STEM learning is characterized by complex disciplinary practices—including modeling, experimentation, data analysis, mathematical reasoning, and computational thinking—that demand forms of assessment and instructional support not easily captured by traditional educational technologies. AI systems are increasingly being developed to support these practices through adaptive tutoring, automated feedback on open-ended work, simulation-based learning, and generative tools that scaffold inquiry and problem solving. At the same time, these applications raise acute concerns about validity, transparency, bias, over-automation, and the displacement of human judgment.

The Cambridge Handbook of Artificial Intelligence in STEM Education will: (1) synthesize foundational theories, empirical research, and design approaches; (2) clarify what is currently known, what remains contested, and where evidence is insufficient; (3) establish shared conceptual and methodological frameworks; and (4) set forward-looking research and policy recommendations for responsible and effective AI use in STEM education.

Chapters will be 8,000–10,000 words without references, original (not previously published), and organized within six thematic parts:
(1) Foundations and Conceptual Frameworks
(2) Learning Processes and Pedagogical Reasoning
(3) AI-Enabled Instructional Approaches
(4) Assessment, Measurement, and Feedback
(5) Equity, Ethics, and Fairness
(6) Policy, Governance, and Systems-Level Implementation

submissions open

Chairs & editors

• Christina Chhin — University of Georgia
• Xiaoming Zhai

Key dates

Chapter Abstract and Outline Submission Due	Sunday, June 21, 2026 (23:59)
First Draft of Chapter Due	Friday, January 1, 2027 (23:59)
Peer Review of Chapters Due	Friday, February 19, 2027 (23:59)
Second Draft of Chapter Due	Friday, March 19, 2027 (23:59)
Final Draft of Chapter Due	Friday, April 30, 2027 (23:59)

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