Development of a generative AI ‐powered teachable agent for middle school mathematics learning: A design‐based research study

作者：Wanli Xing, Yukyeong Song, Chenglu Li, Zifeng Liu, Wangda Zhu, Hyunju Oh · 发表于：British Journal of Educational Technology · 年份：2025 · DOI：10.1111/bjet.13586 · 被引用次数：35 · 研究领域：Educational Games and Gamification、Intelligent Tutoring Systems and Adaptive Learning、Teaching and Learning Programming

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 powered by a recent advancement of generative AI technologies, yielding our teachable agent called ALTER‐Math . This study chronicles one usability testing and three cycles of iterative design and implementation process of ALTER‐Math . The three empirical studies involved a total of 320 middle school students and six teachers in authentic classroom settings. The first study was exploratory, focusing on the qualitative feedback from the students and teachers through open‐ended surveys, interviews and classroom observations. The second study yielded a medium‐high ( M = 3.26) quantitative survey result on students' perceived engagement and usability on top of the qualitative findings. Finally, the final study included pre‐ and post‐knowledge tests in a quasi‐experimental study design as well as student and teacher interviews. The final study revealed a bigger significant knowledge improvement in students who used ALTER‐Math compared to the control group, suggesting a positive impact of AI‐powered teachable agents on students' learning. The design implications learned from multiple iterations are discussed to inform the future design of AI‐powered learning technologies. Practitioner...