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Multi-sourced AI Tutor for Non-Native English Speaker Students — arXiv:2610.22163

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.22163
License (as archived): CC0-1.0
Archived: 2026-10-16T09:05:23.757Z
SHA-256 of the archived text: c27aed2b6e620586a0985e08a8fb5265035aa2dfb662f2c23d589d9b2d28304a
Stable URL of this page: https://metraipolis.com/documento/dc_bfae2e45967b

How to cite

Multi-sourced AI Tutor for Non-Native English Speaker Students — arXiv:2610.22163 — arXiv (en), https://arxiv.org/abs/2610.22163 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bfae2e45967b (Metraipolis archive dc_bfae2e45967b, sha256 c27aed2b6e620586a0985e08a8fb5265035aa2dfb662f2c23d589d9b2d28304a)

BibTeX

@misc{dcbfae2e45967b,
  title = {Multi-sourced AI Tutor for Non-Native English Speaker Students — arXiv:2610.22163},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.22163},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bfae2e45967b; sha256 c27aed2b6e620586a0985e08a8fb5265035aa2dfb662f2c23d589d9b2d28304a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.22163. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Large language models (LLMs) are increasingly being adopted as teaching tools in computing education, particularly for developing AI tutors capable of answering student questions. With the rise in enrollment of international students in computing programs, researchers have begun to explore the difficulties that non-native English speaker (NNES) students face in computing courses due to language barriers. To aid such students, we developed a general-purpose AI tutor system that can process student questions in foreign languages (currently focused on Mandarin) and provide bilingual responses. Our system also incorporates the functionality of generating a glossary of technical terms and their explanations to enhance comprehension. Our approach utilizes a specialized Retrieval-Augmented Generation (RAG) pipeline with a novel question extraction mechanism, enabling us to effectively utilize a diverse array of course materials for answering student queries. We evaluated our AI tutor using 91 Piazza posts, demonstrating that it could answer 83.52% of the posts correctly and in a helpful manner. The tutor also provided high-quality translations for 76.92% of the questions and generated technical term glossaries for 64.47% of them. Our findings underscore the potential of multi-source RAG pipeline in enhancing AI tutor systems, offering support for non-native speakers that are challenging to provide with traditional supporting staff of a computing course.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.22163 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T08:44:17.820Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).