Aug 2026· International Journal of Technology in Education· pp. 1198-1213· 0 citations
TL;DR
The study concludes that person-centered profiling offers a practical framework for understanding and supporting responsible AI integration in higher education and argues that implementation challenges can be understood more effectively through user segments.
Abstract
This study examines the transition from intention to routine use in ChatGPT adoption in higher education from a person-centered perspective. A total of 351 valid responses from students and instructors were analyzed following data screening procedures. Although previous research on generative AI adoption has largely relied on variable-centered approaches such as UTAUT2 to explain average relationships among constructs, the present study argues that implementation challenges can be understood more effectively through user segments. Using k-means clustering on standardized UTAUT2 driver constructs, performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit, three adoption profiles were identified: Capability-Ready Explorers, Routine Adopters, and Skeptical/Detached Users. To interpret differences among these groups more precisely, the study introduces two conversion indicators: the Capability-to-Habit Discrepancy Index (CHDI) and the Intention-to-Use Residual (IUR). Findings show that the largest group, Capability-Ready Explorers, demonstrates relatively high perceived ease of use and support but low habit and only moderate use frequency, indicating a gap between readiness and routinized practice. Variation across faculties and school years further suggests that ChatGPT adoption is shaped by disciplinary and developmental context. The discussion integrates UTAUT2, habit formation theory, and self-regulated learning to argue that successful institutional adoption depends not only on promoting positive attitudes, but also on designing repeatable, legitimate, and accountable routines of use. The study concludes that person-centered profiling offers a practical framework for understanding and supporting responsible AI integration in higher education.
Investigating how individuals form a sense of identity around ChatGPT finds that regular interaction with ChatGPT is associated with the development of a distinct IT identity, and the presence of sufficient resources and organizational or contextual support significantly moderates this relationship, shaping user behaviors in meaningful ways.
Sofia Godinho, Tiago Oliveira, Catarina Neves· Journal of Enterprise Inform...· 0 citations
This study examines ChatGPT adoption among higher education students in Türkiye and Poland through an extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework that also includes personal innovativeness. It compares country-specific adoption patterns across the two samples and offers a cautious contextual interpretation of the observed differences. Survey data were collected from 634 students in Türkiye and 531 students in Poland, and the proposed relationships were examined using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that the extended UTAUT2 framework was empirically useful in both samples, although the relative prominence of specific predictors differed across them. Descriptively, Performance Expectancy (β = 0.258,
p
< 0.001) and Personal Innovativeness showed larger coefficients in the Polish sample, whereas Effort Expectancy (β = 0.104,
p
< 0.01) and Social Influence (β = 0.199,
p
< 0.001) appeared more prominent in the Turkish sample. Price Value showed a statistically significant reversed association with Behavioral Intention in Türkiye (β = -0.187,
p
< 0.001), whereas the association was positive in Poland (β = 0.094,
p
< 0.01). Habit emerged as one of the strongest predictors in the model and was positively associated with both Behavioral Intention and Use Behavior in the two samples. Taken together, the findings suggest that ChatGPT adoption in higher education is shaped by a combination of perceived usefulness, ease of use, social and practical support, habitual use, and context-sensitive evaluations of value. The study contributes to the growing literature on generative AI in higher education by showing that the same theoretical framework can reveal different adoption patterns across two national samples, while also highlighting the need for context-sensitive implementation strategies. The findings offer practical implications for higher education institutions, educators, and policy developers seeking to support the responsible and effective use of ChatGPT.
Ali İbrahim Can Gözüm, Artur Strzelecki, Ümit Ünsal Kaya et al.· Information Systems Frontier...· 0 citations
Generative artificial intelligence (AI) tools such as ChatGPT are increasingly integrated into higher education, yet research emphasizes students’ intentions to adopt these tools rather than their actual use. This study examined whether behavioral intention predicts self-reported usage frequency. Using secondary survey data from higher-education participants (N = 751), a three-item behavioral intention composite and a 0–7 usage-frequency index were analyzed using descriptive statistics, Pearson correlation, and simple linear regression. Mean intention was M = 3.35 (SD = 0.91) and mean frequency was M = 3.22 (SD = 2.10). Behavioral intention correlated positively with usage frequency, r = .49, p < .001, and significantly predicted use, B = 1.12, SE = 0.07, β = .49, R² = .239, F (1, 748) = 235.04, p < .001. Findings support TAM and UTAUT while suggesting that institutional supports are needed to translate intention into sustained and responsible AI use in higher education.
Brandon Hester· American Journal of STEM Edu...· 0 citations
This study examines adult learners’ responsible use of ChatGPT for academic writing by integrating technology acceptance with ethical awareness. Motivated by unresolved questions about over-reliance, plagiarism, and authenticity, it extends the Unified Theory of Acceptance and Use of Technology (UTAUT) to account for ethical factors shaping intention and behavior. Using a sequential explanatory mixed-methods design, we surveyed 285 adult learners from Philippine higher education and conducted follow-up interviews with eight frequent ChatGPT users. Measurement results (PLS-SEM) showed satisfactory reliability and validity; structural paths indicated that performance expectancy, social influence, and facilitating conditions significantly predict behavioral intention, while effort expectancy does not; intention significantly predicts actual use. Thematic analysis revealed ethically oriented practices (e.g., paraphrasing, citation, originality checks) and peer-reinforced norms that regulate responsible adoption. We propose ethical awareness as an added construct and potential moderator of social influence—within UTAUT to better explain AI use in academic contexts. Findings inform guidelines and training that balance efficacy with integrity, and recommend policies that ensure equitable access. Future work should test extended models including trust, critical-thinking competence, and broader digital literacy, ideally through longitudinal designs to observe how adoption and ethics co-evolve.
Mary Rose Martinez, Agnes Bacani· Diversitas Journal· 0 citations
This study investigated how college students’ use of ChatGPT has evolved from a tool for simply getting answers to one that supports motivation and learning engagement. Using a novel survey instrument based on Self-Determination Theory, we measured six types of motivation, three intrinsic, two extrinsic, and amotivation, across two cohorts of students (2023 and 2025). Results revealed statistically significant increases in both intrinsic and extrinsic motivation over time. While early use of ChatGPT focused on convenience and task completion, students increasingly reported using it to overcome mental blocks, build momentum, and stay engaged with their academic work. Follow-up analyses of repeat participants and comparisons by class standing suggest these trends reflect broader shifts in student engagement, not just cohort or experience differences. Cluster analysis revealed distinct motivational profiles, highlighting variation in how students incorporate ChatGPT into their academic routines. These findings suggest that as generative artificial intelligence becomes more familiar, accessible, and sophisticated, students are integrating it more deeply into their learning processes, not just for answers, but for sustained motivation and support.
Jayne Ann Harder, C. E. Klehm, A. Lang et al.· Artificial Intelligence and...· 0 citations
This study aims to compare the explanatory power of three influential theoretical frameworks in post-adoption research for understanding users’ continuance intention toward ChatGPT Plus. The three models are the Technology Acceptance Model (TAM), the Expectation Confirmation Model (ECM) and an extended ECM (EECM) that integrates core belief and evaluative constructs from both TAM and ECM.
Data were collected from 333 active ChatGPT Plus subscribers and analyzed using structural equation modeling. Three separate models were specified and assessed using IBM SPSS and AMOS 31. Their performance was compared based on model fit indices and predictive validity. Each model’s explanatory power was evaluated through R² values for continuance intention.
All three models showed acceptable goodness-of-fit. EECM showed the highest explained variance, while TAM showed the strongest parsimony based on information criteria. In terms of predictive validity, the EECM accounted for the largest proportion of variance in continuance intention at 69.50%, exceeding the explanatory levels of TAM (50.40%) and ECM (69.30%). These results show that while TAM remains a strong and parsimonious predictor of user continuance, the EECM provides the most comprehensive explanation of ChatGPT Plus post-adoption behavior, and ECM performs adequately but less effectively relative to the other two models.
To the best of the author’s knowledge, this study is one of the earliest comparative examinations of TAM, ECM, and EECM in the context of GenAI subscription services. By empirically demonstrating the added value of combining pre-adoption beliefs with post-adoption evaluations, the findings of this study contribute to theoretical work on technology continuance and offer practical insights for improving user retention in AI-driven subscription services.
