Research Associate
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Project Title Data and Theory Driven Artificial Intelligence to Boost the Science of Learning (AI4SoL)
Project Introduction The use of educational technologies is increasingly becoming more ubiquitous in mathematics education. While artificial intelligence (AI) has been integrated into the development of educational technologies, for example Intelligent Tutoring Systems (ITSs), the recent advancements in generative AI (gen AI) promise personalized learning in a more natural way. In particular, leveraging the natural language capabilities of large language models (LLMs)
- a type of gen AI – to enable dialogic practice is a promising nascent field of study. While mathematics learning requires both conceptual and procedural knowledge, students learn mathematics through sense-making of these types of knowledge through problem-solving. This requires students to access and/or construct their own relevant mathematics knowledge, create representations of said knowledge, and map their representations to the knowledge. Besides using these steps to problem-solve, mathematics learning also requires students to communicate their problem-solving strategies and solutions. From a socio-constructivist perspective, co-constructing knowledge requires a dialogic exchange between teacher and students, and feedback from teachers is essential in mathematics discourse. Based on Thurlings et al.’s models of feedback processes, most feedback in computer systems is cognitivist in nature. The advancements in LLMs appear promising in bridging this dialogic gap in feedback and learning via computer systems. This study aims to test the efficacy of LLMs in teaching mathematics word problem solving through dialogue in structured inquiry with/without adaptive learning tasks compared to self-directed problem-solving in improving mathematical problem-solving accuracy, metacognition and self-regulation, and long-term
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