Ning Ding

Message from the Guest Editor

Mathematics plays a central role in shaping analytical thinking, scientific reasoning, and problem-solving capacity across disciplines. At the same time, advances in education science continue to transform how mathematical knowledge is taught, learned, modeled, and applied in diverse learning environments.

This Special Issue, “Teaching, Learning & Modeling: Education Science Meets Mathematics,” seeks to explore the intersection between mathematical theory, pedagogical innovation, and modeling approaches that enhance learning outcomes. We aim to bring together research that advances both the conceptual foundations of mathematics and the educational strategies that support effective instruction and engagement.

We particularly welcome contributions addressing mathematical modeling in education, curriculum development, digital learning environments, quantitative reasoning, assessment methodologies, and interdisciplinary integration of mathematics with science and technology. Studies that bridge theoretical mathematics with classroom application or educational policy perspectives are especially encouraged.

By fostering dialogue between mathematicians and education researchers, this Special Issue aims to promote rigorous scholarship that improves teaching practices, supports student understanding, and strengthens the role of mathematical modeling in addressing real-world challenges.

I look forward to receiving high-quality contributions that advance the synergy between mathematics and education science.

Dr. Ning Ding
Department of Mathematics
Kangwon National University, South Korea
Guest Editor

Ding Ning holds a PhD from Kangwon National University, South Korea. Her research focuses on modeling learning and behavioral mechanisms in human–AI collaborative contexts. From an interdisciplinary perspective integrating educational science and design research, she examines cognitive appraisal, motivational formation, and behavioral regulation processes in generative AI environments, aiming to explain how technology influences learning and work adaptation through multi-path models.

Methodologically, she employs mixed methods combining Structural Equation Modeling, fuzzy-set Qualitative Comparative Analysis, and grounded theory, with the goal of transforming complex learning and behavioral processes into interpretable mechanism-based models. In recent years, her research topics have included AI-assisted learning, technology adoption and resistance, cognitive load, and meaning construction. Related work has been published in journals such as Scientific Reports, Acta Psychologica, Frontiers in Psychology, and Sustainability, and she also serves as a reviewer for multiple international academic journals.

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1. Ding, N., Chen, M., & Hu, L. (2025). Examining the impact of big five personality traits on generation Z designers’ subscription to paid AI drawing tools using SEM and FsQCA. Scientific Reports, 15(1), 17587.
2. Ding, N., Hu, L., Zhao, Q., Kim, K. T., & Chen, M. (2025). Metaverse? No, thanks! Exploring the mechanisms behind Generation Z’s resistance behavior. Frontiers in Psychology, 16, 1672330.
3. Ding, N., Chen, M., & Hu, L. (2025). The impact of personality traits and AI literacy on the adoption intentions of AI among design faculty in Chinese higher education. Acta Psychologica, 260, 105709.
4. Ding, N., Chen, M., & Hu, L. (2025). The design industry in the AI era: How AI awareness and AI literacy influence the innovative work behavior of Chinese Generation Y designers. Acta Psychologica, 260, 105650.
5. Ding, N., Hu, L., Kim, K. T., & Chen, M. (2026). When Generative Artificial Intelligence Becomes a Colleague: Dual Pathways of Empowerment and Depletion in University Design Teachers’ Work Behaviors. Sustainability, 18(4), 1775.
6. Ding, N., Yun, K. J. & Chen, M. (2024). Study on the Impact of Packaging Design Elements and Perceived Value of Sugar-Free Tea Beverages on Consumer Purchasing Behavior. Korea International Commercial Review, 39(2), 229-254.
7. Zhao, T., Ding, N., Gu, J., & Chen, M. (2026). Resource Endowments, Value Cognition, and Strategic Risk-Taking: Explaining Carbon-Reduction Investments in Port Enterprises. Systems, 14(2), 203.
8. Li, Y., Ding, N., Zhao, T., & Chen, M. (2025). What Drives or Hinders the Adoption of Sustainable Smart Logistics in Rural Areas?—A Mixed-Methods Analysis. Sustainability, 17(14), 6626.
9. Hu, L., Chen, M., & Ding, N. (2025). Factors influencing digital media designers’ subscription to premium versions of AI drawing tools through a mixed methods study. Scientific Reports, 15(1), 15994