Development of a generative AI ‐powered teachable agent for middle school mathematics learning: A design‐based research study
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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 notes: What is already known about this topic Learning‐by‐teaching is a long‐standing effective pedagogical strategy to enhance students' domain knowledge and feelings of responsibility in learning. Various teachable agents have been developed and have demonstrated benefits in students' learning. Generative AI offers the potential to provide naturalistic, contextualised and adaptive conversations. What this paper adds Develops a novel generative AI‐powered teachable agent for middle school mathematics learning, called ALTER‐Math . Reports the iterative design process involving empirical classroom implementations of ALTER‐Math . Reveals a bigger significant improvement in the student's mathematical knowledge after using ALTER‐Math , compared to the control group. Implications for practice and/or policy Researchers can be inspired by this design example of a theoretically grounded generative AI learning technology. Educational technology designers could hear the real voices of students and teachers about the generative AI learning technologies. Researchers and educational technology designers could be directed by the design implications to the future design of AI‐powered learning technologies and teachable agents.
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Enhancing students' critical thinking and real-world problem-solving practices remains a key objective, with GenAI-supported interactive learning and analytical tasks designed to promote critical analysis, logical reasoning, and the generative use of knowledge to authentic challenges in science and mathematics.
“Additionally, enhancing students' critical thinking and real-world problem-solving practices remains a key objective. GenAI-supported interactive learning and analytical tasks are designed to promote critical analysis, logical reasoning, and the generative use of knowledge to authentic challenges in science and mathematics.”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [102]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredAI often acts as a dialogic coach or teachable agent that organizes a sequence of demonstration, explanation, questioning, and feedback.
“Beyond answering, AI often acts as a dialogic coach or teachable agent that organizes a sequence of demonstration, explanation, questioning, and feedback, as illustrated by the APLUS mathematics environment and curiosity question training agents [6,102,128].”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [102]
The paper describes ALTER-Math as an interactive mathematics teachable agent: its prompts ask students for further explanations, and its mentor agent evaluates their instructions and gives feedback (Conclusion and Additional information; page numbers unavailable). It does not establish that AI often follows the stated demonstration–explanation–questioning–feedback sequence; in this design, students explain mathematics to the agent, and the agent is instructed not to teach directly. (LLM, conf=0.83)
Experimental — accuracy not yet measuredGenAI serves as a cognitive scaffold by collecting student input in real time, adaptively modifying examples and prompts, and integrating support functions such as simplified language, summarization, and length control within the same workspace.
“First, GenAI serves as a cognitive scaffold. By leveraging conversational interfaces, the system collects student input in real time, adaptively modifies examples and prompts, and integrates support functions such as simplified language, summarization, and length control within the same workspace.”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [102]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
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- Collins, A., Joseph, D., & Bielaczyc, K. (2016). Design research: Theoretical and methodological issues. In Design-based research (pp. 15-42). Psychology Press.
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- Morgan Kaufmann.
Cited by
Sorted by citations
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Self-citationRho, J., Guo, R. S., Xing, W., & Li, C. (2026) · Computers & Education · doi:10.1016/j.compedu.2026.105766
Self-citation
- Authors
- Xing, W., Song, Y., Li, C., Liu, Z., Zhu, W., & Oh, H.
- Affiliations
- University of Florida (US)
- University of Utah (US)
- Corresponding author
- Xing, Wanli
- Venue
- British Journal of Educational Technology
- Publisher
- Wiley
- Published
- 2025-04-17
- Volume
- 56
- Issue
- 5
- Pages
- 2043-2077
- ISSN
- 0007-1013 (ISSN-L)
- 1467-8535
- OpenAlex
- W4409524257
- Cited by
- 50
- References
- 99
- Citations by year
- 2026: 36 · 2025: 14
- FWCI
- 19.98
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- British Journal of Educational Technology (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Education › Mathematics Education and Teaching Techniques
- Keywords
- generative AI, teachable agents, learning by teaching, junior high school mathematics, high school students, student engagement, usability, iterative design
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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