CLARA: An AI-Augmented Analytics Dashboard for Collaboration Literacy

TL;DR

CLARA is an AI-augmented analytics dashboard that extracts semantic representations from transcripts to assess collaboration quality and support human-AI collaboration in learning environments.

Contribution

The paper introduces CLARA, a novel system that combines semantic artifact extraction with agentic analytics to improve collaboration assessment and AI-human interaction.

Findings

CLARA reliably analyzes collaboration quality across seven dimensions.

Artifacts improve retrieval performance over transcript-only methods.

System enhances human interpretation and AI reasoning in learning analytics.

Abstract

Collaboration literacy requires adapting to the evolving demands of group work within complex discussions, making it difficult to develop and assess. Traditional analytics metrics capture behavioral signals while missing the semantic dimensions of how learners approach collaboration and build on each other's ideas. We present Collaboration Literacy through Artifact Reasoning and Augmentation (CLARA), an agentic analytics system that extracts semantic representations from transcripts as analytics artifacts: concept maps representing emergent ideas and relationships, and collaboration assessment characterizing collaboration quality across seven dimensions. While users explore these artifacts through the dashboard, the same artifacts are indexed into distinct vector database collections for agent retrieval and reasoning. This architecture establishes a human-AI common ground where users and AI can operate over shared representations. Evaluation results show that CLARA produces reliable collaboration quality analysis and, owing to the artifacts serving as knowledge infrastructure, improves both retrieval performance and response quality over transcript-only baselines. Our work suggests that AI-produced artifacts may scaffold human interpretation and ground AI reasoning in learning analytics workflows.