Abstract
Self-reflection is a critical yet often neglected component of self-regulated learning (SRL) in digital education. While there are many use cases of Generative Artificial Intelligence (GAI) discussed in literature, its potential to scaffold reflection remains underexamined. This paper aims to provide guidance in the form of Design Requirements in designing purposeful reflection modules based on findings from an evaluated digital Learning Object instantiation that complements existing research by focusing on GAI-assisted student support within the often-neglected reflection phase of SRL. Deployed in an IS course and evaluated using the Learning Object Evaluation Scale for Students and the Learning Strategies in Studies - short Version questionnaire. Multiple linear regression analysis revealed relationships between perceived Learning, Engagement and key SRL strategies, while Interface Quality showed paradoxical effects. Findings led to seven Design Requirements for GAI-enhanced reflective Learning Objects.
Recommended Citation
Stöckl, Melanie Raphaela, "Turning Generative Artificial Intelligence into a
Reflective Metacognitive Co-Regulator: Toward Designing a
Generative Artificial Intelligence-Enhanced Learning Object
for Self-Reflection" (2025). ACIS 2025 Proceedings. 26.
https://aisel.aisnet.org/acis2025/26