Quantifying learning-style adaptation in effectiveness of LLM teaching
Ruben Weijers, Gabrielle Fidelis de Castilho, Jean-François Godbout, Reihaneh Rabbany, Kellin Pelrine
Abstract
This preliminary study aims to investigate whether AI, when prompted based on individual learning styles, can effectively improve comprehension and learning experiences in educational settings. It involves tailoring LLMs baseline prompts and comparing the results of a control group receiving standard content and an experimental group receiving learning style-tailored content. Preliminary results suggest that GPT-4 can generate responses aligned with various learning styles, indicating the potential for enhanced engagement and comprehension. However, these results also reveal challenges, including the model’s tendency for sycophantic behavior and variability in responses. Our findings suggest that a more sophisticated prompt engineering approach is required for integrating AI into education (AIEd) to improve educational outcomes.- Anthology ID:
- 2024.personalize-1.10
- Volume:
- Proceedings of the 1st Workshop on Personalization of Generative AI Systems (PERSONALIZE 2024)
- Month:
- March
- Year:
- 2024
- Address:
- St. Julians, Malta
- Editors:
- Ameet Deshpande, EunJeong Hwang, Vishvak Murahari, Joon Sung Park, Diyi Yang, Ashish Sabharwal, Karthik Narasimhan, Ashwin Kalyan
- Venues:
- PERSONALIZE | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 112–118
- Language:
- URL:
- https://aclanthology.org/2024.personalize-1.10
- DOI:
- Cite (ACL):
- Ruben Weijers, Gabrielle Fidelis de Castilho, Jean-François Godbout, Reihaneh Rabbany, and Kellin Pelrine. 2024. Quantifying learning-style adaptation in effectiveness of LLM teaching. In Proceedings of the 1st Workshop on Personalization of Generative AI Systems (PERSONALIZE 2024), pages 112–118, St. Julians, Malta. Association for Computational Linguistics.
- Cite (Informal):
- Quantifying learning-style adaptation in effectiveness of LLM teaching (Weijers et al., PERSONALIZE-WS 2024)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2024.personalize-1.10.pdf