Work

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

Year: 2026

Type: conference-paper

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.

Source: International Symposium on Computer Science and Educational Technology (ISCSET)

DOI: 10.1109/iscset70652.2026.11722986

Authors: Battsetseg Erdenebat, Uranchimeg Tudevdagva, Amarzaya Amartuvshin, Lomakin Nikolai Ivanovich, Boynazarov Ilkhom Makhmudovich

Institutions: Mongolian University of Science and Technology, National University of Mongolia, Volgograd State Technical University, Tashkent University of Information Technology

Language: English

Topic: Intelligent Tutoring Systems and Adaptive Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: hybrid

OpenAlex ID: W7221725239

FWCI:

Cites: 16

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Related to: 10