Keywords
Learner engagement
Self-Determination Theory
Cognitive dissonance reduction
Educational technology
How to Cite
Abstract
Purpose: This article proposes a psychology-informed conceptual architecture for large language model (LLM)-supported learner interaction. The purpose is to move educational LLMs beyond primarily transactional responses toward relational and transformational engagement that supports motivation, persistence, reflection, and conceptual growth.
Research methods: The article uses conceptual analysis and theory integration. It synthesizes research on LLM use in education, Self-Determination Theory, empathy, cognitive dissonance reduction, intelligent tutoring systems, learner profiling, and adaptive feedback to develop a three-layer learner-engagement model.
Findings: The proposed architecture includes three interacting layers: a Psychological Processes Layer, a Recognition Layer, and a Response Generation Layer. These layers are supported by a learner profile module that preserves continuity across interactions. Together, the model enables LLMs to recognize learner goals, preferences, emotions, and tensions; select motivational and empathic strategies; and generate adaptive, scaffolded, and reflective responses.
Discussion: The framework suggests that psychologically informed LLMs can function as more authentic tutors or coaches by supporting autonomy, competence, relatedness, emotional safety, and productive cognitive conflict. The model has implications for K–12 education, higher education, workplace learning, AI literacy, responsible implementation, and future empirical research on learner-facing AI systems.
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