Understanding Learner Intentions in AI-Enhanced Education: The Impact of Motivation and System Features

Main Article Content

Thuy Dung Pham Thi
Nam Tien Duong

Abstract

AI-based learning systems have transformed education by offering personalized, flexible, and interactive learning experiences. However, understanding the behavioral tendencies of learners in such environments requires an integrated theoretical approach. This study applies the theory of planned behavior (TPB), the technology acceptance model (TAM), and motivation theories to examine how system characteristics—including perceived ease of use (PEOU), feedback capability, and self-control capability—influence learners' intrinsic and extrinsic motivations, which subsequently shape their behavioral intentions to engage with AI-based learning. To test the proposed model, data were collected from 242 college students who had participated in AI-based learning. Using SEM, the findings reveal that intrinsic and extrinsic motivations significantly influence learners' behavioral intentions, with intrinsic motivation having a stronger impact. PEOU is identified as a critical factor, positively affecting both intrinsic and extrinsic motivation. Additionally, feedback capability enhances intrinsic motivation more than extrinsic motivation, underscoring the importance of adaptive learning support. In contrast, self-control capability negatively affects intrinsic motivation, likely due to students' limited experience with the system and initial challenges in navigating AI-driven learning environments. By integrating psychological (TPB, motivation theories) and system-related (TAM) factors, this study advances the theoretical understanding of AI-based learning adoption and bridges the gap between system usability and learner engagement. The findings offer practical insights for educators, developers, and policymakers to design AI-based learning platforms that enhance motivation, user experience, and long-term participation.

Article Details

Section

Digital Learning and E-Learning

References

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