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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
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-019-0171-0 · Cited by 7,242 · cited 1×
“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).”
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013) · Long Range Planning · doi:10.1016/j.lrp.2013.01.001 · Cited by 5,310 · cited 1×
“The reliability and validity criteria were met for each construct, and discriminant validity was confirmed (Hair et al., 2013).”
LLaMA: Open and Efficient Foundation Language Models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al. (2023) · arXiv · doi:10.48550/arxiv.2302.13971 · Cited by 4,602
Statistics, an Introductory Analysis.
Joskow, J., & Yamane, T. (1965) · Journal of the American Statistical Association · doi:10.2307/2282703 · Cited by 4,558
ELIZA—a computer program for the study of natural language communication between man and machine
Weizenbaum, J. (1966) · Communications of the ACM · doi:10.1145/365153.365168 · Cited by 4,452 · cited 1×
“Historical traces of AI’s foray into education can be linked to the development of early chat-bots in the 1960s (Weizenbaum, 1966).”
Agarwal, R., & Prasad, J. (1998) · Information Systems Research · doi:10.1287/isre.9.2.204 · Cited by 3,575 · cited 2×
“Personal innovativeness (PI) has been acknowledged as important factor that impact technology adoption and usage (Agarwal & Prasad, 1998).”
Chatting and cheating: Ensuring academic integrity in the era of ChatGPT
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024) · Innovations in Education and Teaching International · doi:10.1080/14703297.2023.2190148 · Cited by 3,087 · cited 1×
“Cotton et al. (2023) and Tlili et al. (2023) discuss maintaining academic integrity, while Williamson et al. (2023) encourage more research into AI’s educational impact.”
Chatting and Cheating. Ensuring academic integrity in the era of ChatGPT
Cotton, D., Cotton, P., & Shipway, J. R. (2023) · doi:10.35542/osf.io/mrz8h · Cited by 3,079
Unified Theory of Acceptance and Use of Technology: A Synthesis and the Road Ahead
University of Arkansas, Venkatesh, V., Thong, J., Hong Kong University of Science and Technology, Xu, X., & The Hong Kong Polytechnic University (2016) · Journal of the Association for Information Systems · doi:10.17705/1jais.00428 · Cited by 2,513 · cited 1×
“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.”
How Habit Limits the Predictive Power of Intention: The Case of Information Systems Continuance1
Limayem, M., Hirt, S. G., & Cheung, C. M. K. (2007) · MIS Quarterly · doi:10.2307/25148817 · Cited by 2,450 · cited 1×
“Venkatesh et al. (2012) and Limayem et al. (2007) have explained HT as a perceptual construct, and it has been found to be a significant predictor of “Behavioral intention” and technology use (Tamilmani et al., 2019).”
Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019) · Information Systems Frontiers · doi:10.1007/s10796-017-9774-y · Cited by 2,137 · cited 1×
“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.”
What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023) · Smart Learning Environments · doi:10.1186/s40561-023-00237-x · Cited by 2,003 · cited 1×
“Cotton et al. (2023) and Tlili et al. (2023) discuss maintaining academic integrity, while Williamson et al. (2023) encourage more research into AI’s educational impact.”
ChatGPT: five priorities for research
van Dis, E. A. M., Bollen, J., Zuidema, W., van Rooij, R., & Bockting, C. L. (2023) · Nature · doi:10.1038/d41586-023-00288-7 · Cited by 1,842 · cited 1×
“Van Dis et al. (2023) recognize its benefits in democratizing knowledge but call for debates on authorship and accountability.”
Partial Least Squares Structural Equation Modeling
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2022) · Handbook of Market Research · doi:10.1007/978-3-319-57413-4_15 · Cited by 1,483
ChatGPT is fun, but not an author
Thorp, H. H. (2023) · Science · doi:10.1126/science.adg7879 · Cited by 1,437 · cited 1×
“The potential impact of AI on academia and the need for ethical use policies is discussed by Lund et al. (2023), while Thorp (2023) cautions against naming AI as a co-author.”
Chatting about ChatGPT: how may AI and GPT impact academia and libraries?
Lund, B. D., & Wang, T. (2023) · Library Hi Tech News · doi:10.1108/lhtn-01-2023-0009 · Cited by 1,355
Consistent and asymptotically normal PLS estimators for linear structural equations
Dijkstra, T. K., & Henseler, J. (2014) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2014.07.008 · Cited by 1,344
Examining Science Education in ChatGPT: An Exploratory Study of Generative Artificial Intelligence
Cooper, G. (2023) · Journal of Science Education and Technology · doi:10.1007/s10956-023-10039-y · Cited by 1,263 · cited 1×
“Naumowa (2023) stresses critical thinking in using AI, whereas Hsu (2023), Ivanov and Soliman (2023), Lim et al. (2023), Crawford et al. (2023), Sullivan et al. (2023), and Cooper (2023), explore the ethical and practical implications, calling for ethical guidelines and theoretical frameworks.”
Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023) · The International Journal of Management Education · doi:10.1016/j.ijme.2023.100790 · Cited by 1,261 · cited 1×
“Naumowa (2023) stresses critical thinking in using AI, whereas Hsu (2023), Ivanov and Soliman (2023), Lim et al. (2023), Crawford et al. (2023), Sullivan et al. (2023), and Cooper (2023), explore the ethical and practical implications, calling for ethical guidelines and theoretical frameworks.”
Pavlik, J. V. (2023) · Journalism & Mass Communication Educator · doi:10.1177/10776958221149577 · Cited by 972 · cited 1×
“Interviews explore its potential in improving content creation and academic research, with a focus on ethical implications (Lund & Wang, 2023; Pavlik, 2023).”
Slade, E. L., Dwivedi, Y. K., Piercy, N. C., & Williams, M. D. (2015) · Psychology & Marketing · doi:10.1002/mar.20823 · Cited by 922 · cited 1×
“The connection between Personal Innovativeness (PI) and acceptance and usage of technology has been thoroughly investigated in the literature (Slade et al., 2015).”
Lund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., & Wang, Z. (2023) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24750 · Cited by 919 · cited 2×
“Interviews explore its potential in improving content creation and academic research, with a focus on ethical implications (Lund & Wang, 2023; Pavlik, 2023).”
ChatGPT in higher education: considerations for academic integrity and student learning
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.17 · Cited by 853 · cited 1×
“Naumowa (2023) stresses critical thinking in using AI, whereas Hsu (2023), Ivanov and Soliman (2023), Lim et al. (2023), Crawford et al. (2023), Sullivan et al. (2023), and Cooper (2023), explore the ethical and practical implications, calling for ethical guidelines and theoretical frameworks.”
Eysenbach, G. (2023) · JMIR Medical Education · doi:10.2196/46885 · Cited by 840 · cited 1×
“Con-versations with ChatGPT reveal its capabilities and limitations, particularly in areas like plagiarism and misuse in education (Eysenbach, 2023).”
Perkins, M. (2023) · Journal of University Teaching and Learning Practice · doi:10.53761/1.20.02.07 · Cited by 839
Strzelecki, A. (2024) · Interactive Learning Environments · doi:10.1080/10494820.2023.2209881 · Cited by 807
Self-citationTamilmani, K., Rana, N. P., Wamba, S. F., & Dwivedi, R. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102269 · Cited by 660 · cited 1×
“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).”
Generating scholarly content with ChatGPT: Ethical challenges for medical publishing
Liebrenz, M., Schleifer, R., Buadze, A., Bhugra, D., & Smith, A. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00019-5 · Cited by 594 · cited 1×
“The chatbot generates coherent and informative text, and can be accessed in many countries (Liebrenz et al., 2023).”
Nazaretsky, T., Ariely, M., Cukurova, M., & Alexandron, G. (2022) · British Journal of Educational Technology · doi:10.1111/bjet.13232 · Cited by 589 · cited 3×
“The integration of Artificial Intelligence (AI) into various sectors of society has become a prominent focal point of academic inquiry and application, with education standing out as a particularly noteworthy arena (Nazaretsky et al., 2022a).”
Crawford, J., Cowling, M., & Allen, K.-A. (2023) · Journal of University Teaching and Learning Practice · doi:10.53761/1.20.3.02 · Cited by 547 · cited 1×
“Naumowa (2023) stresses critical thinking in using AI, whereas Hsu (2023), Ivanov and Soliman (2023), Lim et al. (2023), Crawford et al. (2023), Sullivan et al. (2023), and Cooper (2023), explore the ethical and practical implications, calling for ethical guidelines and theoretical frameworks.”
Can artificial intelligence transform higher education?
Bates, T., Cobo, C., Mariño, O., & Wheeler, S. (2020) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-020-00218-x · Cited by 490 · cited 1×
“One can point to the surge of interest in AI’s educational potential over the last decade, juxtaposed with a landscape marked by exaggerated claims and often inconclusive findings (Bates et al., 2020).”
El-Masri, M., & Tarhini, A. (2017) · Educational technology research and development · doi:10.1007/s11423-016-9508-8 · Cited by 440 · cited 2×
“…“Social influence” to have a significant effect on “Behavioral intention” in areas such as mobile learning (Kang et al., 2015) and e-learning systems (El-Masri & Tarhini, 2017), while others did not find such significance, such as in the study of Alotumi (2022) or Kumar & Bervell (2019) on…”
Welcome to the Era of ChatGPT et al.: The Prospects of Large Language Models
Teubner, T., Flath, C. M., Weinhardt, C., van der Aalst, W., & Hinz, O. (2023) · Business & Information Systems Engineering · doi:10.1007/s12599-023-00795-x · Cited by 384 · cited 1×
“ChatGPT in university education presents challenges to the academic community wherever ChatGPT is available (Teubner et al., 2023).”
The influence of learning value on learning management system use
Ain, N., Kaur, K., & Waheed, M. (2015) · Information Development · doi:10.1177/0266666915597546 · Cited by 363 · cited 3×
“However, our findings contradict those of studies that 1 3 Innovative Higher Education (2024) 49:223–245 examined the use of learning management systems (Ain et al., 2016) and blackboard learning systems (Raza et al., 2022).”
Acceptance and use of lecture capture system (LCS) in executive business studies
Farooq, M. S., Salam, M., Jaafar, N., Fayolle, A. J.-C., Ayupp, K., Radović-Marković, M. M., & Ali, S. (2017) · Interactive Technology and Smart Education · doi:10.1108/itse-06-2016-0015 · Cited by 348
Preservice Teachers’ Acceptance of Learning Management Software: An Application of the UTAUT2 Model
Raman, A. L., & Don, Y. (2013) · International Education Studies · doi:10.5539/ies.v6n7p157 · Cited by 328 · cited 2×
“…that have demonstrated a notable and affirmative correlation between “Performance expectancy” and “Behavioral intention” in areas such as mobile learning (Arain et al., 2019), e-learning platforms (Azizi et al., 2020; Nikolopoulou et al., 2020), and learning management software (Raman & Don, 2013).”
Faqih, K. M. S., & Jaradat, M.-I. R. M. (2021) · Technology in Society · doi:10.1016/j.techsoc.2021.101787 · Cited by 297 · cited 3×
“…earlier studies on the use of emerging technologies in higher education, including Google Classroom (Kumar & Bervell, 2019), animation (Dajani & Abu Hegleh, 2019), mobile technologies (Hu et al., 2020), blended learning (Azizi et al., 2020), and augmented reality technology (Faqih & Jaradat, 2021).”
Game of algorithms: ChatGPT implications for the future of tourism education and research
Ivanov, S. H., & Soliman, M. S. A. (2023) · Journal of Tourism Futures · doi:10.1108/jtf-02-2023-0038 · Cited by 263 · cited 1×
“Naumowa (2023) stresses critical thinking in using AI, whereas Hsu (2023), Ivanov and Soliman (2023), Lim et al. (2023), Crawford et al. (2023), Sullivan et al. (2023), and Cooper (2023), explore the ethical and practical implications, calling for ethical guidelines and theoretical frameworks.”
Kumar, J. A., & Bervell, B. (2019) · Education and Information Technologies · doi:10.1007/s10639-018-09858-z · Cited by 253 · cited 10×
“This finding is in line with earlier studies on the use of emerging technologies in higher education, including Google Classroom (Kumar & Bervell, 2019), animation (Dajani & Abu Hegleh, 2019), mobile technologies (Hu et al., 2020), blended learning (Azizi et al., 2020), and augmented reality technology (Faqih & Jarada…”
InfluentialHoi, V. N. (2020) · Computers & Education · doi:10.1016/j.compedu.2019.103761 · Cited by 250 · cited 1×
“The explained variances were higher than in a study conducted by Maican et al. (2019) on communication and collaboration applications in academic environments, as well as in a study conducted by Hoi (2020) on how the mobile devices are accepted and used for language learning by students.”
Reasons associated with preservice teachers’ intention to use immersive virtual reality in education
Bower, M., DeWitt, D., & Lai, J. W. M. (2020) · British Journal of Educational Technology · doi:10.1111/bjet.13009 · Cited by 239 · cited 1×
“…such as e-learning systems (Raza et al., 2022), mobile applications (Ameri et al., 2020; Kang et al., 2015), immersive virtual and augmented reality (Bower et al., 2020; Faqih & Jaradat, 2021), and learning management software (Alotumi, 2022; Jakkaew & Hemrungrote, 2017; Kumar & Bervell, 2019).”
Re-examining AI, automation and datafication in education
Williamson, B., Macgilchrist, F., & Potter, J. (2023) · Learning Media and Technology · doi:10.1080/17439884.2023.2167830 · Cited by 234 · cited 1×
“Cotton et al. (2023) and Tlili et al. (2023) discuss maintaining academic integrity, while Williamson et al. (2023) encourage more research into AI’s educational impact.”
Raffaghelli, J. E., Rodríguez, M. E., Guerrero‐Roldán, A., & Bañeres, D. (2022) · Computers & Education · doi:10.1016/j.compedu.2022.104468 · Cited by 227
Extending UTAUT2 toward acceptance of mobile learning in the context of higher education
Arain, A. A., Hussain, Z., Rizvi, W. H., & Vighio, M. S. (2019) · Universal Access in the Information Society · doi:10.1007/s10209-019-00685-8 · Cited by 219 · cited 2×
“…that have demonstrated a notable and affirmative correlation between “Performance expectancy” and “Behavioral intention” in areas such as mobile learning (Arain et al., 2019), e-learning platforms (Azizi et al., 2020; Nikolopoulou et al., 2020), and learning management software (Raman & Don, 2013).”
The Influence of Values on E-learning Adoption
Mehta, A., Morris, N. P., Swinnerton, B. J., & Homer, M. (2019) · Computers & Education · doi:10.1016/j.compedu.2019.103617 · Cited by 214 · cited 1×
“…theory, has been shown to be a significant predictor of “Behavioral intention” in studies such as Raffaghelli et al.’s (2022) exploration of students’ acceptance of an early warning system in higher education and Mehta et al.’s (2019) investigation of the influence of values on e-learning adoption.”
Nikolopoulou, K., Gialamas, V., & Lavidas, K. (2020) · Education and Information Technologies · doi:10.1007/s10639-020-10157-9 · Cited by 205 · cited 2×
“…that have demonstrated a notable and affirmative correlation between “Performance expectancy” and “Behavioral intention” in areas such as mobile learning (Arain et al., 2019), e-learning platforms (Azizi et al., 2020; Nikolopoulou et al., 2020), and learning management software (Raman & Don, 2013).”
Hu, S., Laxman, K., & Lee, K. L. (2020) · Education and Information Technologies · doi:10.1007/s10639-020-10171-x · Cited by 202 · cited 1×
“…earlier studies on the use of emerging technologies in higher education, including Google Classroom (Kumar & Bervell, 2019), animation (Dajani & Abu Hegleh, 2019), mobile technologies (Hu et al., 2020), blended learning (Azizi et al., 2020), and augmented reality technology (Faqih & Jaradat, 2021).”
Azizi, S. M., Roozbahani, N., & Khatony, A. (2020) · BMC Medical Education · doi:10.1186/s12909-020-02302-2 · Cited by 177 · cited 4×
“…that have demonstrated a notable and affirmative correlation between “Performance expectancy” and “Behavioral intention” in areas such as mobile learning (Arain et al., 2019), e-learning platforms (Azizi et al., 2020; Nikolopoulou et al., 2020), and learning management software (Raman & Don, 2013).”
InfluentialLewis, C., Fretwell, C. E., Ryan, J., & Parham, J. B. (2013) · International Journal of Higher Education · doi:10.5430/ijhe.v2n2p22 · Cited by 176 · cited 1×
“Our research affirms the strong association between “Performance expectancy” and “Habit”, which is consistent with previous studies by Venkatesh et al. (2012) and Lewis et al. (2013) on how emerging technologies are accepted in higher education.”
Zacharis, G., & Nikolopoulou, K. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11116-2 · Cited by 165 · cited 1×
“Recent studies also examine how these factors can be affected by different contextual factors, such as the COVID-19 pandemic (Osei et al., 2022; Zacharis & Nikolopoulou, 2022), and how they can be utilized to improve the design and implementation of educational technology tools.”
An Instrument for Measuring Teachers’ Trust in AI-Based Educational Technology
Nazaretsky, T., Cukurova, M., & Alexandron, G. (2022) · LAK22: 12th International Learning Analytics and Knowledge Conference · doi:10.1145/3506860.3506866 · Cited by 158 · cited 3×
“The integration of Artificial Intelligence (AI) into various sectors of society has become a prominent focal point of academic inquiry and application, with education standing out as a particularly noteworthy arena (Nazaretsky et al., 2022a).”
Zwain, A. A. A. (2019) · Interactive Technology and Smart Education · doi:10.1108/itse-09-2018-0065 · Cited by 145 · cited 2×
“…using Google Classroom (Alotumi, 2022; Kumar & Bervell, 2019), mobile learning (Ameri et al., 2020; Arain et al., 2019; Kang et al., 2015; Nikolopou-lou et al., 2020) e-learning platform (El-Masri & Tarhini, 2017; Twum et al., 2022) and learning management system (Ain et al., 2016; Zwain, 2019).”
Zembylas, M. (2023) · Learning, Media and Technology · doi:10.1080/17439884.2021.2010094 · Cited by 142
Sitar‐Tăut, D., & Mican, D. (2021) · Online Information Review · doi:10.1108/oir-01-2021-0017 · Cited by 136 · cited 4×
“A number of scholars “have suggested that personality traits, such as personal innovativeness, play a significant role in technology adoption in the domain of Information Technology (IT)” (Farooq et al., 2017; Sitar-Taut & Mican, 2021).”
InfluentialMaican, C. I., Cazan, A.-M., Lixandroiu, R. C., & Dovleac, L. (2019) · Computers & Education · doi:10.1016/j.compedu.2018.09.010 · Cited by 128 · cited 1×
“The explained variances were higher than in a study conducted by Maican et al. (2019) on communication and collaboration applications in academic environments, as well as in a study conducted by Hoi (2020) on how the mobile devices are accepted and used for language learning by students.”
Twum, K. K., Ofori, D., Keney, G., & Korang-Yeboah, B. (2021) · Journal of Science and Technology Policy Management · doi:10.1108/jstpm-12-2020-0168 · Cited by 127 · cited 4×
“Nevertheless, our results are not the same as those of Twum et al. (2022), who found no direct effect of “Habit” on “Behavioral intention” to use e-learning, and Raman & Don (2013) and Ain et al. (2016), who found no direct impact of “Habit” on “Behavioral intention” to use learning management…”
InfluentialBehavior intention of animation usage among university students
Dajani, D., & Hegleh, A. S. A. (2019) · Heliyon · doi:10.1016/j.heliyon.2019.e02536 · Cited by 121 · cited 4×
“Previous studies on the adoption of animation (Dajani & Abu Hegleh, 2019) and Google Classroom in higher education (Alotumi, 2022; Kumar & Bervell, 2019) have usually found that “Facilitating conditions” have no effect.”
InfluentialYu, C.-W. F., Chao, C., Chang, C.-F., Chen, R.-J., Chen, P.-C., & Liu, Y. (2021) · SAGE Open · doi:10.1177/21582440211055721 · Cited by 117 · cited 2×
“UTAUT “synthesizes the concepts and user experiences that provide the foundation for theories on the user acceptance process of an information system” (Yu et al., 2021).”
Osei, H. V., Kwateng, K. O., & Boateng, K. A. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11047-y · Cited by 115 · cited 2×
“Recent studies also examine how these factors can be affected by different contextual factors, such as the COVID-19 pandemic (Osei et al., 2022; Zacharis & Nikolopoulou, 2022), and how they can be utilized to improve the design and implementation of educational technology tools.”
Alotumi, M. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11051-2 · Cited by 111 · cited 9×
“This finding is in line with earlier studies on the use of emerging technologies in higher education, including Google Classroom (Kumar & Bervell, 2019), animation (Dajani & Abu Hegleh, 2019), mobile technologies (Hu et al., 2020), blended learning (Azizi et al., 2020), and augmented reality technology (Faqih & Jarada…”
InfluentialHahn, M. G., Navarro, S. M. B., De La Fuente Valentin, L., & Burgos, D. (2021) · IEEE Access · doi:10.1109/access.2021.3100890 · Cited by 111
(2021) · Technometrics · doi:10.1080/00401706.2021.1982286 · Cited by 107
Jakkaew, P., & Hemrungrote, S. (2017) · doi:10.1109/icdamt.2017.7904962 · Cited by 103 · cited 2×
“…it has been shown to positively influence acceptance and use of lecture caption systems (Farooq et al., 2017), mobile learning during social distancing (Sitar-Taut & Mican, 2021), and Google Classroom platform for mobile learning (Alotumi, 2022; Jakkaew & Hemrungrote, 2017; Kumar & Bervell, 2019).”
Tamilmani, K., Rana, N. P., & Dwivedi, Y. K. (2018) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-04315-5_19 · Cited by 102 · cited 1×
“Venkatesh et al. (2012) and Limayem et al. (2007) have explained HT as a perceptual construct, and it has been found to be a significant predictor of “Behavioral intention” and technology use (Tamilmani et al., 2019).”
Ameri, A., Khajouei, R., Ameri, A., & Jahani, Y. (2019) · Education and Information Technologies · doi:10.1007/s10639-019-09965-5 · Cited by 101 · cited 2×
“…affecting students’ or teachers’ intentions to use different technology tools such as e-learning systems (Raza et al., 2022), mobile applications (Ameri et al., 2020; Kang et al., 2015), immersive virtual and augmented reality (Bower et al., 2020; Faqih & Jaradat, 2021), and learning management…”
Adoption of Artificial Intelligence in Schools: Unveiling Factors Influencing Teachers’ Engagement
Cukurova, M., Miao, X., & Brooker, R. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-36272-9_13 · Cited by 88
E-learning in higher education during COVID-19: evidence from blackboard learning system
Raza, S. A., Qazi, Z., Qazi, W., & Ahmed, M. (2021) · Journal of Applied Research in Higher Education · doi:10.1108/jarhe-02-2021-0054 · Cited by 85 · cited 3×
“…UTAUT2 model is used to identify factors affecting students’ or teachers’ intentions to use different technology tools such as e-learning systems (Raza et al., 2022), mobile applications (Ameri et al., 2020; Kang et al., 2015), immersive virtual and augmented reality (Bower et al., 2020; Faqih &…”
Investigating the Determinants of Mobile Learning Acceptance in Korea Using UTAUT2
Kang, M., Liew, B. T., Lim, H., Jang, J., & Lee, S. (2014) · Lecture notes in educational technology · doi:10.1007/978-3-662-44188-6_29 · Cited by 72 · cited 3×
“Previous studies have found “Social influence” to have a significant effect on “Behavioral intention” in areas such as mobile learning (Kang et al., 2015) and e-learning systems (El-Masri & Tarhini, 2017), while others did not find such significance, such as in the study of Alotumi (2022) or Kumar…”
InfluentialLi, C., Xing, W., & Leite, W. L. (2022) · British Journal of Educational Technology · doi:10.1111/bjet.13227 · Cited by 59 · cited 1×
“Moreover, accompanying this technological fervor are grave concerns about intellectual property, data privacy, potential biases, and equitable access to resources (Li et al., 2022).”
Naumova, E. N. (2023) · Journal of Public Health Policy · doi:10.1057/s41271-023-00400-1 · Cited by 52
Should schools ban AI chatbots?
Hsu, J. (2023) · The New Scientist · doi:10.1016/s0262-4079(23)00099-4 · Cited by 12 · cited 2×
“Naumowa (2023) stresses critical thinking in using AI, whereas Hsu (2023), Ivanov and Soliman (2023), Lim et al. (2023), Crawford et al. (2023), Sullivan et al. (2023), and Cooper (2023), explore the ethical and practical implications, calling for ethical guidelines and theoretical frameworks.”
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
Yusuf, A., Pervin, N., & Román-González, M. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00453-6 · Cited by 554
“Consequently, there has been an outright discouragement of their usage by some academic journals and HEIs (Chan, 2023; Strzelecki, 2023; Vincent, 2023).”
BackgroundA multinational study on the factors influencing university students’ attitudes and usage of ChatGPT
Abdaljaleel, M., Barakat, M. M., Alsanafi, M., Salim, N. A., Abazid, H., Malaeb, D. N., Mohammed, A. H., Hassan, B. A. R., Wayyes, A. M., Farhan, S. S., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-52549-8 · Cited by 391
Higher education students’ perceptions of ChatGPT: A global study of early reactions
Ravšelj, D., Keržič, D., Tomaževič, N., Umek, L., Brezovar, N., A. Iahad, N., Abdulla, A. A., Akopyan, A., Aldana Segura, M. W., AlHumaid, J., et al. (2025) · PLOS ONE · doi:10.1371/journal.pone.0315011 · Cited by 376
ChatGPT awareness, acceptance, and adoption in higher education: The role of trust as a cornerstone
Shahzad, M. F., Xu, S., & Javed, I. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00478-x · Cited by 274
“Second, the direct effect of CGPTAW on CGPTAI has not been discussed in prior studies (Maheshwari, 2023; Strzelecki, 2023); thus, this study examined the awareness of ChatGPT as a novel technology and its effect on students’ ultimate intention to adopt the technology.”
BackgroundMethodInfluentialShahzad, M. F., Xu, S., & Zahid, H. (2025) · Education and Information Technologies · doi:10.1007/s10639-024-12949-9 · Cited by 204
“Past studies (Shahzad et al., 2024 ; Strzelecki, 2024 ) focused on different theo - ries, such as UTAUT and TAM models.”
Metadata
- Authors
- Strzelecki, A.
- Affiliations
- University of Economics in Katowice (PL)
- Corresponding author
- Strzelecki, Artur
- ORCID
- Strzelecki, Artur
- Venue
- Innovative Higher Education
- Published
- 2023-11-30
- Volume
- 49
- Issue
- 2
- Pages
- 223-245
- ISSN
- 0742-5627 (ISSN-L)
- 1573-1758
- OpenAlex
- W4389203481
- Cited by
- 477
- References
- 78
- Citations by year
- 2026: 195 · 2025: 179 · 2024: 103
- FWCI
- 121.61
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Innovative Higher Education (publishedVersion, cc-by)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Keywords
- ChatGPT, technology acceptance, UTAUT2, personal innovativeness, habit, behavioral intention, use behavior, performance expectancy, hedonic motivation, facilitating conditions, university students, artificial intelligence in higher education
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-06, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-06 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
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