Prompt engineering competence, knowledge management, and technology fit as drivers of educational sustainability through generative AI

This study investigated the impact of prompt engineering competence, knowledge management, and task-individual-technology fit on the continued intention to use artificial intelligence (AI), as well as their implications for educational sustainability. Data from 437 undergraduate students who use AI tools for academic purposes were analyzed using PLS-SEM. The results indicated that prompt engineering competence significantly predicts knowledge acquisition and knowledge application, which, in turn, significantly predict both task-technology fit (TTF) and individual-technology fit (ITF). Furthermore, TTF and ITF were found to have significant impacts on the continuous intention, which, in turn, positively predicts educational sustainability through generative AI. The results of the multi-group analysis revealed that the hypotheses were supported in both the female and male samples and that the model maintained a consistent and robust structure across genders. Overall, the findings provide substantial evidence on how generative AI supports sustainable learning in education by developing prompt engineering skills, promoting effective knowledge management practices, and ensuring technology alignment.