Paper Type
Complete
Abstract
The increasing use of Artificial Intelligence (AI) in higher education is transforming students’ learning practices by shifting planning, monitoring, and evaluation toward AI-supported interaction. While task performance may remain high, externalizing regulatory processes may limit the development of self-regulated learning (SRL). This study examines how students appropriate dialogic AI support within their SRL practices while preserving regulatory agency. Drawing on a sociotechnical perspective, AI appropriation is conceptualized as meta-regulation within the SRL cycle. Survey data and think-aloud-informed group discussions were analyzed to explore associations between baseline SRL profiles, reported changes, and situated AI use. Correlation analyses indicate exploratory, profile-contingent patterns, with lower baseline SRL tending to coincide with larger reported gains. Qualitative findings illustrate how students enact AI as a co-regulatory resource for SRL and critical boundary setting. These findings suggest that AI’s educational value may depend on how learners configure AI within their regulatory routines.
Paper Number
1183
Recommended Citation
Stöckl, Melanie Raphaela, "Toward Students’ Appropriation of Artificial Intelligence for Learning: The Role of Self-Regulated Learning in AI-Mediated Contexts" (2026). AMCIS 2026 Proceedings. 1.
https://aisel.aisnet.org/amcis2026/sig_ed/sig_ed/1
Toward Students’ Appropriation of Artificial Intelligence for Learning: The Role of Self-Regulated Learning in AI-Mediated Contexts
The increasing use of Artificial Intelligence (AI) in higher education is transforming students’ learning practices by shifting planning, monitoring, and evaluation toward AI-supported interaction. While task performance may remain high, externalizing regulatory processes may limit the development of self-regulated learning (SRL). This study examines how students appropriate dialogic AI support within their SRL practices while preserving regulatory agency. Drawing on a sociotechnical perspective, AI appropriation is conceptualized as meta-regulation within the SRL cycle. Survey data and think-aloud-informed group discussions were analyzed to explore associations between baseline SRL profiles, reported changes, and situated AI use. Correlation analyses indicate exploratory, profile-contingent patterns, with lower baseline SRL tending to coincide with larger reported gains. Qualitative findings illustrate how students enact AI as a co-regulatory resource for SRL and critical boundary setting. These findings suggest that AI’s educational value may depend on how learners configure AI within their regulatory routines.
Comments
SIG ED