@inproceedings{n-s-2026-retrieval,
title = "A Retrieval-Augmented Generation Approach for Enhancing Pedagogical Support in Education",
author = "N, Ramkumar and
S, Rajendran",
editor = "Ponnusamy, Rahul and
Kumaresan, Prasanna Kumar and
Rajiakodi, Saranya and
B, Premjith and
MC, Shunmuga Priya and
Murugappan, Abirami and
G, Jyothish Lal and
V, Sowmya and
Chakravarthi, Bharathi Raja",
booktitle = "Proceedings of the Fifth Workshop on Multimodal Machine Learning in Low-resource Languages",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.mmlow-main.3/",
pages = "28--39",
ISBN = "979-8-89176-445-3",
abstract = "The manual creation of high-quality study materials such as summaries, question{--}answer sets, and flashcards remains a time-consuming task for both educators and learners. While large language models can generate such content rapidly, they frequently suffer from hallucinations and lack grounding in the source material. This paper presents a Retrieval-Augmented Generation (RAG) framework designed to address these limitations by transforming educational documents into pedagogically structured learning resources. The system integrates document preprocessing, semantic chunking, embedding-based retrieval, and a locally deployed language model to produce concise summaries, targeted questions, and flashcards. A cross-sectional evaluation involving 48 undergraduate students and 12 educators was conducted to assess usability, accuracy, cognitive support, and overall pedagogical value. Findings indicate that the AI Study Agent is perceived as supportive of comprehension and revision activities, and objective evaluation supports factual consistency relative to non-retrieval generation. Challenges related to output accuracy, oversimplification, and user trust remain consistent with limitations identified in existing GenAI literature. The study concludes that while the system demonstrates strong potential to support teaching and learning workflows, its perceived effectiveness relies on responsible use, educator oversight, and continued refinement to ensure pedagogical alignment and reliability."
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%0 Conference Proceedings
%T A Retrieval-Augmented Generation Approach for Enhancing Pedagogical Support in Education
%A N, Ramkumar
%A S, Rajendran
%Y Ponnusamy, Rahul
%Y Kumaresan, Prasanna Kumar
%Y Rajiakodi, Saranya
%Y B, Premjith
%Y MC, Shunmuga Priya
%Y Murugappan, Abirami
%Y G, Jyothish Lal
%Y V, Sowmya
%Y Chakravarthi, Bharathi Raja
%S Proceedings of the Fifth Workshop on Multimodal Machine Learning in Low-resource Languages
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-445-3
%F n-s-2026-retrieval
%X The manual creation of high-quality study materials such as summaries, question–answer sets, and flashcards remains a time-consuming task for both educators and learners. While large language models can generate such content rapidly, they frequently suffer from hallucinations and lack grounding in the source material. This paper presents a Retrieval-Augmented Generation (RAG) framework designed to address these limitations by transforming educational documents into pedagogically structured learning resources. The system integrates document preprocessing, semantic chunking, embedding-based retrieval, and a locally deployed language model to produce concise summaries, targeted questions, and flashcards. A cross-sectional evaluation involving 48 undergraduate students and 12 educators was conducted to assess usability, accuracy, cognitive support, and overall pedagogical value. Findings indicate that the AI Study Agent is perceived as supportive of comprehension and revision activities, and objective evaluation supports factual consistency relative to non-retrieval generation. Challenges related to output accuracy, oversimplification, and user trust remain consistent with limitations identified in existing GenAI literature. The study concludes that while the system demonstrates strong potential to support teaching and learning workflows, its perceived effectiveness relies on responsible use, educator oversight, and continued refinement to ensure pedagogical alignment and reliability.
%U https://aclanthology.org/2026.mmlow-main.3/
%P 28-39
Markdown (Informal)
[A Retrieval-Augmented Generation Approach for Enhancing Pedagogical Support in Education](https://aclanthology.org/2026.mmlow-main.3/) (N & S, MMLow 2026)
ACL