Behavioral and ethical predictors of continuous intention to use generative AI responsibly in higher education
TL;DR
The results showed that authenticity, originality, responsibility, and confidentiality significantly predicted attitudes (ATs) toward GenAI, while ATs, perceived behavioral control, and subjective norms significantly predicted continuous intention.
Abstract
This study examined factors that support the sustainable and responsible integration of generative artificial intelligence (GenAI) into higher education. The research model was based on the theory of planned behavior (TPB) and was extended to include ethical considerations, such as authenticity, originality, responsibility, and confidentiality. In the qualitative phase, structured interviews with faculty members across various departments examined the potential benefits, limitations, and ethical concerns of GenAI use in higher education. The qualitative findings informed the development of the theoretical framework and the research model. In the quantitative phase, PLS-SEM was used to test the model with data from 1,261 GenAI users. The results showed that authenticity, originality, responsibility, and confidentiality significantly predicted attitudes (ATs) toward GenAI, while ATs, perceived behavioral control, and subjective norms significantly predicted continuous intention. The findings contribute by testing an ethically grounded extension of the TPB for the responsible integration of GenAI in higher education. They also emphasize the need for clear behavioral rules and ethical guidelines, developed with relevant stakeholders, to support sustainable and responsible use of GenAI.