Bibliographic Details
| Title: |
Enhancing Mobile Learning with AI-Powered Chatbots: Investigating ChatGPT's Impact on Student Engagement and Academic Performance. |
| Authors: |
Dahri, Nisar Ahmed1,2, Al-Rahmi, Waleed Mugahed3 waleed.alrahmi@dau.edu.sa, Alhashmi, Khadijah Amru4, Bashir, Farhan5 |
| Source: |
International Journal of Interactive Mobile Technologies. 2025, Vol. 19 Issue 11, p17-38. 22p. |
| Subjects: |
Artificial intelligence, Open learning, Mobile learning, ChatGPT, Cognitive load, Education research |
| Abstract: |
In mobile learning environments, after-class review strategies play a crucial role in reinforcing key concepts, summarizing knowledge, and enhancing subject mastery. However, students often encounter difficulties reviewing lessons due to limited support and immediate assistance, impacting their overall learning experience. This study examines the role of artificial intelligence (AI)-powered ChatGPT as a mobile learning tool to support pre-service students in academic performance, cognitive load reduction, perceived learning, trust, and motivation. Utilizing a quasi-experimental design, two classes enrolled in an assessment and evaluation course at UTM University, Malaysia, participated in the study. The experimental group (n = 16) engaged with ChatGPT via mobile devices for post-lesson reviews, while the control group (n = 16) relied on traditional instructor-led interactions. Pre- and post-tests and ANCOVA analyses were conducted to evaluate changes in students' learning outcomes. The findings indicate that mobile AI-powered ChatGPT significantly enhances academic achievement, reduces cognitive load, and fosters increased motivation, perceived learning, and trust. The results highlight the potential of integrating AI-driven mobile learning solutions to provide personalized, on-demand academic support, enabling students to engage in more effective and flexible learning experiences beyond traditional classroom settings. These insights contribute to the growing body of research on AI in education, emphasizing the need for further exploration into mobile AI-driven interventions for diverse learning contexts. [ABSTRACT FROM AUTHOR] |
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| Database: |
Engineering Source |