Rana, S. P. ORCID: https://orcid.org/0000-0002-8014-8122, Dey, M., Patel, P., Requena, J. and Fu, C. (2025) Prompting and rag vs. student engagement and comprehension in educational technology. In: 2025 IEEE Global Engineering Education Conference (EDUCON), London, United Kingdom, 2025. IEEE Xplore . Institute of Electrical and Electronics Engineers, Inc. (IEEE), Piscataway, New Jersey, pp. 1-7. ISBN 979-8331539498 (doi:10.1109/EDUCON62633.2025.11016579)

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Abstract

Generative AI (GenAI) has emerged as a valuable tool in education technology, offering potential to enhance learning and teaching processes. While concerns like fraudulent practices, algorithmic bias, privacy issues, and overreliance on technology persist, GenAI's benefits are significant when used strategically. It is essential, however, to view GenAI as a supplementary aid for students and educators rather than a replacement for human-led teaching. Effective use of GenAI requires thoughtful implementation, including techniques like prompting and retrieval-augmented generation (RAG). Prompting involves formulating questions or tasks for the AI, while RAG enhances the AI's ability to retrieve relevant information based on its training. This study focuses on the relationship between GenAI and students, excluding educators' roles. A mixed-method survey evaluated students' interactions with GenAI-generated answers in two scenarios: one where they had prior topic knowledge and another where they did not. Five chatbots-ChatGPT, Gemini, Copilot, Perplexity AI, and Sana AI-were tested with varied prompts. Results showed that students benefit most when they are engaged and have foundational topic knowledge. These findings underscore the role of educators in fostering student engagement and guiding effective GenAI use. By prioritizing understanding, educators ensure GenAI enhances learning, reinforcing that AI should support, not replace, education.

Item Type:	Conference Proceedings
Title of Proceedings:	2025 IEEE Global Engineering Education Conference (EDUCON), London, United Kingdom, 2025
Uncontrolled Keywords:	training, surveys, privacy, Generative AI, retrieval augmented generation, educational technology, problem-solving, engineering education
Subjects:	L Education > L Education (General) T Technology > T Technology (General)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Engineering (ENG)
Last Modified:	11 Nov 2025 10:54
URI:	https://gala.gre.ac.uk/id/eprint/51082

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