First-Year Students’ Narratives on Generative AI-Based Feedback and Cognitive Engagement - Nuraeni Novira; Amrah Kasim; Andi Abdul Hamzah; Abd. Fattah; Maratu Shoaleha; Hilyah Rahmat (Yayasan Adra Karima Hubbi, 2026)
Background. The integration of Generative Artificial Intelligence (AI) in higher education has transformed how formative feedback is delivered, offering possibilities that are immediate, adaptive, and personalized. However, a critical gap persists in the literature: most studies have measured the effectiveness of AI-based feedback without examining how students actually experience and make meaning of it. This gap is especially pronounced among first-year university students in developing countries such as Indonesia, where the lived dimensions of AI-mediated learning remain largely unexplored. Purpose. This study aims to explore first-year students’ narratives on their experiences with Generative AI-based formative feedback and how these experiences shape their cognitive engagement. Method. This study employs a qualitative approach using narrative inquiry. Twelve first-year students were purposively selected from 80 participants based on theoretical saturation criteria, providing rich reflective data on their interactions with Generative AI-based feedback. Data were analyzed through inductive thematic narrative analysis, identifying recurring patterns that illuminate how students construct meaning from their experiences and how those experiences shape their cognitive engagement. Results. The findings indicate that students perceive AI-based feedback as a supportive and responsive learning companion that facilitates reflection, encourages independent learning, and promotes deeper engagement with learning materials. Students’ narratives also highlight the importance of immediacy and personalization in feedback, which contribute to the development of critical thinking, self-regulation, and deep learning strategies. Conclusion. This study emphasizes the importance of understanding AI-based learning not only through measurable outcomes but also through students’ lived experiences. By foregrounding students’ voices, this research provides insights into how AI-mediated feedback can support meaningful learning and enhance cognitive engagement. The findings offer practical implications for designing more adaptive and student-centered feedback practices in higher education.