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An Educator-Guided LLM Pedagogical Agent for Scaffolded Feedback in Conceptual Database Design
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Sara Riazi, Pedram Rooshenas

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ResearcharXiv cs.AI

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

arXiv:2610.00870v1 Announce Type: new Abstract: 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.

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This story was published by arXiv cs.AI and written by Sara Riazi, Pedram Rooshenas. SyncAI.news shows a preview; the complete article is on the publisher's site.

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