PLAI: A Pilot Study of Profile-Based Explanation for AI-Supported Learning
TL;DR
PLAI is presented, a web-based prototype that generates explanations from lecture slides, audio transcripts, and a structured student profile through a chat-based interface and suggests that profile-based multimodal prompts may mainly support motivational engagement rather than immediate test performance.
Abstract
Large language models (LLMs) are increasingly used as on-demand conversational learning assistants, but they typically do not adapt explanations to a student’s background unless explicitly prompted. We present the Personalized Learning Assistant Interface (PLAI), a web-based prototype that generates explanations from lecture slides, audio transcripts, and a structured student profile through a chat-based interface. We evaluated PLAI in a controlled pilot study with 24 STEM students, comparing profile-based personalization with a baseline condition using the same slide and transcript context. Immediate learning was assessed with a five-item knowledge test, while subjective experience was measured using the User Experience Questionnaire (UEQ) and open-ended feedback. We did not observe clear differences in knowledge-test outcomes, but participants in the personalized condition reported significantly higher UEQ Stimulation. These results suggest that profile-based multimodal prompts may mainly support motivational engagement rather than immediate test performance, while larger and longer-term studies are needed to assess learning effects.