Synthesis: Graf et al. (2026) transformed a conventional Learning Analytics Dashboard (LAD) into an interactive ILAD by adding an LLM-powered pedagogical agent and a Judgement of Learning (JoL) self-assessment feature. In a 5-week case study with 30 CS students across three conditions (no agent, "telling" agent, "eliciting" agent), the elicit condition produced more reflection and more accurate mastery calibration. The study bridges Learning Analytics dashboard design with Intelligent Tutoring principles and Metacognition research, showing that how learners interact with their data matters more than simply seeing it.
Study Design
30 CS students in a university programming course, paid to regularly use the ILAD over 5 weeks3 conditions randomized: no agent, "tell" agent (provides info about learner data), "elicit" agent (asks questions about learner data)ILAD extended a conventional LAD with two features:1. LLM-powered pedagogical agent with access to learning analytics and course context
2. Interactive Judgement of Learning (JoL) — self-assessment required before viewing system metrics
Key Findings
Students in the elicit condition engaged in more reflectionElicit-condition students more accurately judged their own mastery (better JoL calibration)The "tell" agent (providing information) did not produce the same benefits — highlighting that pedagogical strategy matters, not just AI presenceThe study demonstrates how interactivity can shift LADs from static visualizations to engagement tools that promote metacognitive processesDesign Implications
The paper challenges the dominant LAD paradigm of "show data → hope for insight." Drawing on Intelligent Tutoring research (Chi's ICAP framework), the authors argue that:
1. Interactivity ≠ navigation — clicking filters is not cognitive engagement; two-way dialogue is
2. Elicitation beats telling — asking learners to self-assess before revealing metrics drives calibration better than showing data upfront
3. LLMs enable scalable interactivity — the pedagogical agent can ask personalized questions at scale, bringing ITS-style dialogue to dashboard contexts
These findings connect to broader Self Regulated Learning theory and the Metacognition literature on judgement calibration.
Connected Concepts
Higher EdLearning AnalyticsLLMMetacognitionPedagogical AgentSelf Regulated LearningvisualizationConnected Articles
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Graf, L., Bassner, P., Anzinger, M., Dietrich, F., Krusche, S., & Poquet, O. (2026). Interactive learning dashboards: rethinking learning visualisations as engagement tools. Education and Information Technologies.