LA-ReflecT is a system designed to connect students and teachers in real-time and interactively, enabling learners to engage in structured reflection on their learning processes. The system employs multimodal learning analytics by integrating diverse data sources—such as videos, texts, interaction logs, and responses generated using ChatGPT—to comprehensively capture learners’ progress and cognitive engagement.

By collecting and analyzing these multimodal learning activity data, LA-ReflecT offers support aimed at enhancing the quality of learning experiences. For example, it enables real-time tracking of how learners engage with content, what they focus on, and how their performance evolves. Through reflection based on accumulated learning records, students can refine their learning strategies, while educators are empowered to revise instructional content and support approaches.

The system is designed to foster learner autonomy and metacognition, thereby promoting deeper learning. At its core lies the visualization and facilitation of a cyclical process of reflection and improvement, which holds the potential to generate new developments in both learning and teaching practices.

While the use of AI in education remains a subject of ongoing debate, this research adheres to the guidelines set by Japan’s Ministry of Education, Culture, Sports, Science and Technology (MEXT), and aims to build a platform capable of constructively responding to the associated challenges.