  The rise of AI has brought remarkable advancements in education, with AI
models demonstrating their ability to analyse and provide instructive solutions
to complex problems. This study compared and analysed the responses of four
Generative AI-powered chatbots (GenAIbots) - ChatGPT-3.5, ChatGPT-4, Bing Chat,
and Bard - within the constructivist theoretical framework. Using a single-case
study methodology, interaction logs between the GenAIbots and a simulated
student in Physics learning scenarios were analysed. The GenAIbots were
presented with conceptually dense Physics problems to promote deep
understanding. The qualitative analysis focused on tutor traits such as
subject-matter knowledge, empathy, assessment emphasis, facilitation skills,
and comprehension of the learning process. Findings showed that all GenAIbots
functioned as agents-to-think-with, fostering critical thinking,
problem-solving, and subject-matter knowledge. ChatGPT-4 stood out for
demonstrating empathy and a deep understanding of the learning process.
However, inconsistencies and shortcomings were observed, highlighting the need
for human intervention in AI-assisted learning. In conclusion, while GenAIbots
have limitations, their potential as agents-to-think-with in Physics education
offers promising prospects for revolutionising instruction.
