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

TL;DR

This paper introduces an interpretable closed-loop ITS that uses multimodal data and a structured feedback system to improve on-camera presentation skills, validated through large-scale data and a learner study.

Contribution

It develops a novel, explainable feedback architecture connecting multimodal scoring with coaching, enabling scalable, effective practice for presentation skills.

Findings

Achieved rubric-aligned scoring with expert-level performance (R2=0.48-0.61).

Participants showed significant improvement in all BARS dimensions (d=0.39-0.90).

Practice frequency correlated positively with posttest performance.

Abstract

This paper presents an interpretable closed-loop Intelligent Tutoring System (ITS) that supports feedback-guided practice for developing on-camera oral presentation skills at scale. The system operationalizes a seven-dimensional Behaviorally Anchored Rating Scale (BARS) and implements a three-layer interpretable feedback architecture that connects rubric-aligned multimodal scoring, audience-perceived expressive diagnostics, and retrieval-augmented conversational coaching to support deliberate practice. Built on an XGBoost backbone, the ITS maps multimodal inputs (facial, vocal, textual, and oculomotor features) into evidence-based feedback that can be traced back to observable performance cues. Trained on 10,360 Massive Open Online Course (MOOC) video segments, the system achieved rubric-aligned scoring with performance levels comparable to expert ratings (R2 = 0.48-0.61, Spearman's rho = 0.69-0.78, MAE = 0.43-0.57). In a pre-post validation study with 204 adult learners over a 30-day practice window, participants demonstrated significant improvements across all seven BARS dimensions (Cohen's d = 0.39-0.90), with practice frequency showing a strong positive association with posttest performance after controlling for baseline scores and demographics. The results demonstrate how multimodal analytic outputs can be systematically transformed into observable behavioral change through an integrated feedback architecture, advancing explainable and pedagogically grounded ITS design for performance-based competencies.