An Interpretable Closed-Loop Intelligent Tutoring System for Multimodal Affective Feedback in Asynchronous Presentation Training

Key Finding

Closed-loop ITS with multimodal affective scoring (facial, vocal, textual, oculomotor) produced significant presentation skill gains (Cohen's d = 0.39-0.90, N=204) over 30 days.

Synthesis

This paper presents one of the most comprehensive closed-loop Intelligent Tutoring for soft-skill training. The system operationalizes a seven-dimension BARS across facial, vocal, textual, and oculomotor inputs, using an XGBoost backbone for interpretable scoring that approaches expert-rater reliability (Spearman's rho 0.69-0.78). The three-layer feedback architecture — rubric-aligned scoring, audience-expressive diagnostics, and RAG conversational coaching — creates a complete deliberate practice loop. With 204 adult learners and Cohen's d of 0.39-0.90 across all seven dimensions, this is among the stronger efficacy signals in ITS research. The interpretability requirement (feedback traceable to observable cues) directly addresses concerns raised in Educational LLM Alignment about opaque AI feedback, while the multimodal approach extends beyond text-only systems like Cyberscholar GenAI Writing Feedback. The closed-loop architecture shares philosophical ground with AI Tutor Behavioral Evaluation's call for behavioral feedback loops, and the retrieval-augmented coaching component parallels Retrieval Augmented Tutoring Algorithm Kite's approach to grounded tutoring.

Connected Concepts

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Citation

K.-E, A.S.H.H. & Technologies, V.I.T.O.L. (2026). An Interpretable Closed-Loop Intelligent Tutoring System for Multimodal Affective Feedback in Asynchronous Presentation Training