Development Of A Generative AI-Powered Teachable Agent For Middle School Mathematics Learning: A Design-Based Research Study Abstract This paper reports on a design-based research (DBR) study that aims to devise an artificial intelligence (AI)-powered teachable agent that supports secondary school students’ learning- by-teaching practices of mathematics learning content. A long-standing pedagogical practice of learning-by-teaching is […]
Analyzing Student Attention And Acceptance Of Conversational AI For Math Learning: Insights From A Randomized Controlled Trial
Analyzing Student Attention And Acceptance of Conversational AI For Math Learning: Insights From A Randomized Controlled Trial Abstract The significance of nurturing a deep conceptual understanding in math learning cannot be overstated. Grounded in the pedagogical strategies of induction, concretization, and exemplification (ICE), we designed and developed a conversational AI using both ruleand generation-based techniques […]
Automated Feedback For Student Math Responses Based On Multi-Modality And Fine-Tuning
Automated Feedback For Student Math Responses Based On Multi-Modality And Fine-Tuning Abstract Open-ended mathematical problems are a commonly used method for assessing students’ abilities by teachers. In previous automated assessments, natural language processing focusing on students’ textual answers has been the primary approach. However, mathematical questions often involve answers containing images, such as number lines, […]
Retrieval-Augmented Generation To Improve Math Question-Answering: Trade-Offs Between Groundedness And Human Preference
Interactive question-answering (QA) with tutors has shown to be an effective way for middle school math students to learn. While not all students have access to a tutor, large language models make it possible to automate portions of the tutoring process–including interactive QA to support students’ conceptual discussion of mathematical concepts. Some have questioned how LLM responses can be better aligned with a school’s curriculum. In this paper, Levonian and colleagues explore how retrieval-augmented generation (RAG) can help improve response quality by incorporating textbook information and other educational resources, while also identifying trade-offs of using RAG.