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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.06963 |
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| _version_ | 1866915990986555392 |
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| author | Ostrowska, Anna Kukla, Michał Majstrak, Gabriela Opala, Jan Pergała, Sebastian Skwarek, Jan Wróblewska, Anna |
| author_facet | Ostrowska, Anna Kukla, Michał Majstrak, Gabriela Opala, Jan Pergała, Sebastian Skwarek, Jan Wróblewska, Anna |
| contents | This demo paper describes the development of the AI Teaching \& Learning Assistant, a modular Moodle plugin that leverages Retrieval-Augmented Generation (RAG) to deliver high-quality, hallucination-free education. The system employs a dual-centric design, providing students with interactive, Socratic-based tutoring and educators with a "human-in-the-loop" workspace for supervised content generation. By grounding Large Language Model (LLM) responses in teacher-provided materials, the assistant addresses the risks of misinformation while encouraging deep conceptual mastery. Evaluation via the Ragas (LLM-as-a-Judge) framework and a preliminary user study confirms its effectiveness, achieving faithfulness scores up to 0.97 and a 4.00/5.00 recommendation rate. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_06963 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | From Surface Learning to Deep Understanding: A Grounded AI Tutoring System for Moodle Ostrowska, Anna Kukla, Michał Majstrak, Gabriela Opala, Jan Pergała, Sebastian Skwarek, Jan Wróblewska, Anna Human-Computer Interaction Artificial Intelligence Computation and Language Information Retrieval This demo paper describes the development of the AI Teaching \& Learning Assistant, a modular Moodle plugin that leverages Retrieval-Augmented Generation (RAG) to deliver high-quality, hallucination-free education. The system employs a dual-centric design, providing students with interactive, Socratic-based tutoring and educators with a "human-in-the-loop" workspace for supervised content generation. By grounding Large Language Model (LLM) responses in teacher-provided materials, the assistant addresses the risks of misinformation while encouraging deep conceptual mastery. Evaluation via the Ragas (LLM-as-a-Judge) framework and a preliminary user study confirms its effectiveness, achieving faithfulness scores up to 0.97 and a 4.00/5.00 recommendation rate. |
| title | From Surface Learning to Deep Understanding: A Grounded AI Tutoring System for Moodle |
| topic | Human-Computer Interaction Artificial Intelligence Computation and Language Information Retrieval |
| url | https://arxiv.org/abs/2605.06963 |