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AI-Enabled Gamified Teaching Based on Learning Resonance Theory

Kaixuan Chen*

Abstract


The fundamental dilemma faced by traditional teaching lies in the mismatch between the way knowledge is presented and students'
cognitive needs. This study constructs the "Learning Resonance Theory, " proposing that learning effectiveness is a function of knowledge energy, student energy, and the resonance coefficient, and uses this theoretical framework to guide AI-enabled gamified teaching practice. An action research study conducted in a 10-session "Mathematical Thinking" course demonstrates that AI, by assuming the roles of game designer,
impartial referee, and real-time commentator, significantly enhances the resonance coefficient between knowledge presentation and student
cognition, promoting conceptual change and deep learning while substantially reducing the design costs of gamified teaching, making it feasible for routine classroom implementation rather than remaining at the level of "demonstration lessons." This study provides a new theoretical
perspective for understanding the mechanisms of teaching effectiveness and offers an actionable practical paradigm for instructional design in
the AI era.

Keywords


Learning resonance theory; Artificial intelligence; Gamified teaching; Conceptual change; Teaching methods

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References


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DOI: http://dx.doi.org/10.70711/aitr.v4i3.9932

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