Empowering Multimodal Learning Analytics using Agentic AI: A Comprehensive Platform for Simulation-based Clinical Training with Intelligent Assessment

Abstract

Simulation-based clinical training generates rich multimodal data that remains underused due to fragmented modalities, annotation bottlenecks, weak provenance, and tools misaligned with educator workflows. We introduce ClinVision, an educator-in-the-loop platform that operationalizes end-to-end multimodal learning analytics: synchronized multi-camera review, ISBAR-aligned scoring (a validated clinical communication framework), and templated reports with jump-to-evidence provenance. Agentic AI - systems that act on behalf of users while preserving human authority - assists with phrasing under explicit control (accept/edit/reject) and visible provenance, supporting accountable use rather than prescriptive automation. An in-learning deployment with five educators revealed full ISBAR coverage and time-efficient workflows, though AI suggestions were used selectively. We surface three transferable design tensions (assistance vs. authority, structure vs. flexibility, evidence vs. overload) and demonstrate that workflow integration, temporal primitives, and background AI assistance may better support high-stakes assessment than analytics or automation alone.

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