erdEducation Reform and Development2652-53642652-5372Bio-Byword Scientific Publishing Pty. Ltd.10.26689/erd.v7i10.12588ArticleA Closed-Loop Integration Model of AI Technology in Junior Secondary Mathematics Teachinghttps://artdesignp.com/journal/erd/7/10/10.26689/erd.v7i10.12588LiuYongbo,LiYue20257102025-11-14 The deep integration of digital transformation and artificial intelligence (AI) is driving profound changes in mathematics education. International forums like the 15th International Congress on Mathematical Education (ICME-15) have emphasized “reconstructing teaching paradigms through AI” as a central theme. However, current research often remains limited to a tool-oriented approach involving specific technologies, creating a disconnect between learning and teaching processes. This gap hinders a fundamental solution to the core challenge of balancing standardized education with personalized cultivation. To address this, this study proposes the “AI Dual-Loop Empowerment” model. This data-driven framework establishes a dynamic closed-loop system. Within the “student self-learning loop,” activities such as “preview” and “instant diagnosis” generate “learning data.” These data, in turn, drive the “teacher teaching loop,” where educators perform “learning analytics” and “implement interventions.” The outcomes of these interventions feed back into the students’ subsequent learning, creating a virtuous cycle in which “learning informs teaching and teaching promotes learning” and enabling continuous “data-driven decision.”

Yan K, Duan MH, Zhang YQ, 2025, Theoretical Evolution Framework and Trends of AI-Enabled Education. Tsinghua Journal of Education, 46(2): 33–42 + 84.

Ding L, Wu S, 2024, Digital Transformation of Education in China: A Review Against the Backdrop of the 2024 World Digital Education Conference. Science Insights Education Frontiers, 20(2): 3283–3299.

Ministry of Education of the People’s Republic of China, 2022, Mathematics Curriculum Standards for Compulsory Education. Beijing Normal University Press, Beijing.

Kang Y, Zhang J, Song C, 2024, Frontiers and Future Directions of International Mathematics Education Research in the Digital-Intelligent Era: A Review Based on Invited Parallel Reports at ICME-15. Journal of Mathematics Education, 33(5): 67–73.

National Council of Teachers of Mathematics, 2024, Artificial Intelligence and Mathematics Teaching, visited on August 30, 2025, https://www.nctm.org/standards-and-positions/Position-Statements/Artificial-IntelligenceandMathe-matics-Tea-ching/.

Zeng P, 2025, Effective Application of Artificial Intelligence in Junior Secondary Mathematics Teaching. Proceedings of the 2024 (Second Half) “Electronic Technology and Information Science” Symposium, Guangdong Provincial Electronics Society, Guangdong Shenzhen Bao’an Tangwei Wanli School, 2–5.

Tuanaya R, Retnawati H, 2024, Book Review: Empowering Primary Mathematics Educators: Strategies for Addressing Anxiety in the Classroom; Karen Wicks (2021) Tackling Anxiety in Primary Mathematics Teachers. Educational Studies in Mathematics, 117(1): 163–166.

Zhu Z, Wang M, 2023, Mathematics Education in the Age of Artificial Intelligence. Primary and Secondary School Classroom Teaching Research, (6): 1–6.

DBN, IPP, Alistair W, et al., 2018, SKOPE-IT (Shareable Knowledge Objects as Portable Intelligent Tutors): Overlaying Natural Language Tutoring on an Adaptive Learning System for Mathematics. International Journal of STEM Education, 5(1): 12.

Li W, Liu X, Zhang Q, et al., 2023, VR-Enhanced Cognitive Learning: Method, Framework, and Application. Applied Sciences, 13(8): 4756.

Grobler R, Ankiewicz P, 2021, The Viability of Diverting from a Linear to a Parallel Approach to the Development of PCK in Technology Teacher Education. International Journal of Technology and Design Education, 32(2): 1–21.

Guo W, Liang Z, Wang C, et al., 2025, Student-AI Collaborative Creative Problem-Solving: The Role of Human Agency. Computers & Education, 239: 105433–105433.

Haraldsrud A, Odden TOB, 2025, Bridging Artificial Intelligence and Real Intelligence: Self-Scaffolding Computational Modeling with Generative AI in Chemistry. Journal of Chemical Education, 102(10): 4255–4266.

Ferrario A, Facchini A, Termine A, 2024, Experts or Authorities? The Strange Case of the Presumed Epistemic Superiority of Artificial Intelligence Systems. Minds and Machines, 34: 30.

Laurah T, Shubha V, et al., 2023, Demystifying AI: Current State and Future Role in Medical Education Assessment. Academic Medicine: Journal of the Association of American Medical Colleges, 99(4S): S42–S47.

Lee H, Lin C, Wang W, et al., 2023, Precision Education via Timely Intervention in K-12 Computer Programming Course to Enhance Programming Skill and Affective-Domain Learning Objectives. International Journal of STEM Education, 10: 52.

Altaf S, Asad R, Ahmad S, et al., 2023, A Hybrid Framework of Deep Learning Techniques to Predict Online Performance of Learners During COVID-19 Pandemic. Sustainability, 15(15): 11731.

Hahn L, 2023, The Relationship of Instructor-Type and Intervention in a First-Year Seminar Using a Closed-Loop Analytics Model, thesis, Northern Arizona University.

AlZoubi D, 2022, Unpacking Instructors’ Use and Sensemaking Processes of Feedback Dashboards: A Human-Centered Approach, thesis, Iowa State University.

Nur N, 2021, Developing Temporal Machine Learning Approaches to Support Modeling, Explaining, and Sensemaking of Academic Success and Risk of Undergraduate Students, thesis, The University of North Carolina.

Ji H, 2025, Teaching with Conversational AI: Exploring Human-AI Collaboration and the Beliefs and Experiences of Language Teachers, thesis, Temple University.

Hussain D, Rasool D, Tabassum S, 2025, Human-AI Collaboration in Education: Rethinking the Role of Teachers and Learners in the Age of Intelligent Technologies. Academia International Journal for Social Sciences, 4(3): 2643–2653.