The impact of an AI ‐driven multimodal teaching analytics dashboard on pre‐service teachers' reflective learning in microteaching

Microteaching is a cornerstone of teacher education, enabling pre‐service teachers (PSTs) to rehearse, observe and refine instructional practices through structured reflection. Traditional video‐based review in microteaching is often time‐consuming and insufficiently analytical. Although learning analytics dashboards (LADs) can visualize teaching data to support reflection, many remain descriptive, offering limited insight into how behavioural evidence relates to pedagogical meaning. To address this gap, this study introduces iTeachBoard, an AI‐driven multimodal teaching analytics dashboard that integrates layered multimodal timeline visualization based on data‐storytelling design principle with LLM‐generated narrative feedback to support reflective learning in microteaching. A quasi‐experimental study ( n = 53) compared iTeachBoard with a conventional visual analytics dashboard (VAD). Results revealed that iTeachBoard significantly enhanced PSTs' data interpretation, meaning construction and reflective writing depth. Participants perceived narrative‐style feedback as more trustworthy, explainable and pedagogically relevant. These findings suggest that LLM‐driven multimodal learning analytics can help bridge data visualization and pedagogical interpretation, thereby supporting PSTs' sense‐making, particularly data interpretation and meaning construction, as well as reflective writing depth in microteaching. Practitioner notes What is already known about this topic Microteaching offers pre‐service teachers (PSTs) valuable opportunities to rehearse lessons, receive feedback and reflect on their teaching. However, reflections often remain superficial due to limited feedback structure and lack of actionable guidance. Learning analytics dashboards (LADs) can visualize teaching behaviours, but most rely on descriptive metrics that lack interpretability and pedagogical relevance. Sense‐making and data‐storytelling design principles have been proposed to help PSTs connect learning data with pedagogical meaning, yet empirical validation in teacher education remains scarce. What this paper adds Introduces iTeachBoard, an AI‐driven multimodal LAD that integrates large language model (LLM)‐based narrative feedback with multimodal visualizations to support deep reflective learning in microteaching. Proposes the AI for Microteaching Multimodal Analytics Model (AI4MT), linking observable teaching behaviours to pedagogically meaningful reflective dimensions. Provides empirical evidence that narrative‐style feedback combined with multimodal visualization significantly improves PSTs' overall sense‐making, with significant gains in data interpretation and meaning construction, as well as reflective writing depth, compared with a conventional video analytics dashboard. Implications for practice and/or policy iTeachBoard can be implemented in teacher education and professional development to promote evidence‐based reflection, collaborative lesson analysis and continuous pedagogical improvement. The dashboard can enhance teachers' data literacy and AI literacy, fostering meaningful engagement with multimodal feedback for reflective growth. Institutions should develop policies ensuring ethical data use, privacy protection and transparency in AI‐supported reflective systems to promote responsible adoption in educational settings.