Special Session: Artificial Intelligence in Mathematics Education: Innovations in Teaching, Learning, Assessment, and Problem Solving

Short description of the proposed topic: Artificial Intelligence is rapidly reshaping mathematics education through Generative AI, Large Language Models, intelligent tutoring, adaptive learning, automated assessment, learning analytics, and AI-assisted problem solving. This Special Session will bring together researchers, mathematics educators, AI specialists, and educational technology scholars to examine evidence-based and pedagogically meaningful uses of AI in mathematics teaching and learning. It welcomes theoretical, technological, and empirical studies on personalized learning, mathematical reasoning, assessment and feedback, teacher development, modeling, and human-AI collaboration, as well as critical work on accuracy, explainability, academic integrity, privacy, and equity. The session directly aligns with AsiaEdu 2026 themes on Artificial Intelligence in Education and Emerging Technologies in Learning, especially personalized learning, adaptive instruction, automated assessment, teacher professional development, and technology-supported STEM education.

Novelty and motivation:Mathematics poses distinctive challenges for AI because learning depends not only on final answers, but also on valid multi-step reasoning, proof, symbolic and graphical representations, modeling choices, and transparent feedback. Generative AI can support these processes but can also produce plausible yet incorrect derivations or encourage superficial answer-seeking. A dedicated session is therefore needed to connect mathematics education with applied mathematics, optimization, computer science, learning analytics, and educational technology. Its focused scope on the design, verification, and empirical evaluation of AI-supported mathematical learning environments enables deeper technical and pedagogical discussion than a broad regular track.

Related Topics for this Session:
• Generative AI and Large Language Models in Mathematics Education
• Intelligent Tutoring Systems and Adaptive/Personalized Mathematics Learning
• AI-Supported Mathematical Reasoning, Problem Solving, Modeling, and Optimization
• Automated Assessment, Feedback, Learning Analytics, and Educational Data Mining
• AI-Assisted Instructional Design and Teacher Professional Development
• Human-AI Collaboration, AI Literacy, Academic Integrity, Ethics, and Responsible Use

Submission System: http://confsys.iconf.org/submission/asiaedu2026 (Papers recommended by the Special Session Chairs: please log in to the system and select the corresponding special session for submission.)

Short Biography of Organizers & Chairs with Photo

Dr. Hoan Van Tran丨Head of Mathematics, Lac Hong University, Dong Nai, Vietnam

Dr. Hoan Van Tran is Head of Mathematics at Lac Hong University, Dong Nai, Vietnam. He holds a Master's degree in Applied Mathematics and a Ph.D. in the Theory and Methodology of Mathematics Teaching from the Vietnam Institute of Educational Sciences. His research focuses on mathematics education and mathematical optimization, including university mathematics teaching, outcome-based education, mathematical problem solving, linear programming, and optimization algorithms. He has published peer-reviewed research and presented at national and international conferences. His combined background in mathematics education and optimization supports interdisciplinary work on effective and responsible AI use in mathematics teaching, learning, assessment, and problem solving.

Prof. (Dr.) Praveen Agarwal丨Department of Mathematics, Anand International College of Engineering, Jaipur, India

Prof. (Dr.) Praveen Agarwal is a mathematician, educator, and academic leader at Anand International College of Engineering, Jaipur, India. He earned his Master's degree from the University of Rajasthan in 2000 and his Ph.D. in Mathematics from Malaviya National Institute of Technology (MNIT) Jaipur in 2006. With more than two decades of research and pedagogical experience, his interests include special functions, fractional calculus, numerical analysis, differential and difference equations, inequalities, and fixed point theory. He has published more than 480 research papers with international collaborators and has authored or edited research monographs and books. He has been listed among the World's Top 2% Scientists in multiple years. He has delivered invited, plenary, and keynote lectures internationally, served in editorial roles for numerous journals, and organized international conferences, workshops, seminars, and summer schools. His expertise in applied mathematics and international research collaboration brings a strong computational and interdisciplinary perspective to AI-enhanced mathematics education.

Prof. Syed Abbas丨School of Mathematical and Statistical Sciences, Indian Institute of Technology Mandi, Mandi, H.P. 175005, India

Prof. Syed Abbas is a Professor in the School of Mathematical and Statistical Sciences at the Indian Institute of Technology Mandi, India. He received his Ph.D. from IIT Kanpur in 2009. His research focuses on differential equations, ecological modeling, nonlinear and functional differential equations, and mathematical modeling. He has extensive experience in research, teaching, academic projects, and professional service, including international conference organization. His expertise in applied mathematics, nonlinear analysis, and mathematical modeling contributes a complementary perspective to AI-assisted mathematical reasoning, computational problem solving, modeling, and the interface between mathematical research and education.