Creating adaptive learning pathways for inquiry-based learning in school science using generative AI large language models

This article explores the potential of generative artificial intelligence (GAI) tools to create adaptive learning pathways for inquiry-based learning (IBL) science instruction. The research compares tailored pathways from ChatGPT 4.0 and Llama 3.1 for differentiated inquiry instruction in science subjects. A qualitative approach was employed using a case study design to provide thick descriptions of Pathways from ChatGPT 4.0 and Llama3.1in supporting teachers to cater to personalised IBL instruction. Five science teachers were purposefully selected to generate and implement adaptive learning pathways for IBL instruction. Content analysis of GAI-generated pathways and teacher semi-structured interviews revealed insights into the performance of the GAI models and the usefulness of the pathways in teaching inquiry lessons. The findings revealed that LLM-based GAI engines could be used to address the individual learning needs of students in IBL science classrooms. Adaptive learning pathways showed great potential in differentiating instruction. ChatGPT 4.0 was seen to be more robust when compared with Llama 3.1. Challenges observed included difficulty keeping track of differentiated assessments, time management, ethical concerns and AI biases. These findings have implications for exploiting GAI tools when crafting IBL instruction. Some recommendations for teacher practice and research in developing adaptive learning pathways are also discussed.