Talk · International Symposium on Computer Science and Educational Technology · August 1, 2026

A Course-Scoped RAG Domain Model for an AI Teacher Assistant: Architecture and Pilot Evaluation

Battsetseg Erdenebat, Uranchimeg Tudevdagva, Amarzaya Amartuvshin, N. I. Lomakin, Ilhom Boynazarov

Abstract

Large Language Models (LLMs) are increasingly used in higher education, yet general-purpose systems may generate unsupported or overly general answers when they are not grounded in verified course materials. This paper presents a course-scoped Retrieval-Augmented Generation (RAG) Domain Model for an AI Teacher assistant as a foundational component for a future Intelligent Tutoring System (ITS). The prototype uses course-approved materials as a retrieval scope and returns answers with file and page references through a Telegram-based interface. A pilot implementation was evaluated with a user survey$\boldsymbol{(} \mathbf{N}=\mathbf{3 3} \boldsymbol{)}$, representative source-cited interactions, and a structured plan for subsequent source support evaluation. The survey indicates positive perceived usefulness and reuse intention, while response speed remains a limitation. The contribution is an architecture and pilot evaluation framework for traceable, course-bounded AI assistance rather than a complete ITS or a randomized learning-outcome study.

Intelligent Tutoring Systems and Adaptive LearningArtificial Intelligence in EducationArtificial Intelligence in Healthcare and Educationretrieval-augmented generationdomain modelintelligent tutoring systems

A Course-Scoped RAG Domain Model for an AI Teacher Assistant: Architecture and Pilot Evaluation

Abstract

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