jordan [28], India [38], and Philippines [20].”
Students' Behavioral Intentions Towards Learning and Using Chatbots in Education
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Dimensiones latentes en la adopción de ChatGPT en la universidad: modelo CHASSIS
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“Over the past decade, people have gradually expressed increasing interest in the impact of Big Data and AI on education, but this interest significantly surged with the advent of GPT-40 [10], and the discussion reached an unprecedented level of intensity with the emergence of DeepSeek.”
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“The AI tools involved are diverse, including ChatGPT, TalkAI, SpeakG, Kimi, ERNIE Bot, Douban , and others.”
InfluentialService innovation through generative AI art platforms: the MIND affordances
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“…model (AISAS), the Technology Acceptance Model (TAM) (Albayati, 2024; Xu et al., 2023), the Unified Theory of Acceptance and Use of Technology (UTAUT), and UTAUT2 (Camilleri, 2024; Sobaih et al., 2024; Strzelecki, 2024), these studies primarily focus on its technical aspects.”
Meng, C. K., Wijaya, L., Lasian, L., Lestari, M. I., Wahono, J. W., & Anisa, N. N. (2025) · International Conference on Information Management and Technology (ICIMTech) · doi:10.1109/icimtech67074.2025.11265225 · Cited by 1
“Previous research that emphasises the need of analysing technology acceptance across a spectrum of cultural backgrounds [10], [11], guides the present approach by stressing the need of diverse samples to improve the knowledge of how new artificial intelligence technologies are adopted in different…”
Mohammed, I. A., Valentine Joseph Owan, P. D., Thomas, G., & Ekpoto, D. F. (2025) · Discover Education · doi:10.1007/s44217-025-01018-z · Cited by 1
“For example, the UTAUT2 model has been used to understand students’ acceptance and use of technology in different fields like ChatGPT [9, 56], ICT in tourism [4], e-learning [1, 3, 51]; mobile applications [6, 41]; immersive virtual and augmented reality [21]; and learning management systems [5, 7,…”
Students’ Acceptance and Enjoyment of ChatGPT in English Language Learning
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“Acceptance refers to the level of approval and willingness to use ChatGPT in their academic works (Liu, 2024; Strzelecki, 2024).”
Singh, B., & Sharma, D. K. (2025) · International Conference on Information Systems and Computer Networks (ISCON) · doi:10.1109/iscon65210.2025.11341462 · Cited by 2
“Using the UTAUT2 model, which considers elements such as performance expectancy, habit, and enjoyment (hedonic motivation), Strzelecki’s [18] study examines how university students accept and utilize ChatGPT.”
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A Multi-Year Survey of Use and Perceptions of Generative AI in Higher Education
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“Recent work on AI acceptance in higher education has explored gen AI use across countries [22, 23, 46] and ChatGPT adoption among students [46].”
Artificial Intelligence in Higher Education
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Nyamekye, E., Mensah, L. K., & Mutawakil, A.-R. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2026.2641773 · Cited by 2
“This has been shown in prior studies involving AI-based tools such as chatbots and ChatGPT (Cabrera-Sánchez et al., 2021).”
InfluentialIqbal, J., Hashmi, Z. F., Asghar, M. Z., & Seitamaa‐Hakkarainen, P. (2026) · Humanities and Social Sciences Communications · doi:10.1057/s41599-026-06895-9 · Cited by 1
ChatGPT at University: The Definitive Transition from Adoption to Quality of Student Interaction
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“Preliminary evidence suggests guided exploration increases breadth by 30-40% and reduces gender gaps by 15-25% (Strzelecki, 2024), though assignments must occur early (Year 1-2) before specialized patterns solidify.”
InfluentialSánchez, D. O., & Pérez-González, C. (2026) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2026.102717 · Cited by 2
From adoption to augmentation: a TCCM analysis of generative AI in higher education
Chugh, P., & Jain, V. (2026) · Quality & Quantity · doi:10.1007/s11135-026-02718-w · Cited by 2
Ahmed, S., Shanta, M. I., Ashrafuzzaman, M., & Hossen, R. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01277-1 · Cited by 1
“Strzelecki [11], administered a cross-sectional online survey to 503 university students in Poland to examine the conditions predicting willingness to use ChatGPT academi-cally.”
Pre-service mathematics and science teacher’s perceptions of ChatGPT as future educators
Örnek, F., Issah, M., Mutawah, M. A. A., Abdel-Hameed, F. S. M., Alaam, S. A., & Örnek, B. N. (2026) · Discover Education · doi:10.1007/s44217-026-01567-x · Cited by 1
“This finding aligns with earlier work on innovation diffusion [5] and more recent studies on AI adoption that emphasize the importance of user disposition and exploratory behavior [43, 49].”
InfluentialIntegrating Generative AI with Digital Learning Tools for Inclusive Education
Rappa, N. A., & Nonis, K. P. (2026) · Springer eBooks · doi:10.1007/978-3-032-12174-5_30 · Cited by 1
Rashid, S., & Malik, S. (2026) · Cogent Education · doi:10.1080/2331186x.2026.2661437 · Cited by 1
Fading skills, rising doubts – what fuels tourists’ skepticism toward AI?
Miskolczi, M., Jászberényi, M., Keller, K., Coronel, M., Szabóné, L. P., & Kökény, L. (2026) · Information Technology & Tourism · doi:10.1007/s40558-026-00370-6 · Cited by 1
“The dependent variables are often Behavioral Intention to Use (BIU) or Actual Use (AU) , while moderator variables include Age , Gender , Prior Experience , and Digital Competence (Obenza et al. 2024; Strzelecki 2024).”
Are Individuals Ready for CBDC Adoption? Insights from SLR and PLS-SEM using the UTAUT Model
Rani, M., & Kumar, J. (2026) · Asia-Pacific Financial Markets · doi:10.1007/s10690-026-09611-4 · Cited by 1
Alnujaidi, S. (2026) · Journal of language and Education · doi:10.17323/jle.2026.27903 · Cited by 2
The impact of artificial intelligence use on enhancing research efficacy among postgraduate students
Al-Rantisi, A. M., & Koofan, E. (2026) · Discover Education · doi:10.1007/s44217-026-01742-0 · Cited by 1
“In addition, Strzelecki [54] noted that although students are generally receptive to using ChatGPT in higher education, this acceptance is shaped by elements such as their technological skills and expectations regarding performance.”
Factors influencing accounting students attitudes towards AI tools in higher education using PLS SEM
Pajares, J. H., Sarrúa, L. A. J., & Miyakawa, K. C. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01376-z · Cited by 1
“These studies indicate that factors such as perceived usefulness, ease of use, social influence, self-efficacy, and facilitating conditions significantly influence the adoption of these technologies [29, 30, 69].”
InfluentialAlzahrani, S., & Bhunia, A. K. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1803390 · Cited by 1
Liu, Y., Meng, M., & Zhang, Y. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1858187 · Cited by 1
Decoding symmetric and asymmetric pathways in generative AI learning adoption: a multi-method study
Tian, X., Ayanwale, M. A., Molefi, R. R., Manchanda, P., Gençel, N., & OGUNJOUN, B. O. (2026) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-026-00613-w · Cited by 2
“At the cognitive-instrumental level, performance expectancy and effort expectancy predicted behavioral intention, aligning with UTAUT-based scholarship and Gen-AI adoption studies that report perceived usefulness and ease as supportive of intention formation (Parveen et al., 2025; Strzelecki, 2024).”
Kim, J., Lee, C., & Lim, J. S. (2026) · Telematics and Informatics · doi:10.1016/j.tele.2026.102436 · Cited by 2
Peng, R., Zhou, X., Duan, D., & Guo, H. (2026) · Buildings · doi:10.3390/buildings16142800 · Cited by 2
Attitudes and acceptance of generative AI-assisted problem solving among Mathematics students
Sixian, L., Muhammad, M., & Xiao, J. (2026) · Journal of Statistical Sciences and Computational Intelligence · doi:10.64497/jssci.323 · Cited by 1
Wan, Y., & Lu, K. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1896153 · Cited by 1
“…ChatGPT adoption research similarly treats perceived ease of use or effort expectancy as an important predictor of students’ acceptance of generative AI tools, because lower operational barriers can reduce psychological resistance to technology engagement (Shahzad et al., 2024; Strzelecki, 2024).”
InfluentialGözüm, A. İ. C., Strzelecki, A., Kaya, Ü. Ü., & Taşdan, M. (2026) · Information Systems Frontiers · doi:10.1007/s10796-026-10785-5 · Cited by 2
Self-citationChen, X., Cheng, C.-Y., & Hartanto, A. (2026) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2026.101267 · Cited by 1
Shi, H., Zhang, T., Wang, Z., Yang, X., Shao, Z., & Ma, X. (2026) · Journal of Affective Disorders · doi:10.1016/j.jad.2026.122428 · Cited by 1
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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