LLM-Driven Feedback for Enhancing Conceptual Design Learning in Database Systems Courses

TL;DR

This paper introduces a novel LLM-driven system that provides targeted, detailed feedback on student-created database design diagrams, enhancing learning outcomes in a Database Systems course.

Contribution

It presents a new system that converts ERDs into JSON, extracts requirements, and uses LLMs to generate specific feedback and questions, improving conceptual design learning.

Findings

Effective feedback improved student design skills

System successfully converts ERDs to JSON for analysis

Generated tailored questions to aid understanding

Abstract

The integration of LLM-generated feedback into educational settings has shown promise in enhancing student learning outcomes. This paper presents a novel LLM-driven system that provides targeted feedback for conceptual designs in a Database Systems course. The system converts student-created entity-relationship diagrams (ERDs) into JSON format, allows the student to prune the diagram by isolating a relationship, extracts relevant requirements for the selected relationship, and utilizes a large language model (LLM) to generate detailed feedback. Additionally, the system creates a tailored set of questions and answers to further aid student understanding. Our pilot implementation in a Database System course demonstrates effective feedback generation that helped the students improve their design skills.