The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index; 1 of the 43 sources its claims cite were checked for support.
An expectancy value theory (EVT) based instrument for measuring student perceptions of generative AI
Open accessPublished version of a preprint
Abstract
This study examines the relationship between student perceptions and their intention to use generative artificial intelligence (GenAI) in higher education. With a sample of 405 students participating in the study, their knowledge, perceived value, and perceived cost of using the technology were measured by an Expectancy-Value Theory (EVT) instrument. The scales were first validated and the correlations between the different components were subsequently estimated. The results indicate a strong positive correlation between perceived value and intention to use generative AI, and a weak negative correlation between perceived cost and intention to use. As we continue to explore the implications of GenAI in education and other domains, it is crucial to carefully consider the potential long-term consequences and the ethical dilemmas that may arise from widespread adoption.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This study validates an Expectancy-Value Theory (EVT)-based instrument for measuring higher education students’ perceptions of generative AI and examines how knowledge, perceived value, and perceived cost relate to intention to use GenAI. Among 405 Hong Kong students, perceived value showed the strongest positive correlation with intention to use, while perceived cost showed a weaker negative correlation.
Key findings
- Perceived value was strongly positively correlated with intention to use GenAI (r = 0.606, p < 0.001); its subscales were also positively correlated: attainment value (r = 0.587), intrinsic value (r = 0.459), and utility value (r = 0.506), all p < 0.001.
- Perceived cost was negatively correlated with intention to use (r = -0.295, p < 0.001), while knowledge (r = 0.178, p < 0.001) and past use frequency (r = 0.339, p < 0.001) were positively correlated with intention.
- Reliability was acceptable for knowledge (Cronbach’s α = 0.812), perceived value (α = 0.876), and perceived cost (α = 0.746); CFA fit indices were reported as acceptable, with χ²/df < 3, RMSEA < 0.07, and SRMR < 0.080 for the scales.
- Descriptive results showed limited prior use of GenAI (Q1 mean = 2.32), while intention items had means of 3.92 (Q22) and 3.86 (Q23); perceived cost item means ranged from 2.85 to 3.15.
Methods
- Cross-sectional online survey using convenience sampling at one university in Hong Kong in March 2023; 405 valid responses out of 460, mean age 23.87 years, 51.4% male and 48.6% female, 50.9% undergraduate and 49.1% postgraduate, 52.8% STEM and 45.7% non-STEM.
- The EVT-based instrument had 23 five-point Likert items: knowledge (Q2–Q6), perceived value (Q7–Q17; attainment Q7–Q10, intrinsic Q11–Q13, utility Q14–Q17), perceived cost (Q18–Q21), and intention to use (Q22–Q23), plus Q1 for use frequency.
- Analyses used IBM SPSS 27 and IBM AMOS 28: descriptive statistics, Cronbach’s alpha, confirmatory factor analysis (CFA) without exploratory factor analysis, AVE/CR/HTMT validity checks, and Pearson correlations.
Limitations
- The paper states that the sample of 405 may not fully represent the larger student population, the study was conducted at a single point in time, it focused only on higher education students, it relied on self-reported data subject to response biases, and it did not explore individual differences such as cultural background, personal experiences, and learning styles.
- The paper also states that skipping EFA may overlook alternative factor structures or item-loading issues, though it justifies using only CFA given the strong theoretical basis.
- Evident from the text: convenience sampling from a single Hong Kong university limits generalizability, and the cross-sectional design cannot establish causality.
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Claims the paper makes
24 Not checked yet · 24 Own result · 18 Own method · 9 Own interpretation · 3 No basis given · 1 Partly supported · accuracy not yet measured
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 results indicate a strong positive correlation between perceived value and intention to use generative AI, and a weak negative correlation between perceived cost and intention to use.”
§ Smart Learning Environments > Abstract
hedgedcorrelational- 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
“Whereas students' responses on the cost of using generative AI were more neutral, with means settling between 2.85 and 3.15.”
§ Smart Learning Environments > Stage 1: descriptive analysis
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
“They showed an overall agreement perception of the statements regarding their knowledge of ChatGPT (Q2-Q6), with means ranging from 4.15 to 3.8.”
§ Smart Learning Environments > Stage 1: descriptive analysis
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
“The results indicate a strong positive correlation between perceived value and intention to use generative AI, and a weak negative correlation between perceived cost and intention to use.”
§ Smart Learning Environments > Abstract
hedgedcorrelational- 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 use frequency and familiarity with GenAI technologies among participants varied (never = 33.6%;rarely = 22.0%; sometimes = 28.9%;often = 9.6%; always = 5.9%) based on Q1 (”
§ Smart Learning Environments > Stage 1: descriptive analysis
plainn = 405- 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
“The correlations between students' past use frequency (r = 0.339), students' knowledge (r = 0.178), student-perceived cost (r = -0.295) of GenAI, and their intention to use were more moderate but still significant.”
§ Smart Learning Environments > Stage 3: correlations among the variables
plaincorrelationaln = 405 ·- 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 results (see Table 9) suggested a relatively high and positive correlation between student-perceived value (r = 0.606, p < 0.001) and students' intention to use.”
§ Smart Learning Environments > Stage 3: correlations among the variables
hedgedcorrelationaln = 405 ·- 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
“Moreover, students identified utility value in aspects such as increased efficiency, provision of personalized and immediate feedback, and facilitation of idea generation.”
§ Smart Learning Environments > 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
“The three subscales-attainment value (r = 0.587, p < 0.001), intrinsic value (r = 0.459, p < 0.001), and utility value (r = 0.506, p < 0.001)-were also positively correlated with students' intention to use GenAI.”
§ Smart Learning Environments > Stage 3: correlations among the variables
plaincorrelationaln = 405 ·- 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
“Compared with knowledge, the connection between the student-perceived cost of using generating AI and students' intention to use GenAI was stronger, though in a negative way.”
§ Smart Learning Environments > Stage 3: correlations among the variables
plaincorrelationaln = 405 ·- 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. 30
- Probably not a substantial claim. 25
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Students who have used AI applications and have sound knowledge of AI tend to have a positive view of the technology (Chen et al., 2022), thus resulting in high expectancies for success.”
§ Smart Learning Environments > Implications
What the sources say
- Partly supportedcites Chen et al., 2022 sourceExperimental — accuracy not yet measured
The Results (Statistically significant associations; page not provided) report that respondents with prior clinical AI use were more positive about its accuracy and reported better AI knowledge. The Discussion (page not provided) also describes direct users as more positive. These findings concern respondents overall, not specifically students with both prior use and sound knowledge, and do not establish that knowledge itself predicts a positive view. The citation precedes the citing authors’ inference about expectancies for success, so that inference is not assessed here. (LLM, conf=0.85)
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.
It may state two separate claims as one.
- Partly supported
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
“Thus, based on the means of Q22 (mean = 3.92) and Q23 (mean = 3.86), many of the participants believed that the integration of generative AI technologies in higher education will have a positive impact on teaching and learning in the long run and they envision integrating generative AI technologies into their teaching practices in the future.”
§ Smart Learning Environments > Stage 1: descriptive analysis
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 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
“Additionally, they also recognized the value (Q7-Q17) of ChatGPT in various teaching, learning, and working occasions; except for Q11 (”
§ Smart Learning Environments > Stage 1: descriptive analysis
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.
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 majority of participants acknowledged the potential advantages of GenAI in the workplace and its capacity to enhance learning outcomes, encompassing the improvement of academic performance and the development of digital competence.”
§ Smart Learning Environments > 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.
It may state two separate claims as one.
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
“As shown in Table 7, though some of the AVE values are lesser and closer to 0.50, yet with the CR value of the knowledge scale, value scale, and cost scale as 0.822, 0.763, and 0.873 accordingly, which exceed the cutoff point of 0.60, it may be able to conclude that the convergent validity is acceptable (Fornell & Larcker, 1981).”
§ Smart Learning Environments > Stage 2: reliability and validity of the scales
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: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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 findings from Table 7 demonstrates that EVT-related factors, such as knowledge, perceived value (including attainment, intrinsic, and utility values), and perceived cost, are all significantly correlated with students' intention to use GenAI in higher education.”
§ Smart Learning Environments > Discussion
boostedcorrelational- 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 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 perceived value has the strongest positive correlation with intention to use, while the perceived cost has a weak negative correlation.”
§ Smart Learning Environments > 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.
It may state two separate claims as one.
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
“As shown in the responses to the questionnaire, students were concerned that the use of GenAI could undermine the value of a university education, deprive them of the opportunities to interact with others, and hinder the development of transferable skills.”
§ Smart Learning Environments > 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: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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
“TLI of perceived value (= 0.948) are less than 0.95, yet still higher than 0.90 and its CFI is higher than 0.95.”
§ Smart Learning Environments > Stage 2: reliability and validity of the scales
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 values of HTMT ratios (see Table 8) further denote good discriminant validity, as all the values range from 0.660 to 0.002, lower than the threshold of 0.85 (Henseler et al., 2015).”
§ Smart Learning Environments > Stage 2: reliability and validity of the scales
What the sources say
- Not checked yetcites Henseler et al., 2015 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.
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
“Cronbach's alphas for the 5 knowledge, 11 perceived value, and 4 perceived cost items are 0.812, 0.876, and 0.746.”
§ Smart Learning Environments > Stage 2: reliability and validity of the scales
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
“The missing data were imputed by the multiple imputation procedure in SPSS.”
§ Smart Learning Environments > Rationale for analyses
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.
None of the sources cited for this claim have been checked yet.
In the paper
“EVT posits that individual's motivation to engage in a behaviour is influenced by their expectations of success and the value they place on the behaviour (Wigfield, 1994;Wigfield & Eccles, 2000), working as a framework to examine students' intentions and perceptions from knowledge, perceived value, and perceived cost of the technology in the early stage.”
§ Smart Learning Environments > Introduction
What the sources say
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.
Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“The validity of constructs was measured by Average variance extracted (AVE), composite reliability (CR) (Fornell & Larcker, 1981;Hair et al., 2015), and Heterotrait-monotrait (HTMT) ratio (Henseler et al., 2015).”
§ Smart Learning Environments > Rationale for analyses
What the sources say
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
“Thus, based on the means of Q22 (mean = 3.92) and Q23 (mean = 3.86), many of the participants believed that the integration of generative AI technologies in higher education will have a positive impact on teaching and learning in the long run and they envision integrating generative AI technologies into their teaching practices in the future.”
§ Smart Learning Environments > Stage 1: descriptive analysis
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.
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
“They did have a certain level of understanding and concerns over the limitations of generative AI technology in terms of handling complex tasks.”
§ Smart Learning Environments > Stage 1: descriptive analysis
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.
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
“Meanwhile, the participants recognized the technology's attainment value such as improving efficiency, and its foreseeable utility value in the workplace as well as the long-term impacts on the learning outcomes (for example, academic performance, creativity-and emotionrelated competencies).”
§ Smart Learning Environments > Stage 1: descriptive analysis
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
“The questionnaire was modified based on the feedback received during the pilot study to ensure its accuracy and clarity.”
§ Smart Learning Environments > Instrument for analyses
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: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
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 that the questionnaire items were relevant and clear, pilot studies were conducted before the formal data collection.”
§ Smart Learning Environments > Instrument for analyses
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.
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
“As summarized by Table 4, S.D., skewness, and kurtosis values indicate a normal distribution of the dataset, which allows further calculations in stage 2 and stage 3.”
§ Smart Learning Environments > Stage 1: descriptive analysis
hedgeddirect- 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
“As shown in Table 5, the Cronbach alpha values are all greater than or closer to 0.7, indicating an acceptable internal consistency within the three scales.”
§ Smart Learning Environments > Stage 2: reliability and validity of the scales
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: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The expectancy-value framework has been widely used in educational research to examine students' motivation, learning, and achievement (Cheng et al., 2020;Sin et al., 2022).”
§ Smart Learning Environments > Expectancy-value theory and other frameworks
What the sources say
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
“Previous research has also shown a positive correlation between students' knowledge of technology and their intention to use it such as via the UTAUT model (Venkatesh et al., 2003).”
§ Smart Learning Environments > Expectancy-value theory and other frameworks
What the sources say
- Not checked yetcites Venkatesh et al., 2003 source
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: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Moreover, EVT also emphasizes the role of students' knowledge and familiarity with GenAI, which is an essential aspect of this study's research questions.”
§ Smart Learning Environments > Why expectancy-value theory?
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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. 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
“Further, most of the items were constructed to reflect issues and challenges associated with GenAI use within the EVT framework.”
§ Smart Learning Environments > Instrument for analyses
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.
Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“The questionnaire was specifically designed for the purposes of this study.It was informed by a review of relevant literature and existing questionnaires on teachers' and students' perceptions of educational technologies based on the EVT framework (e.g., Backfisch et al., 2021aBackfisch et al., , 2021b;;Ball et al., 2019;Chen, 2011;Ranellucci et al., 2020).”
§ Smart Learning Environments > Instrument for analyses
What the sources say
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
“Regarding the validation stage (i.e.stage 2), due to the strong theoretical basis of EVT, it was decided to use only Confirmatory Factor Analysis (CFA) without using Exploratory Factor Analysis (EFA), following the calculation of Cronbach's alpha.”
§ Smart Learning Environments > Rationale for analyses
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.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“the EVT presents both expectancy (self-efficacy) and perceived value as having a direct impact on technology adoption in a concurrent manner (Backfisch et al., 2021a(Backfisch et al., , 2021b)).”
§ Smart Learning Environments > Literature on student's perception of AI and GenAI
What the sources say
- Not checked yetcites Backfisch et al., 2021a
- Not checked yetcites Backfisch et al., 2021b
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.
Probably not a substantial claim.
In the paper
“While Dahlkemper et al (2023) showed that students who had prior experience with AI tended to have a positive attitude towards AI, our study was able to demonstrate a significant relationship between students' frequency of GenAI use and their intention to use GenAI tools.”
§ Smart Learning Environments > Discussion
What the sources say
- Not checked yetcites Dahlkemper et al (2023)
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: 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
“Previous research has shown that perceived barriers, such as cost, can negatively affect students' intentions to use technology in education (Chan & Hu, 2023;Flake et al., 2015;Regmi & Jones, 2020;Stüber, 2018).”
§ Smart Learning Environments > Expectancy-value theory and other frameworks
What the sources say
- Not checked yetcites Chan & Hu, 2023
- Not checked yetcites Flake et al., 2015 source
- Not checked yetcites Regmi & Jones, 2020
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: 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
“According to Brown (2006), CFA is a hypothesis-driven method that allows for direct testing of the proposed factor structure.”
§ Smart Learning Environments > Rationale for analyses
What the sources say
- Not checked yetcites Brown, 2006
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. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“EVT is considered more suitable for this study because it takes into account the perceived value and cost associated with using GenAI, which are critical factors in determining students' intentions to use such technology.”
§ Smart Learning Environments > Why expectancy-value theory?
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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
- Rests on an assumption: The concept of cost is either overlooked or underdeveloped in models such as TAM and UTAUT.
None of the sources cited for this claim have been checked yet.
In the paper
“Studies have shown that perceived usefulness and perceived ease of use are significant determinants of technology acceptance (Davis, 1989;Teo, 2009;Venkatesh et al., 2003).”
§ Smart Learning Environments > Expectancy-value theory and other frameworks
What the sources say
boosteddirect- 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
“Similarly, Gado et al. (2022) and Hu (2022) found that perceived usefulness of AI had a significant relationship with students' intention to use AI.”
§ Smart Learning Environments > Discussion
What the sources say
- Not checked yetcites Gado et al. (2022) source
- Not checked yetcites Hu (2022)
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: 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
“This finding resonates with Kim et al. 's (2020) and Hu's (2022) studies that students' perceived ease of use and perceived usefulness of AI tools positively affect their intention to use the tools.”
§ Smart Learning Environments > Discussion
What the sources say
- Not checked yetcites Kim et al. 's (2020) source
- Not checked yetcites Hu's (2022)
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: 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
“Moreover, the use of CFA in this study can be considered a parsimonious approach, ensuring that our research findings are concise and easier to interpret.”
§ Smart Learning Environments > Rationale for analyses
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check 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. It may be too vague to check against evidence. 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
“Furthermore, the study primarily relied on self-reported data, which may be subject to response biases, such as social desirability or recall bias.”
§ Smart Learning Environments > Limitation
hedgedconditional- 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
“Additionally, the study was conducted at a single point in time, and thus, it may not account for potential changes in students' attitudes and perceptions towards GenAI over time.”
§ Smart Learning Environments > Limitation
hedged- 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.
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 EVT-based instrument addresses this gap in the literature and provides a strong foundation for future research and practice in this area.”
§ Smart Learning Environments > Implications
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check 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. It may be too vague to check against evidence. It may state two separate claims as one.
- Rests on an assumption: There is a lack of robust, theoretically grounded instruments to assess students' attitudes towards GenAI adoption.
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, given the strong theoretical basis, prior research, and focus on hypothesis testing, using only CFA in this study can be considered a justifiable decision.”
§ Smart Learning Environments > Limitation
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
“It is essential to acknowledge that skipping EFA in the validation can have some limitations, such as the potential to overlook alternative factor structures or issues with item loadings (Fabrigar et al., 1999).”
§ Smart Learning Environments > Limitation
What the sources say
- Not checked yetcites Fabrigar et al., 1999 source
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.
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
“Firstly, the sample size was restricted to 405 participants, which may not be fully representative of the larger student population.”
§ Smart Learning Environments > Limitation
hedgedn = 405- 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
“Knowledge, a key component of AI literacy, can increase students' confidence and readiness in using GenAI (Ng et al., 2021).”
§ Smart Learning Environments > Implications
What the sources say
- Not checked yetcites Ng et al., 2021
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: 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.
In the paper
“Wigfield (1994) refers to expectancy and value as motivational constructs that determine an individual's decision to perform and persist in tasks.”
§ Smart Learning Environments > Implications
What the sources say
- Not checked yetcites Wigfield (1994) source
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.
Probably not a substantial claim.
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
“This study offers a validated instrument for measuring students' perceptions of GenAI, which can be used by researchers for further studies of GenAI adoption.”
§ Smart Learning Environments > Conclusion
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.
In the paper
“Since χ 2 is very sensitive to sample size and its p-value will tend to be small in a big sample, we decided to report the χ 2 /df ratio instead as recommended by Schermelleh-Engel et al. (2003).”
§ Smart Learning Environments > Stage 2: reliability and validity of the scales
What the sources say
- Not checked yetcites Schermelleh-Engel et al. (2003)
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.
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
“We conducted our study with full adherence to ethical principles and guidelines for human subject research, and we obtained approval from the Human Research Ethics Committee (HREC) at the University of Hong Kong.All participants provided informed consent, and we did not report any personal identifiers in the study.(Ethical Approval No.: EA230079).”
§ Smart Learning Environments > Additional information
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 paper relies on this without support.
The adaptation of GenAI in higher education is still in the exploratory stage.
“Since the adaptation of GenAI in higher education is still in the exploratory stage, past research on AI cannot fully illustrate students' perceptions in GenAI-specific context.”
The paper relies on this without support.
No currently available questionnaires measure students' perceptions of GenAI based on the EVT framework.
“Since there are no currently available questionnaires on students' perceptions of GenAI which are based on the EVT framework, some of the questionnaire items in this study were adapted from other similar instruments.”
The paper relies on this without support.
There has been limited research into the predictive utility of EVT in relation to students' attitudes and intention towards GenAI use.
“Although the EVT has been used in many areas, there has been limited research into the predictive utility of this framework in relation to students' attitudes and intention towards GenAI use.”
The paper relies on this without support.
The concept of cost is either overlooked or underdeveloped in models such as TAM and UTAUT.
“In addition, the concept of”
The paper relies on this without support.
There is a lack of robust, theoretically grounded instruments to assess students' attitudes towards GenAI adoption.
“To date, there has been a lack of robust, theoretically grounded instruments to assess students' attitudes towards GenAI adoption, making it challenging to systematically understand the factors that influence their intention to use the technologies.”
The paper relies on this without support.
There is an urgent need for research into students' acceptance and willingness to adopt GenAI.
“To achieve this goal, students' acceptance and willingness to adopt GenAI are crucial and there is an urgent need for research into this area.”
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?
The cited source says what the claim attributes to it.
In the citing paper
“GSE-6AI30, Hwang et al.’s instrument39, and Chan & Zhou’s EVT based instrument (knwl. of gen. AI subscale)37 are also promising instruments with good evidence for structural validity and internal consistency, however, their content validity needs to be checked on the higher-education students.”
From A systematic review of AI literacy scales (2024) · cites this paper as [37]
The cited source says what the claim attributes to it.
In the citing paper
“GSE-6AI30, Hwang et al.’s instrument39, and Chan & Zhou’s EVT based instrument (knwl. of gen. AI subscale)37 are also promising instruments with good evidence for structural validity and internal consistency, however, their content validity needs to be checked on the higher-education students.”
From A systematic review of AI literacy scales (2024) · cites this paper as [37]
The cited source says what the claim attributes to it.
In the citing paper
“GSE-6AI30, Hwang et al.’s instrument39, and Chan & Zhou’s EVT based instrument (knwl. of gen. AI subscale)37 are also promising instruments with good evidence for structural validity and internal consistency, however, their content validity needs to be checked on the higher-education students.”
From A systematic review of AI literacy scales (2024) · cites this paper as [37]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“The literature strongly supports the shift toward authentic assessments, tasks that are complex, context-rich, and reflective of real-world scenarios as a way to make AI-generated responses less relevant or useful (Chan & Zhou, 2023; Haroud & Saqri, 2025; Ozfidan et al., 2024).”
From Six Institutional Intervention Areas to Support Ethical and Effective Student Use of Generative AI in Higher Education: A Narrative Review (2026) · cites this paper as Chan & Zhou, 2023
Experimental — accuracy not yet measured
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 major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
§ Smart Learning Environments > Stage 2: reliability and validity of the scales
“the CR value of the knowledge scale, value scale, and cost scale as 0.822, 0.763, and 0.873 accordingly”
§ Smart Learning Environments > Conclusion
“| Knowledge | 5 | 0.833 | 0.506 |”
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
§ Smart Learning Environments > Stage 2: reliability and validity of the scales
“the CR value of the knowledge scale, value scale, and cost scale as 0.822, 0.763, and 0.873 accordingly”
§ Smart Learning Environments > Conclusion
“| Perceived cost | 4 | 0.777 | 0.467 |”
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
§ Smart Learning Environments > Discussion
“The findings from Table 7 demonstrates that EVT-related factors, such as knowledge, perceived value (including attainment, intrinsic, and utility values), and perceived cost, are all significantly correlated with students' intention to use GenAI in higher education.”
§ Smart Learning Environments > Conclusion
“Table: 7 Table 7 Convergent validity results (N = 405)”
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
§ Smart Learning Environments > Literature on student's perception of AI and GenAI
“Perceived usefulness, perceived social norm, and attitude towards AI were shown to predict intention to use AI; however, perceived ease of use was found to have no significant influence on intention to use.”
§ Smart Learning Environments > Expectancy-value theory and other frameworks
“intention to use AI was not significantly affected by perceived usefulness”
Citations
Cites
Evaluating Structural Equation Models with Unobservable Variables and Measurement Error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.2307/3151312 · Cited by 109,314
Evaluating structural equation models with unobservable variables and measurement error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800104 · Cited by 109,190
Hu, L., & Bentler, P. M. (1999) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705519909540118 · Cited by 108,788
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483
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
A 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
Confirmatory factor analysis for applied research
Brown, T. A. (2007) · Choice Reviews Online · doi:10.5860/choice.44-2769 · Cited by 15,621
(1998) · DigitalCommons - Kennesaw State University (Kennesaw State University) · Cited by 13,997
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450
Evaluating the use of exploratory factor analysis in psychological research.
Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999) · Psychological Methods · doi:10.1037/1082-989x.4.3.272 · Cited by 9,587
Research works printed but not linked: 4
- Chan, C. K. Y. (2023b). Is AI changing the rules of academic misconduct? An in-depth look at students' perceptions of 'AI-giarism'. (Under review).
- Mok, L. (2023). Hong Kong Education University approves use of chatgpt in coursework despite bans by two other schools. Hong Kong Free Press HKFP. https://hongkongfp.com/2023/03/24/hong-kong-education-university-approves-use-of-chatgpt-in-coursework-despite-bans-by-two-other-schools/
- Russell, S. J., & Norvig, P. (2016). Artificial intelligence: A modern approach. Pearson.
- Stüber, J. (2018). Barriers of Digital Technologies in Higher Education: A Teachers’ Perspective from a Swedish University [Mater Thesis, Linnaeus University] diva-portal. https://www.diva-portal.org/smash/get/diva2:1201871/FULLTEXT01.pdf.
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Murshidi, G. A., Shulgina, G., Kapuza, A., & Costley, J. (2024) · Figshare · doi:10.6084/m9.figshare.26757736
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Hınız, G. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7569-4.ch007
Development and Validation of an AI Literacy Diagnostic Tool for Middle School Students
Chung, K., Kim, S., Lee, J., Lee, C., Noh, H., Kwon, Y., & Kim, H. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2595309
Model of Acceptance of Artificial Intelligence Devices in Higher Education
Salazar, L., & Rivera, L. (2026) · Computers · doi:10.3390/computers15010046
Modes of Assessment in Pre-Calculus Students
Eltoum, R. A., Abdelsalam, H., & Pilotti, M. (2025) · IEEE International Conference on Emerging Trends in Engineering and Computing (ETECOM) · doi:10.1109/etecom66111.2025.11319131
A Scientometric Analysis of Generative AI in the Educational Context
Vázquez, J. P. G., Astorga-Vargas, M. A., Corral, L. E. V., Zatarain-Aceves, H., & Vega, J. E. S. (2026) · Springer eBooks · doi:10.1007/978-981-95-4871-2_18
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Khan, S., Ramli, A. F., & Yawadi, M. (2025) · International Journal of Law Government and Communication · doi:10.35631/ijlgc.1042016
Zwischen Algorithmus und Akzeptanz
Schmidtner, M., Egger, C., & Fox, C. (2026) · Springer eBooks · doi:10.1007/978-3-658-46709-8_111-1
Generative AI Use Among Slovenian Lower Secondary Students: Use Patterns and Attitudes
Arcet, B., Dolenc, K., & Kranjec, E. (2026) · Applied Sciences · doi:10.3390/app16052539
University Instructor’s Generative AI Perceptions and Use
Oyarzun, B. A., Kim, S. Y., Bong, J. Y., & Maxwell, D. (2026) · American Journal of Distance Education · doi:10.1080/08923647.2026.2642490
Ng, J. W., & Naval, M. A. (2025) · International Conference on Artificial Intelligence and Teacher Education (ICAITE) · doi:10.1109/icaite68636.2025.11442478
Lo, D., Chon, L., & Chen, J. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-3638-1.ch011
Hong, S., & Lee, S. W. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791002
Jiang, M., Tang, S., & Wang, Q. N. (2026) · Sustainability · doi:10.3390/su18094278
Mohale, N. E. (2026) · Studies in Learning and Teaching · doi:10.46627/silet.v7i1.753
Liu, X., & Yodmongkol, P. (2026) · Journal of Education for Students Placed at Risk (JESPAR) · doi:10.1080/10824669.2026.2661009
Lorenzo, G., Cerdán-Chacón, A., & Lorenzo-Lledó, . (2025) · De Gruyter eBooks · doi:10.1515/9783112206393-009
Extending UTAUT for Blockchain Adoption in Power Plant Asset Management: Towards UTAUT4-BPP
Arifin, M., Kusrini, E., Cahyo, W. N., & Widodo, I. D. (2025) · International Conference on Decision Aid Sciences and Applications (DASA) · doi:10.1109/dasa68193.2025.11499051
Cheng, L. (2026) · Advances in Psychology · doi:10.12677/ap.2026.165239
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Bulut, A., Gökhan, A. B. A., Mutlu, C., Guven‐Uslu, P., & Çalışkan, R. E. (2026) · Measurement Interdisciplinary Research and Perspectives · doi:10.1080/15366367.2026.2665784
Expectations and willingness of maritime english students toward ICT-integrated textbooks
Rahmawati, Y., Fitriati, S. W., Williyan, A., & Mutiaraningrum, I. (2026) · Asian-Pacific Journal of Second and Foreign Language Education · doi:10.1186/s40862-026-00406-8
Gonzales, G. G., Hallarte, D. K., Milano, M. L., Pasaje, S. J., Villaflor, M. G., & Gonzales, R. R. (2026) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2026.102921
Kwon, S.-H., Kim, Y., & Park, S.-H. (2026) · Journal of Korean Gerontological Nursing · doi:10.17079/jkgn.2025.00255
Chatterjee, R. S., Hameed, I., Chaudhuri, S., & Chatterjee, S. (2026) · Strategy and Leadership · doi:10.1108/sl-03-2026-0125
Li, D., & Li, H. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2026.2684742
Setiyawan, A., Soeharto, S., Wijaya, T. T., & Lavicza, Z. (2026) · Technology Knowledge and Learning · doi:10.1007/s10758-026-10016-5
Kara, M., & Yurt, E. (2026) · Current Psychology · doi:10.1007/s12144-026-09795-y
İyilikci, E. A., Yurt, H. M., Keysan, E. E., Ağaoğulları, N., Dallar, H., Güven, L. S., Özkan, M. S., Şahin, M., Günhan, Z., Baykuşak, E., et al. (2026) · Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi · doi:10.35379/cusosbil.1647141 · Cited by 1
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Onbaşılı, Ü. İ., Batı, K., Yetişir, M. İ., & Doğan, C. D. (2026) · Educational Studies · doi:10.1080/03055698.2026.2710334
Koca, M., & Büyükmıhçı, G. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1861972
Filippo, F., Lagrasta, F. P., Lisi, S., & Scozzi, B. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-23684-5_46
Elepaño, M. A. D., Manimtim, L., Buendia, B. D., Pasahol, I. S., Abel, J. V., & Sargento, J. U. (2026) · International Conference on e-Education, e-Business, e-Management and e-Learning · doi:10.1109/ic4e69618.2026.11631602
Kilat, R. V., Valle, L. C., Cruz, J. D., & Lim, J. B. (2026) · Discover Education · doi:10.1007/s44217-026-01963-3
Schmidtner, M., Egger, C., & Fox, C. (2026) · Springer eBooks · doi:10.1007/978-3-658-51525-6_111
Yang, T., Li, M., Liang, J., Wei, C., & Liu, B. (2026) · International Conference on Computer Science and Technologies in Education (CSTE) · doi:10.1109/cste69562.2026.11649695
Singh, M. I., Pradhan, S., Naveen, S., & Vinod, L. (2026) · Discover Education · doi:10.1007/s44217-026-02090-9
Peng, Z., Wang, G., Ma, H., Qiu, H., & Chen, H. (2026) · International Conference on Distance Education and Learning (ICDEL) · doi:10.1109/icdel70250.2026.11663860
Wang, G., Peng, Z., Qiu, H., Ma, H., & Chen, H. (2026) · International Conference on Distance Education and Learning (ICDEL) · doi:10.1109/icdel70250.2026.11663875
Kumar, V., Anand, A., Butler, R., & Bista, K. (2026) · AI in Education · doi:10.3390/aieduc2030031
García-López, I. M., & Guillén-Yparrea, N. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-37438-7_8
Hu, P. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1900190 · Cited by 1
An, B., Zhu, C. W., & Dong, H. (2026) · International Journal of Technology and Design Education · doi:10.1007/s10798-026-10113-7
Anonymous, Xu, W., Qi, Y., & Lu, D. (2026) · Physical Review Physics Education Research · doi:10.1103/62m6-lkh7
Kim, S. Y., Bong, J. Y., Maxwell, D. W., & Oyarzun, B. A. (2026) · Educational Technology Research and Development · doi:10.1007/s11423-026-10713-z · Cited by 1
Schmid, J. M., Lhotzky, J. F., Veith, J. M., & Bitzenbauer, P. (2026) · Eurasia Journal of Mathematics Science and Technology Education · doi:10.29333/ejmste/19400
Fathi, Z., Zarei, P., Peimani, P., SOLEIMANI, I., Fong, C. J., Baquet, Z., & Maku, S. O. (2026) · Journal of Computing in Higher Education · doi:10.1007/s12528-026-09504-5
Wang, X., Yu, W., 周佳穎, Qiao, Y., & Chen, Z. (2026) · Sustainability · doi:10.3390/su18199979
Gu, L. C., Gao, X., Li, R., Liu, Z., & Li, Y. (2026) · International Journal of Technology and Design Education · doi:10.1007/s10798-026-10128-0
Liu, G., & Hou, C. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1946110
Li, Y., Yang, Y., & Bao, S. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1975841
Fung, K. Y. (2026) · Innovation in Language Learning and Teaching · doi:10.1080/17501229.2026.2740151
Exploring undergraduate experiences with AI tools in higher education
Özfidan, B., Luaces, S., Reyes, C., & Verna, N. (2026) · International Journal of Information and Learning Technology · doi:10.1108/ijilt-11-2025-0348
Bueno, W. T. P., Huacho, M. E. R., Flores, L. O. I. S., & Palacios, J. A. (2026) · Revista Tribunal · doi:10.59659/revistatribunal.v6i17.564
Capability, opportunity and motivation in university students’ generative AI usage
Opoku‐Mensah, E., Ankrah, S. T., Agyapong, R. A., & Tuffour, P. (2026) · Educational Studies · doi:10.1080/03055698.2026.2751074
de Zúñiga, H. G., Yadav, D., & Jang, E. (2026) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2026.100417
Metadata
- Authors
- Chan, C. K. Y., & Zhou, W.
- Affiliations
- University of Hong Kong (HK)
- Corresponding author
- Chan, Cecilia Ka Yuk
- Venue
- Smart Learning Environments
- Published
- 2023-12-07
- Volume
- 10
- Issue
- 1
- ISSN
- 2196-7091 (ISSN-L)
- OpenAlex
- W4389438662
- Semantic Scholar
- 775889071de3cc3d403054736b1307d578392418
- Cited by
- 192
- References
- 66
- Citations by year
- 2026: 75 · 2025: 71 · 2024: 45 · 2023: 1
- FWCI
- 45.45
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Smart Learning Environments (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- generative AI, student perceptions, intention to use, expectancy-value theory, perceived value, perceived cost, technology acceptance, higher education, scale validation
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 2026-10-08, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-08 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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