CLARA: An AI-Augmented Analytics Dashboard for Collaboration Literacy

2026-05-17

AI Summary

Existing analytical metrics struggle to capture the semantic dynamics of idea construction and progression in collaborative discourse, thereby hindering the effective cultivation and assessment of collaborative competencies. To address this limitation, this work proposes an intelligent analysis system that leverages natural language processing to automatically generate concept maps and multidimensional evaluations of collaboration quality from discussion transcripts, producing interpretable semantic artifacts. These artifacts are stored in a vector database to facilitate human–AI shared understanding and enable interactive exploration. By grounding human–AI collaboration in AI-generated semantic representations, the approach enhances both the interpretability of collaborative processes for human users and the reasoning capabilities of the AI system. Empirical results demonstrate that the proposed system significantly outperforms baseline methods relying solely on raw text in terms of collaboration quality analysis, retrieval performance, and response quality.

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.

Problem

• collaboration literacy
• semantic representation
• learning analytics
• collaboration assessment
• AI-augmented analytics

Innovation

• analytics artifacts
• semantic representation
• human-AI common ground
• collaboration literacy
• agentic analytics