Self-regulated learning (SRL) is a critical competency for academic success and lifelong learning, yet providing personalized SRL support in real-time classroom settings remains challenging. In this randomized controlled trial, we investigated whether generative-AI-(GenAI)-based interventions can effectively support SRL in secondary school students. Drawing on an integrative model of self-regulation in education, the study contrasted two types of GenAI-supported interventions—targeting either motivational (utility value) or strategic (cognitive-learning-strategy-based) aspects of SRL—with a control condition using standard ChatGPT. A total of 371 students (Grades 7–9) were randomly assigned to one of the three conditions and participated in six 45-min sessions during regular physics or English lessons. Outcome measures included self-reported utility value, strategy use (self-reported and tested performance), interest, effort, and tested domain-specific knowledge, assessed at pre- and posttest. The results showed that students in the utility value condition reported more favorable development of perceived utility value than those in the cognitive strategy condition. However, no statistically significant advantages of either intervention over the control condition were found for effort, domain-specific knowledge, or elaboration-based strategy use. Exploratory analyses indicated that students who engaged more meaningfully with the GenAI tended to have more sustained interest than those in the control group. No subject-specific (physics or English) differences in intervention effects were observed. Findings suggest GenAI-based interventions may help preserve motivational aspects of SRL under certain conditions, but further development is needed to effectively support cognitive strategies and improve learning outcomes in secondary school settings. GenAI-based tools were tested for promoting SRL in a school-based RCT design. Utility value prompts helped preserve students’ utility value in the learning content. No clear condition differences emerged for other learning outcomes. Exploratory analyses revealed that meaningful GPT interactions buffered motivational declines. The study provides ideas for scalable and classroom-integrated SRL support using GenAI.
Zusammenfassung Der Beitrag geht von der Feststellung aus, dass der in der quantitativen Forschung systematisch belegte Zusammenhang zwischen dem sozioökonomischen Status von Familien und dem Bildungserfolg von Kindern nach wie vor vor allem über abstrakte theoretische Modelle zu erklären versucht wird. Theoretische Vorschläge dagegen, die am konkreten Interaktionsgeschehen innerhalb der Familie ansetzen, liegen bislang nur in Ansätzen vor. Vor diesem Hintergrund präsentiert der Beitrag Befunde aus einem Forschungsprojekt zur familialen Medienerziehung, die exemplarisch zeigen, in welch unterschiedlichem Maße Eltern unterschiedlicher sozialer Milieus Bildungsanstrengungen von Kindern als Gegenleistung für ihre „Medienzeit“ einfordern. Diese Differenz, so wird argumentiert, sollte als eine Quelle von Bildungsungleichheit ernst genommen werden, die nicht bloß das Resultat ungleicher gesellschaftlicher Ressourcenverteilungen, sondern auch Ausdruck unterschiedlicher milieuspezifischer Wertstandpunkte ist.
