Melike Aydemir Arslan, Ömer Arpacık, C. Sevda Kucuk, Hatice Yildiz Durak
This study explored the impact of Generative AI (GenAI)-supported online learning on graduate students’ interaction, motivation, and self-regulation in higher education. Employing an explanatory sequential mixed methods design, the research involved 31 students enrolled in graduate-level research methodology courses. Quantitative data were collected through pre- and post-tests using validated scales measuring three types of online interaction (student-content, student-teacher, student-student), intrinsic and extrinsic goal orientation, self-efficacy, and metacognition. Results indicated significant improvements across all domains, with large effect sizes in metacognitive strategies and self-regulated interaction. Qualitative findings from focus group interviews revealed that students found GenAI-based feedback immediate, detailed, and motivating-enhancing academic engagement and cognitive awareness. However, concerns about overly critical or generic feedback and peer limitations were noted. The hybrid model integrating AI, instructor, and peer feedback emerged as a key factor in promoting deeper learning and interaction. Findings underscored the potential of GenAI tools to support autonomous and reflective learning in digital education environments. Implications for instructional design included the importance of real-time, personalized feedback and the balance between automated and human support to foster sustainable student motivation and self-regulation.