Integrating technology acceptance, self-determination, and self-regulation: A structural model of generative AI-supported learning and competence

Abstract

This study proposes an integrated framework that synthesizes Technology Acceptance, Self-Determination Theory (SDT), and Self-Regulated Learning (SRL) to explain how learners engage with and learn from AI tools. SRL, which involves proactive planning, monitoring, evaluation, and the regulation of cognition and motivation, is particularly crucial in AI contexts, where learners must manage their learning processes and regulate interactions with intelligent systems that influence cognitive load and task structure. Students using AI need to evaluate AI-generated responses, revise prompts, compare alternative outputs, and integrate AI suggestions with their own reasoning. These tasks represent a sophisticated form of SRL—AI-augmented regulation—where learners coordinate internal metacognition with external AI scaffolding. This study distinguishes between SRL as a macro-level regulatory capacity and Metacognitive Strategy Use (MSU) as a micro-level metacognitive enactment. SRL encompasses broad processes, such as goal setting, planning, and monitoring, while MSU refers to specific, real-time strategies during task execution, such as checking accuracy and revising prompts. By framing SRL and MSU in this way, the study clarifies how broader regulatory capacities enable specific metacognitive actions, facilitating deep learning and task engagement in AI-mediated contexts. This framework offers a developmental account of AI-supported learning that extends beyond simple acceptance to explain the processes by which learners sustain, regulate, and deepen their interaction with AI tools.