Examination of college students’ continuance intention to use AI educational tools in local undergraduate universities across five northwestern Chinese provinces and a capability-centric extension of the unified theory of acceptance and use of technology (UTAUT) are developed.
Abstract
Purpose Generative artificial intelligence (AI) is increasingly used in higher education, yet students’ continued use of AI educational tools may operate differently in resource-constrained university contexts. This study examines college students’ continuance intention to use AI educational tools in local undergraduate universities across five northwestern Chinese provinces and develops a capability-centric extension of the unified theory of acceptance and use of technology (UTAUT). Methods A sequential explanatory mixed-methods design was adopted. After pilot-based construct purification, 703 valid survey responses from students with prior AI tool experience were analyzed using partial least squares structural equation modeling (PLS-SEM). Additional analyses included common method bias diagnostics, PLSpredict, cross-validated predictive ability testing, multi-group analysis, and post-hoc power analysis. Semi-structured interviews with 25 students were used to contextualize the quantitative findings. Results Self-efficacy was the strongest predictor of continuance intention, followed by performance expectancy, perceived policy signals, and social influence. Algorithmic trust showed only a weak positive association, whereas effort expectancy showed a weak but significant negative association, contrary to the original UTAUT prediction. Qualitative findings indicated that students perceived AI tools as easy to access but cognitively demanding to use effectively, due to prompt refinement, factual verification, error correction, and task adaptation. Conclusion Continued AI educational tool use in resource-constrained universities depends less on surface-level ease of use and more on students’ perceived capability to manage the full AI-assisted learning workflow. This study extends UTAUT-based AI adoption research by foregrounding self-efficacy and proposing a capability-centric perspective on generative AI continuance in higher education.
Background. Generative artificial intelligence (GenAI) tools are increasingly integrated into higher education, yet the factors driving their adoption among pre-service teachers remain underexplored in Southeast Asian contexts.
Objectives. This study investigated predictors of generative AI use among 118 pre-service science teachers at a state college in Masbate, Philippines. Drawing on the Technology Acceptance Model, the Unified Theory of Acceptance and Use of Technology, and the Theory of Planned Behavior, five constructs were examined: Perceived Usefulness, Attitude Toward AI, Effort Expectancy, Facilitating Conditions, and Subjective Norms.
Materials and method. A quantitative descriptive-predictive design with stratified random sampling was used. Data were collected using a validated 32-item instrument and analyzed using four multiple linear regression models.
Results. Generative AI use was rated high (M = 3.67, SD = 0.72). Perceived Usefulness (M = 3.79), Attitude Toward AI (M = 3.71), and Effort Expectancy (M = 3.75) were rated high, while Facilitating Conditions (M = 3.44) and Subjective Norms (M = 3.35) were rated moderate. Across all four models (F = 50.47 to 67.14, p < .001), Perceived Usefulness was the sole predictor consistently significant at p < .001 (β = 0.443 to 0.479), explaining 63.9 to 64.6% of the variance (R² = 0.639 to 0.646). Attitude Toward AI and Effort Expectancy reached significance in selected model configurations. Multicollinearity was within acceptable limits (VIF = 1.40 to 4.76).
Conclusion. These correlational findings, drawn from a single institutional sample, point to the primacy of perceived usefulness in technology adoption. As a hypothesis for future confirmation, teacher education programs may benefit from demonstrating the practical value of generative AI in science instruction.
RP Reel Acosta, Liza Angel Almanzor, Irish Cierto et al.· Journal of Learning Theory a...· 0 citations
A moderated serial mediation model in which AI tool use predicts willingness to communicate sequentially through AI self-efficacy and foreign language enjoyment (FLE) is proposed and tested, with foreign language anxiety (FLA) moderating the FLE-to-WTC link.
The rapid development of generative AI (GenAI) technology has led to the widespread adoption of AI oral practice tools among university EFL (English as a Foreign Language) learners, yet the boundary conditions under which such tools improve oral proficiency remain underexplored. This study aims to investigate whether the assumption that more frequent use leads to better outcomes is universally valid or is conditioned by learners’ AI learning engagement willingness. Based on questionnaire responses from 248 university students across multiple institutions in China, this study examined the relationships among AI oral tool usage frequency, AI learning engagement willingness (comprising task value perception, willingness to invest resources, and AI-augmented self-efficacy), and self-reported English oral proficiency. Statistical analyses, including one-way ANOVA and hierarchical multiple regression, revealed that both AI learning engagement willingness and usage frequency were independently and positively associated with oral proficiency, displaying an additive rather than interactive relationship. These findings reframe the more use, better outcome logic by revealing a dual-path mechanism: usage frequency ensures the floor of language exposure, while AI learning engagement willingness determines the ceiling of deep processing. The study provides theoretical and practical implications for AI tool design and pedagogical implementation in college English education.
Shijie Hu, Yu-Yang Zhao, Yu-Han Zeng et al.· American Journal of Artifici...· 0 citations
Artificial intelligence (AI) educational tools are increasingly incorporated into higher education, making students’ awareness, attitudes, satisfaction and continued-use intentions important for understanding their educational adoption. This study examined these dimensions among students at Government College Madappally, Kerala. A descriptive research design was adopted, and primary data were collected from 185 students through a structured questionnaire using stratified sampling. Frequencies, percentages and weighted mean scores were used for descriptive analysis. One-way ANOVA assessed differences in awareness across Commerce, Science and Arts streams, while independent-samples t-tests examined gender differences in attitude and satisfaction. Students demonstrated high awareness of AI educational tools, with an overall mean awareness score of 3.94, and awareness of AI mistakes and limitations also remained high at 3.70. Attitudes were positive, with the highest mean score recorded for the statement that AI makes the learning process easier (4.07). Satisfaction was high for accuracy and effectiveness (4.04) and for feedback and guidance (3.90). Seventy-one per cent of respondents agreed or strongly agreed that they intended to continue using AI tools for their studies. Awareness differed significantly across academic courses (F = 8.07, p = 0.000438), whereas gender differences in attitude (p = 0.17) and satisfaction (p = 0.14) were not statistically significant. The findings indicate positive student acceptance alongside persistent technical and feedback-related challenges, highlighting the importance of AI literacy, responsible use and institutional support.
M. Praveen, C. Anwaya, T. Vineethan· Journal of global research i...· 0 citations
This quantitative study aimed to explore the factors affecting the behavioral intention of Moroccan master’s and doctoral students to use AI-Powered tools in literature review. It contributes to the growing discussion on AI adoption in higher education by providing empirical evidence on the factors influencing students’ intentions to use AI-Powered tools in research. Using convenience sampling method, the study collected quantitative data through a validated online 5-point Likert scale questionnaire from 106 Master’s and doctoral students from various Moroccan universities. Multiple Linear Regression was conducted to examine the influence of the key constructs of the modified version of Unified Theory of Acceptance and Use of Technology, including effort expectancy, performance expectancy, social influence, facilitating conditions, hedonic motivation, and price value, in addition to AI tools self-efficacy and perceived ethics on students’ behavioral intention to use these AI-Powered tools. The results revealed that performance expectancy, effort expectancy, hedonic motivation, and perceived ethics have a significant positive influence on behavioral intention. Conversely, social influence, facilitating conditions, AI tools self-efficacy, and price value had no significant influence on behavioral intention. These findings inform higher education stakeholders and policymakers about the factors influencing AI-Powered tools adoption in research.
Jaouad El Mazouzi, Soumia El Yamani· Arab World English Journal· 0 citations
The advancement of artificial intelligence in higher education has transformed academic practices and has increased the need to understand how students’ critical thinking competencies shape their interaction with technology-mediated learning environments. In this context, the present study aimed to examine the predictive associations between critical thinking dimensions, task functionality, and the frequency of artificial intelligence use among university students at the Pontifical Catholic University of Ecuador, Santo Domingo Campus, using a PLS-SEM model.
The study followed a quantitative approach with a non-experimental, cross-sectional, and explanatory-predictive design. The sample consisted of 401 university students selected through intentional and voluntary non-probability sampling. Data were collected using a structured Likert-type questionnaire. SPSS was used for data screening, descriptive statistics, and preliminary reliability analysis. AMOS was used only for a preliminary covariance-based confirmatory factor analysis to examine the factorial structure of the instrument. The final measurement and structural models were estimated using PLS-SEM, with bootstrapping applied to assess the statistical stability of the structural paths.
The findings indicated partial empirical support for the proposed model. Epistemic awareness was positively associated with task functionality, whereas task functionality showed the strongest statistically supported association with the frequency of artificial intelligence use. The remaining direct associations between critical thinking dimensions and task functionality or AI use frequency were not statistically supported.
Critical thinking dimensions, task functionality, and artificial intelligence use constitute an interrelated system within the proposed PLS-SEM model. The findings suggest that epistemic awareness is associated with students’ perceptions of task functionality, and that task functionality is associated with the reported frequency of artificial intelligence use in higher education.
Ángel Ramón Sabando-García, Darwin Maccoll Primero Llacsaguache-Calle, W. R. Villota-Oyarvide et al.· Frontiers in Education· 0 citations
