Can Machines Think? Assessing the Accuracy of GenAI Chatbots in a Physics University Entrance Exam

Abstract

The rapid advancement of artificial intelligence (AI) over the past few years has led to the development of Generative AI (GenAI) tools with enhanced capabilities, including multimodal functionality, reduced susceptibility to hallucinations, and real-time access to internet resources. Past studies have revealed that GenAI tools are used in daily life across various fields, including education, healthcare, engineering, and software development. School learners are increasingly relying on them for their academic activities. There is a paucity of empirical research on the accuracy of these tools' responses, particularly in the field of physics education. This mixed-method case study aims to evaluate the accuracy of responses from ChatGPT and Google Gemini chatbots in answering physics university entrance exams in South Africa. Technological Pedagogical Content Knowledge and Webb’s Depth of Knowledge were used to construct the theoretical framework. The research instrument used in this study was the 2024 university entrance physics exam paper in South Africa. The question paper was loaded into each chatbot, then they were prompted to respond to the questions. Two expert examiners assessed the responses of the chatbots. The performance of each chatbot was compared to that of the learners who took the exams. The findings were that the chatbots outperformed the learners. This study's findings suggest that these chatbots can serve as teaching assistants to support learners in exam preparation and formative assessment tasks. Learners should employ critical thinking skills to assess the responses they receive from chatbots during interactions.

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