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Instructional Emotion in the Age of Artificial Intelligence：Value, Alienation Risks, and Pathways for Reconstruction

Year: 2026
Type: article
Abstract: The growing use of artificial intelligence in education raises questions about how emotional experience, relational care, and professional responsibility can be sustained in increasingly data-mediated teaching environments. This conceptual article examines instructional emotion as an interrelated configuration of teachers’ emotional engagement, students’ learning-related experiences, and the affective relationships through which teaching acquires meaning. Drawing on scholarship on achievement emotions, caring pedagogy, teacher–student relationships, and artificial intelligence in education, the article organizes its analysis around three value foundations: embodied human experience, relational connection, and teachers’ professional responsibility. It then identifies four potential forms of alienation arising when technological efficiency overrides educational judgment: procedural rigidity in teaching methods, emotional distance in interaction, the loss of affective meaning in content, and the reduction of assessment to measurable performance. Corresponding reconstruction pathways include emotionally adaptive instructional design, humanistic interaction, affective knowledge graphs, and emotionally responsive assessment. These pathways are guided by teacher judgment and bounded by consent, fairness, transparency, and human oversight. The framework distinguishes the interpretation of emotional evidence from claims to measure learners’ inner states directly. Its contribution is to connect educational values, potential risks, and pedagogical responses within a coherent conceptual structure. The proposed relationships remain open to empirical examination across educational settings.
DOI: 10.30560/ier.v9n5p90
Author: Guan Xin
Language: English
Open Access status: diamond
OpenAlex ID: W7224788024
FWCI:
Cites: 15
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Related to: 10