Prompting and Rag Vs. Student Engagement and Comprehension in Educational Technology
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.
Authors
- Preeti Patel (ORCID: https://orcid.org/0000-0003-3875-453X)
- Maitreyee Dey (ORCID: https://orcid.org/0000-0002-6862-7032)
- Soumya Prakash Rana (ORCID: https://orcid.org/0000-0002-8014-8122)
- Colin Fu (ORCID: https://orcid.org/0009-0008-1240-4957)
- Jesus Requena
Institutions
- London Metropolitan University (GB)
- Queen Mary University of London (GB)
- Medway School of Pharmacy (GB)
- University College London (GB)
Publication Details
- Published
- 2025-04-22
- DOI
- https://doi.org/10.1109/educon62633.2025.11016579
- Citations
- 1
- Primary Topic
- Online and Blended Learning
- Type
- article
- Field-Weighted Citation Impact
- 8.93