Evaluating the influence of generative AI on students’ academic performance through the lenses of TPB and TTF using a hybrid SEM-ANN approach

The rapid rise of Generative AI in education has brought transformative potential. However, there is limited empirical insight into the factors influencing students’ use of these tools and their impact on academic performance. Specifically, research has not thoroughly examined how task-technology fit and behavioral factors shape Generative AI usage. This study addresses these gaps by integrating the Task-Technology Fit (TTF) and the Theory of Planned Behavior (TPB) to develop a theoretical research model. Data were collected from university students through a structured survey, and the model was validated using a hybrid Structural Equation Modeling-Artificial Neural Network (SEM-ANN) approach. The results demonstrate that both task and technology characteristics significantly impact task-technology fit, positively influencing the use of Generative AI tools. Additionally, behavioral factors such as attitudes, subjective norms, and perceived behavioral control were found to strongly encourage Generative AI usage. Notably, the study confirms that these AI tools positively contribute to students’ academic performance. At the same time, the study recognizes the ethical dilemmas tied to Generative AI, highlighting issues such as academic integrity, excessive dependence, and its potential effects on critical thinking. The findings offer valuable insights for various stakeholders and provide practical guidance for strategically integrating AI tools to enhance student outcomes.