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Explaining Competence Development in AI-Enabled Learning Environments: A Stimulus-Organism-Response Model for Higher Education

Author(s)

Suxia Chen*, Di Zuo

Affiliation(s)

School of Business, Shanghai Normal University Tianhua College, Shanghai, China *Corresponding Author

Abstract

Artificial intelligence (AI) is rapidly reshaping higher education, yet existing research remains more effective at describing technological functions and structural changes than at explaining how AI-enabled learning environments influence learner development. To address this gap, this conceptual study develops a theoretical framework based on the Stimulus-Organism-Response (S-O-R) model. Within this framework, AI-enabled learning environments are conceptualized as contextual stimuli, learners' behavioral and psychological engagement as organism variables, and competence development as the response. Drawing on research in AI-enabled learning, interdisciplinary education, learning analytics, and generative AI, the framework identifies three dimensions of contextual stimuli—physical context, social interaction, and intelligent systems—and argues that these conditions shape competence development through the reciprocal dynamics of behavioral and psychological engagement. The study addresses this gap by offering a mechanism-oriented explanation of AI-enabled learning, integrating process-optimization and structural-transformation perspectives, and reconceptualizing learning outcomes from narrow performance indicators to broader competence development. It further argues that the educational effects of AI are conditional rather than uniformly beneficial. By clarifying the pathway from AI-enabled learning environments to competence development, the study provides a theoretical foundation for future empirical research in higher education.

Keywords

Artificial Intelligence; AI-Enabled Learning Environments; Higher Education; Competence Development; Stimulus-Organism-Response; Learner Engagement

References

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