Aims and Scope

With the rapid advancement of data mining, artificial intelligence, data science education is entering a new era that demands innovative pedagogy, adaptive learning technologies, and interdisciplinary training. The ICDM 2026 Education Track invites high-quality contributions on effective methods for teaching, learning, and democratizing data mining and AI for the next generation of students and practitioners. This track provides a forum for sharing insights, tools, and best practices that advance data mining education, emphasizing approaches that bridge theory with real-world practice, promote responsible AI literacy, and leverage emerging technologies such as generative AI and intelligent tutoring systems. We particularly welcome evidence-based strategies that link data mining principles with pedagogical innovation and support cultivating high-quality global STEM talents.

Topics of Interest

The ICDM 2026 Education Track seeks original submissions on innovative pedagogical models and practical teaching strategies that support the development of data mining and AI education. Topics include, but are not limited to, the following themes:

• Innovative Teaching Methods: Novel pedagogical approaches for teaching data mining, including generative-AI-enhanced classroom innovations and inquiry-, project-, or flipped-based STEM teaching methodologies.

• Experiential Learning: Experiential education through internships, capstone projects, hackathons, real-world datasets, cloud infrastructures, open-source platforms, and strategies for teaching responsible, fair, and trustworthy data mining.

• Curriculum Design: Curriculum models that integrate theory and practice in data mining and AI, featuring modular and scalable designs supported by AI-enhanced courseware, virtual labs, and interactive learning environments.

• Talent Cultivation: Cultivating high-level data science talents through mentorship and research-oriented education, while promoting interdisciplinary training across domains such as healthcare, finance, engineering, and the social sciences.

• Educational Data Mining (EDM) & Learning Analytics: Data-driven methods for understanding student learning behaviors, including predictive modeling, automated feedback, assessment analytics, and large-scale student data mining for adaptive and personalized learning.

The topics include, but are not limited to:

• Teaching data mining, machine learning, and deep learning

• Computational thinking and coding education

• AI-enhanced courseware, virtual labs, and intelligent tutoring systems

• Responsible AI education, including fairness, interpretability, and trustworthy AI

• Experiential learning through real-world datasets, cloud platforms, and open-source ecosystems

• Educational data mining and learning analytics

Submission Guidelines

Authors are invited to submit original, unpublished research that has not been submitted to other venues. Submissions should follow the ICDM 2026 formatting guidelines and will be peer-reviewed based on relevance, academic rigor, innovation, and potential impact on the data mining education community. Further submission guidelines are given at http://icdm2026.neu.edu.cn.