Toru Matsuoka
In FY2026, based on the “Multi-layered Intangible Asset Model” developed in the previous year, we aim to develop a generative AI-based system prototype to support the formation of intangible assets through learner reflection, and to conduct a formative evaluation.
Specifically, we will implement the “Supportive Information” from the 4C/ID model as adaptive AI-driven prompts (cognitive and meta-cognitive scaffolding). By analyzing learner reflections based on this theory, we will construct functions to facilitate growth and visualize its trajectory through structured representations such as “knowledge graphs” and “pattern languages.”
