mlsanityData to 14 Oct 2026

An Educator-Guided LLM Pedagogical Agent for Scaffolded Feedback in Conceptual Database Design

arXiv 2610.00870 ↗First posted 1 Oct 2026cs.AIcited by 0

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.

Closest ancestors · 2

Same direction, newest first

  1. 2025FACET: Teacher-Centred LLM-Based Multi-Agent Systems-Towards Personalized Educational Worksheets15 Aug
  2. 2025Towards Adaptive Feedback with AI: Comparing the Feedback Quality of LLMs and Teachers on Experimentation Protocols18 Feb

Other references · 3

  1. 2023Prototyping the use of Large Language Models (LLMs) for adult learning content creation at scalecited by 12
  2. 2023Are Large Language Models Fit For Guided Reading?cited by 2
  3. 2024LLM-Driven Feedback for Enhancing Conceptual Design Learning in Database Systems Coursescited by 2

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  8. 2026On the Limitations of Large Language Models for Conceptual Database Modeling