2026· International Journal of Information and Education Technology· 0 citations· 58 references
TL;DR
The findings indicate that critical thinking is predictively associated with students’ self-reported behavioral intention to use AI chatbots and shows a larger standardized association than traditional TAM factors, including perceived usefulness, perceived ease of use, and attitude.
Abstract
Artificial Intelligence (AI) chatbots are increasingly reshaping learning practices in higher education. This study extends the Technology Acceptance Model (TAM) by incorporating critical thinking as a key cognitive antecedent of behavioral intention to use AI chatbots for learning. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), data collected from Vietnamese university students (n = 100) were analyzed to examine the proposed relationships. The findings indicate that critical thinking is predictively associated with students’ self-reported behavioral intention to use AI chatbots and shows a larger standardized association than traditional TAM factors, including perceived usefulness, perceived ease of use, and attitude. Attitude toward AI chatbot use also directly predicts behavioral intention and is positively associated with critical thinking, suggesting a potential mediation mechanism. The extended model demonstrates substantial explanatory power in explaining variance in students’ self-reported behavioral intention. Overall, the results highlight critical thinking as an important cognitive factor associated with more reflective and responsible use of AI chatbots in university education. Given the cross-sectional and exploratory design, the findings should be interpreted as predictive rather than causal.
This study explores the associations between artificial intelligence (AI) usage patterns and perceived critical thinking among university students in China, with a focus on three types of AI use: Information Retrieval Use (IRU), Content Generation Use (CGU), and Text Revision Use (TRU). Grounded in Self-Determination Theory (SDT), the research investigates how academic motivation moderates these relationships. Cognitive Load Theory (CLT) provides an interpretive lens for understanding the observed patterns, though load was not directly measured. A cross-sectional survey was conducted with 342 undergraduate students, collecting data on their AI usage behaviors, academic motivation, and perceived critical thinking dispositions. The results reveal that IRU is positively associated with perceived critical thinking, while CGU shows a negative association. TRU showed no significant association. Regarding moderation effects, intrinsic motivation strengthened the positive association between IRU and perceived critical thinking, but, contrary to expectations, also amplified the negative association between CGU and perceived critical thinking. In contrast, extrinsic motivation buffered the negative association of CGU: at high levels of extrinsic motivation, the negative association between CGU and perceived critical thinking was entirely eliminated. These findings indicate that the cognitive correlates of AI use depend on both usage patterns and motivational factors. The study highlights the importance of considering both the type of AI use and students’ motivational orientation when designing educational strategies for AI integration in higher education.
Jing Zhang, Yang Yang· Scientific Reports· 0 citations
This study investigated the cognitive, affective, and value-based determinants of higher education learners’ intention to continue using AI chatbot assistance with the extended Technology Continuance Theory (TCT), demonstrating that perceived usefulness and perceived ease of use play central roles in shaping learners’ attitudes, attitudes, and continuance intention.
K. Kavitha, V. P. Joshith· Journal of educational compu...· 0 citations
This research examines the determinants of AI utilization among accounting students using an integrated task-technology fit (TTF) and DeLone and McLean (D&M) information systems success framework and assesses their impact on academic performance.
This study employs a quantitative survey method using purposive sampling, distributing structured questionnaires to 313 accounting students in higher education institutions in Indonesia. Data were analyzed using partial least squares structural equation modeling (PLS-SEM) to test the relationships among variables and evaluate the proposed conceptual model.
The results reveal that task characteristics, technological characteristics, system quality, information quality and service quality all exert a positive influence on the intensity of AI use in academic contexts. These findings suggest that when AI technologies are perceived as reliable, accurate and supportive of academic tasks, students are more likely to integrate them effectively into their learning activities. Furthermore, the results indicate that frequent and functional use of AI technologies significantly enhances students’ academic performance − reflected in improved comprehension of course material, greater efficiency in task completion, and more effective achievement of academic objectives.
The findings offer valuable insights for higher education institutions to strategically integrate artificial intelligence technologies in enhancing teaching and learning processes. Technology developers are encouraged to improve the academic rigor, transparency and reliability of AI-based systems. Moreover, institutional policies and ethical guidelines must be established to guide the responsible and constructive use of AI within academic environments.
This study introduces a novel approach by integrating two theoretical frameworks − TTF and the DeLone and McLean information systems success Model − that have rarely been combined in prior research on AI adoption in education. Unlike prior studies that primarily focus on the technical or acceptance aspects of technology, this research emphasizes how generative AI aligns with students’ academic tasks and learning needs. Its primary contribution lies in empirically examining this fit within the Indonesian context, specifically among accounting students, a demographic that has remained largely underrepresented in discussions on AI-based educational technologies.
Ananda Desyta Alistyaningrum, Frank Aligarh, Didik Prasetyanto et al.· Higher Education, Skills and...· 0 citations
The rapid advancement of Artificial Intelligence (AI) has transformed higher education, particularly through the use of ChatGPT as a learning support tool. However, the extent to which AI ChatGPT usage and self-directed learning influence students' critical thinking skills remains unclear. Therefore, this study aimed to examine the effects of AI ChatGPT usage and self-directed learning on the critical thinking skills of students at the Faculty of Teacher Training and Education, Universitas Ekasakti. This study employed a quantitative approach using a survey method. The population consisted of 277 students, with 164 respondents selected through non-probability sampling using purposive sampling. Data were collected using a Likert-scale questionnaire and analyzed using multiple linear regression with SPSS. The findings revealed that AI ChatGPT usage had a positive and significant effect on critical thinking skills (t = 2.086; p = 0.039 < 0.05), whereas self-directed learning had no significant effect (t = 1.381; p = 0.169 > 0.05). Simultaneously, AI ChatGPT usage and self-directed learning significantly influenced students' critical thinking skills (F = 3.244; p = 0.042 < 0.05). The coefficient of determination (R²) was 0.039, indicating that the independent variables explained 3.9% of the variance in critical thinking skills, while 96.1% was explained by other factors outside the scope of this study.
Ayu Oktaviani, Caterina Lo, Serli Diovani Teza· TOFEDU: The Future of Educat...· 0 citations
These findings provide theoretical support for SCT in technology‐mediated learning and suggest practical strategies for educators, including tailoring instructional design to learners' cognitive profiles, fostering motivation and self‐efficacy and leveraging AI tools to enhance active and sustained engagement in language learning.
Yifan Wang, Ran Zhi· European Journal of Educatio...· 0 citations
The progression of Generative Artificial Intelligence (GAI) has revolutionized English language learning. It has ensured personalized, interactive, and accessible learning experiences. This study examines the factors influencing college students' intention to use generative AI based English language learning applications. The present study integrates the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB). The study explores the effects of perceived usefulness, perceived ease of use, attitude, subjective norm, and perceived behavioral control on students behavioural intention. A cross sectional descriptive research design was applied. The primary data were collected through a structured questionnaire circulated to undergraduate and postgraduate students in various Arts and Science Colleges. Structural Equation Modeling (SEM) was employed to test the proposed conceptual model utilizing 267 responses. It is found that Perceived usefulness and perceived ease of use significantly and positively influenced students attitudes toward using Generative AI based English language applications. The study also shows that attitude, subjective norm, and perceived behavioral control significantly influenced behavioural intention of students. The structural model demonstrated 69.5% of the variance in attitude and 56.9% of the variance in behavioural intention. The findings show that the integrated TAM and TPB framework provides a rigorous explanation of college students acceptance of generative AI based English language learning applications.
Pradeesh N.M, Navya A.H, Samanway Krishnan· International Journal of Adv...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
