CLARA: An AI-Augmented Analytics Dashboard for Collaboration Literacy

Key Finding

Agentic analytics using AI-produced concept-map artifacts as shared human-AI representations improves collaboration quality analysis and AI response grounding over transcript-only baselines.

Synthesis

CLARA introduces a novel architecture for Learning Analytics where AI-produced artifacts (concept maps, seven-dimension collaboration assessments) serve as shared representations between human dashboard users and AI reasoning agents. By indexing these artifacts into vector databases, CLARA establishes a human-AI common ground that simultaneously scaffolds human interpretation of collaboration data and grounds AI reasoning — improving both retrieval performance and response quality. This dual-use architecture has implications beyond collaboration analytics: the principle of AI-produced artifacts as shared infrastructure could apply to Intelligent Tutoring where student models, Knowledge Tracing IRT estimates, and concept maps could serve as common ground between tutor agents and teacher dashboards. The artifact-as-knowledge-infrastructure approach also resonates with RAG patterns used in Retrieval Augmented Tutoring Algorithm Kite and Cyberscholar GenAI Writing Feedback, where structured content representations improve AI response quality. CLARA's focus on semantic dimensions beyond behavioral signals parallels the shift advocated by AI Tutor Behavioral Evaluation toward evaluating what students actually do, not just what the AI says.

Connected Concepts

Connected Articles

Citation

2026, A. (2026). CLARA: An AI-Augmented Analytics Dashboard for Collaboration Literacy