Harnessing generative AI: Exploring its impact on cognitive engagement, emotional engagement, learning retention, reward sensitivity, and motivation through reinforcement theory

With the rapid integration of artificial intelligence into educational environments, understanding its influence on student learning and engagement has become increasingly critical. This study examines the impact of generative AI on students’ cognitive engagement, emotional engagement, learning retention, reward sensitivity, and motivational engagement through the lens of Reinforcement Theory. A total of 487 undergraduate students from seven universities in North China participated in the study. The study employed SPSS and AMOS for descriptive statistics, correlation analysis, regression analysis, and structural equation modeling (SEM) to explore the complex relationships between generative AI and student outcomes. The findings revealed a significant positive relationship between generative AI and all measured student outcomes. Specifically, 62 % of the variance in cognitive engagement, 66 % in emotional engagement, 46 % in learning retention, 50 % in reward sensitivity, and 72 % in motivation were attributed to the influence of AI-driven learning tools. These results highlight the role of AI in enhancing cognitive processing, fostering emotional connections with learning materials, improving retention through personalized learning, and strengthening reward-based motivation. The study further confirms that students’ engagement with generative AI is a strong predictor of their learning outcomes. Frequent interaction with AI-generated content enhances cognitive comprehension, emotional investment, and adaptive learning behaviors, reinforcing intrinsic and extrinsic motivation. The findings underscore the transformative potential of AI-assisted learning environments in higher education, offering valuable implications for educators, policymakers, and technology developers seeking to optimize AI integration in academic settings.