# Generative AI for Enhancing Active Learning in Education: A Comparative Study of GPT-3.5 and GPT-4 in Crafting Customized Test Questions

arXiv: https://arxiv.org/abs/2406.13903 · HTML: https://mlsanity.com/paper/2406.13903/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 24 references that are in the map.
- First posted on arXiv 20 Jun 2024.

## Builds on (most cited first, 3 of 3)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)

## Built on by (most cited first, 8 of 8)

- [EduBench: A Comprehensive Benchmarking Dataset for Evaluating Large Language Models in Diverse Educational Scenarios](https://mlsanity.com/paper/2505.16160/) (2025)
- [Evolution of AI in Education: Agentic Workflows](https://mlsanity.com/paper/2504.20082/) (2025)
- [Advancing Object Detection in Transportation with Multimodal Large Language Models (MLLMs): A Comprehensive Review and Empirical Testing](https://mlsanity.com/paper/2409.18286/) (2024)
- [Pedagogy-driven Evaluation of Generative AI-powered Intelligent Tutoring Systems](https://mlsanity.com/paper/2510.22581/) (2025)
- [Active Learning Methods for Efficient Data Utilization and Model Performance Enhancement](https://mlsanity.com/paper/2504.16136/) (2025)
- [Unpacking Generative AI in Education: Computational Modeling of Teacher and Student Perspectives in Social Media Discourse](https://mlsanity.com/paper/2506.16412/) (2025)
- [The Impact of Role Design in In-Context Learning for Large Language Models](https://mlsanity.com/paper/2509.23501/) (2025)
- [PeerCoPilot: A Language Model-Powered Assistant for Behavioral Health Organizations](https://mlsanity.com/paper/2511.21721/) (2025)

## Abstract (arXiv)

This study investigates how LLMs, specifically GPT-3.5 and GPT-4, can develop tailored questions for Grade 9 math, aligning with active learning principles. By utilizing an iterative method, these models adjust questions based on difficulty and content, responding to feedback from a simulated 'student' model. A novel aspect of the research involved using GPT-4 as a 'teacher' to create complex questions, with GPT-3.5 as the 'student' responding to these challenges. This setup mirrors active learning, promoting deeper engagement. The findings demonstrate GPT-4's superior ability to generate precise, challenging questions and notable improvements in GPT-3.5's ability to handle more complex problems after receiving instruction from GPT-4. These results underscore the potential of LLMs to mimic and enhance active learning scenarios, offering a promising path for AI in customized education. This research contributes to understanding how AI can support personalized learning experiences, highlighting the need for further exploration in various educational contexts