An Educator-Guided LLM Pedagogical Agent for Scaffolded Feedback in Conceptual Database Design
On the map: LLM agents and tool use → Educational agents and math problem solving
Abstract (arXiv)
We present an educator-guided LLM pedagogical agent for scaffolded feedback in conceptual database design. Integrated into an entity--relationship diagram (ERD) editor, the system grounds feedback in the student artifact, assignment requirements, educator-authored rubrics, and instructional resources. Its architecture separates hidden, artifact-grounded diagnosis from the workflow that controls the form and disclosure level of student-facing support. We instantiate the architecture as a four-stage workflow progressing from concept checks and guided application to low-detail feedback and localized clarification. Each feedback request creates a stateful episode linked to versioned ERD states. In a deployment spanning three ERD environments and 383 feedback episodes, 71.1% of observed target-level changes fully or partially incorporated the hidden diagnostic target, including many after Stages~1--2. Qualitative analysis showed that staged disclosure sometimes withheld inaccurate details, supported selective uptake, or allowed later recovery, though some errors still shaped revisions. Survey responses from a self-selected sample favored delayed disclosure and student agency but noted indirectness and repetition.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Educational agents and math problem solving, one of 16 directions in LLM agents and tool use. Builds on 5 of its 45 references that are in the map. First posted on arXiv 1 Oct 2026.
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5 of 45 references.
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- 2025FACET: Teacher-Centred LLM-Based Multi-Agent Systems-Towards Personalized Educational Worksheets15 Aug
- 2025Towards Adaptive Feedback with AI: Comparing the Feedback Quality of LLMs and Teachers on Experimentation Protocols18 Feb
Other references · 3
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- 2026From SQL Errors to Concept Gaps: An AI-Powered Knowledge Graph Analytics Platform for Personalized Feedback
- 2026Evidence-Decision-Feedback: Theory-Driven Adaptive Scaffolding for LLM Agents
- 2026Confirming Correct, Missing the Rest: LLM Tutoring Agents Struggle Where Feedback Matters Most
- 2026ISD-Agent-Bench: A Comprehensive Benchmark for Evaluating LLM-based Instructional Design Agents
- 2026Tailoring Scaffolding to Diagnostic Strategies: Theory-Informed LLM-Based Agents
- 2026ERUnderstand: Evaluating Vision-Language Models on Structured ER Diagrams
- 2026When Agents Implement Systems: A Case Study in Defects, Detection, and Evaluation Rigor
- 2026On the Limitations of Large Language Models for Conceptual Database Modeling