Journal of Artificial Intelligence and Science Communication

Review

Beyond Automation: Pedagogical Strategies for Meaningful Human-AI Collaboration in the Classroom

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https://doi.org/10.54963/jaisc.v2i1.2226
Volume 2 Issue 1 (2026): In Progress
Copyright (c) 2026 Dongxing Yu
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This work is licensed under a Creative Commons Attribution 4.0 International License.

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Journal of Artificial Intelligence and Science Communication (JAISC)  publishes accepted manuscripts under Creative Commons Attribution 4.0 International (CC BY 4.0). Authors who submit their papers for publication by Journal of Artificial Intelligence and Science Communication (JAISC) agree to have the CC BY 4.0 license applied to their work, and that anyone is allowed to reuse the article or part of it free of charge for any purpose, including commercial use. As long as the author and original source is properly cited, anyone may copy, redistribute, reuse and transform the content.

Authors

  • Dongxing Yu

    Institute for Sustainable Development in Education, School of Education, Shanghai Sanda University, Shanghai 201209, China

Received: 30 August 2025; Revised: 16 November 2025; Accepted: 2 December 2025; Published: 4 January 2026

This article examines how generative artificial intelligence can be integrated into teaching without reducing learning to automated answer production. Rather than treating AI adoption as a purely technical question, the paper argues that the central pedagogical challenge is how to preserve human judgment, metacognitive effort, and disciplinary understanding when students can now generate plausible outputs in seconds. Building on the original manuscript's distinction between automation and augmentation, the revised version strengthens the argument by anchoring the problem in concrete classroom pain points, especially writing-intensive and feedback-heavy courses where students' use of AI often outpaces institutional policy. It synthesizes recent scholarship on hybrid intelligence, self-regulated learning, teacher-AI collaboration, assessment redesign, and academic integrity, while also identifying limitations in current frameworks, including weak subject-specific guidance, limited long-term evidence, and insufficient attention to equity and cultural context. In response, the paper clarifies the rationale for the COLLABORATE framework and explains how its ten principles work together to make AI use visible, bounded, and pedagogically productive. The framework is presented as a conceptual, evidence-informed design model rather than as an empirically validated intervention. The paper concludes by outlining practical implementation pathways, ethical safeguards, and a concrete research agenda for testing which forms of human-AI collaboration best support student learning, process transparency, and foundational skill retention.

Keywords:

Human-AI Collaboration Generative AI Pedagogical Strategies AI Literacy Metacognition Teacher-AI Partnership Educational Technology

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