Enhancing Critical Thinking and Self-Efficacy with GenAI: A Social Cognitive Perspective Using Structural Equation Modelling
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| Title: | Enhancing Critical Thinking and Self-Efficacy with GenAI: A Social Cognitive Perspective Using Structural Equation Modelling |
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| Language: | English |
| Authors: | Da Teng, Xue Zhou, Hosam Al-Samarraie, Lei Fang (ORCID |
| Source: | Journal of Computer Assisted Learning. 2026 42(1). |
| Availability: | Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us |
| Peer Reviewed: | Y |
| Page Count: | 14 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Critical Thinking, Self Efficacy, Artificial Intelligence, Social Cognition, Structural Equation Models, Cognitive Processes, College Students, Learner Engagement, Man Machine Systems |
| DOI: | 10.1002/jcal.70176 |
| ISSN: | 0266-4909 1365-2729 |
| Abstract: | Background: The integration of Generative Artificial Intelligence (GenAI) into higher education is growing rapidly, yet its impact on learning processes remains underexplored. Existing research and theories, such as social cognitive theory (SCT), largely focus on human-to-human learning interactions, leaving a gap in understanding how cognitive and motivational mechanisms operate in human-AI contexts. Objectives: This study investigates how GenAI features influence students' critical thinking and self-efficacy, with a specific focus on the mediating role of cognitive engagement. Methods: Drawing on SCT, we conceptualised GenAI features--playfulness, perceived learning value and output quality--as environmental stimuli influencing student outcomes via cognitive engagement. Survey data were collected from 223 undergraduate and postgraduate students. Structural equation modelling was used to test both direct effects and the mediating role of cognitive engagement. Results and Conclusions: The results indicate that GenAI playfulness and perceived learning value significantly enhance students' cognitive engagement, which then positively affects their critical thinking and self-efficacy. Cognitive engagement functioned as a key mediator in these relationships. However, output quality did not exhibit a significant effect, suggesting that engagement, rather than content quality alone, is crucial for fostering meaningful cognitive development. This study extends SCT by adapting it to human-AI learning contexts and provides actionable insights for designing GenAI tools that enhance learner engagement and development. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1495882 |
| Database: | ERIC |
| Abstract: | Background: The integration of Generative Artificial Intelligence (GenAI) into higher education is growing rapidly, yet its impact on learning processes remains underexplored. Existing research and theories, such as social cognitive theory (SCT), largely focus on human-to-human learning interactions, leaving a gap in understanding how cognitive and motivational mechanisms operate in human-AI contexts. Objectives: This study investigates how GenAI features influence students' critical thinking and self-efficacy, with a specific focus on the mediating role of cognitive engagement. Methods: Drawing on SCT, we conceptualised GenAI features--playfulness, perceived learning value and output quality--as environmental stimuli influencing student outcomes via cognitive engagement. Survey data were collected from 223 undergraduate and postgraduate students. Structural equation modelling was used to test both direct effects and the mediating role of cognitive engagement. Results and Conclusions: The results indicate that GenAI playfulness and perceived learning value significantly enhance students' cognitive engagement, which then positively affects their critical thinking and self-efficacy. Cognitive engagement functioned as a key mediator in these relationships. However, output quality did not exhibit a significant effect, suggesting that engagement, rather than content quality alone, is crucial for fostering meaningful cognitive development. This study extends SCT by adapting it to human-AI learning contexts and provides actionable insights for designing GenAI tools that enhance learner engagement and development. |
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| ISSN: | 0266-4909 1365-2729 |
| DOI: | 10.1002/jcal.70176 |
