Academic Identity Formation among University Students in AI-Enhanced Collaborative Learning Spaces - Paul Johan Kawatu; Yuni Misrahayu; Teti Berliani; Aliah Bagus Purwakania Hasan (Sakura Digital Nusantara, 2026)
Purpose: This study aims to analyze how academic identity is constructed and transformed among students engaged in Artificial Intelligence (AI)-assisted collaborative learning, addressing the limited research on this issue in underrepresented regions such as Papua. Methods: A qualitative case study approach was conducted over eight weeks in a collaborative university class in Papua. Data were collected through four classroom observations, in-depth interviews with eight students and one lecturer, five cycles of reflective journals, and analysis of learning artifacts. The data were analyzed using thematic analysis with systematic coding, source triangulation, and comparison of identity development at the beginning and end of the study. Findings: The findings indicate that AI appeared to support students’ academic agency, increase confidence in articulating scientific arguments, and strengthen the visibility and legitimacy of their contributions in collaborative group work. However, differences in digital literacy and varying intensities of AI use were associated with unequal participation and influence within collaborative activities. Research Implications: These findings suggest that reflective and inclusive AI integration may support academic identity development while highlighting the importance of equitable pedagogical strategies to reduce dependency, marginalization, and participation gaps in collaborative learning environments. However, the findings are limited to a single-case qualitative context and may not be fully transferable to other educational settings. Originality: This study provides contextual and empirical insights into academic identity formation within AI-mediated collaborative learning spaces in an underrepresented higher education context, particularly Papua, and contributes to discussions on inclusive learning design in human AI educational environments.