Promoting Reflection Skills of Pre-Service Teachers—The Power of AI-Generated Feedback

Abstract

Reflection skills are a key but challenging element in teacher training. Feedback on reflective writing assignments can improve reflection skills, but it is affected by challenges (high variability in judgments and time investment). AI-generated feedback offers many options. Therefore, the aim of this study was to examine the potential of AI-generated feedback compared to that provided by lecturers for developing reflective skills. A total of 93 randomly selected pre-service teachers (70% female) in a course at a German university wrote two reflections and received feedback from either lecturers or ChatGPT 4.0 based on the same prompts. Pre-service teachers’ written reflections were assessed, and an online questionnaire based on standard instruments was applied. Control variables included metacognitive learning strategies and reflection-related dispositions. Based on a linear mixed model, the main effects on reflective skills were identified for time ($\widehat{\mathsf{\beta }}$ = 0.41, p = 0.003) and feedback condition ($\widehat{\mathsf{\beta }}$ = −0.42, p = 0.032). Both forms of feedback similarly fostered reflective skills over time, with academic self-efficacy emerging as a pertinent disposition ($\widehat{\mathsf{\beta }}$ = 0.25, p = 0.014). The limitations of this study and implications for teacher training are discussed.