Self-regulated Learning Strategies with Generative AI among Pre service Teachers

Abstract

Generative artificial intelligence (GenAI) has emerged as a promising resource to support self-regulated learning in initial teacher education contexts. However, there is still limited empirical evidence on how pre-service teachers implement specific self-regulated learning strategies mediated by these technologies. The present study aimed to identify the self-regulated learning strategies mediated by GenAI used by pre-service teachers in the Dominican Republic and to establish differentiated profiles according to their level of regulation. A non-experimental, descriptive survey-based study was conducted. Data were collected through self-report using a questionnaire specifically designed and validated for this purpose and administered to 608 pre-service teachers. The results show a moderate use of self-regulated learning strategies supported by GenAI, with greater frequency in planning, study organization, and information seeking, whereas strategies related to evaluation, metacognitive monitoring, and critical reflection were less frequent. In addition, three differentiated self-regulation profiles were identified, significantly associated with the academic program attended. The findings highlight the need to strengthen pedagogical and technological preparation aimed at promoting the critical and reflective use of GenAI in initial teacher education and to encourage further research examining the development of these processes over time.