Students’ little helper: Investigating continuous-use determinants of generative AI and ethical judgment

Students’ little helper: Investigating continuous-use determinants of generative AI and ethical judgment

연구 분야: Software Development

학회: Education and Information Technologies

Generative artificial intelligence (genAI) tools have achieved unprecedented adoption rates in recent years. However, their use in higher education is highly controversial. While advocates point to the tools’ various benefits, most notably productivity gains, critics argue that genAI promotes poor academic work and often consider its use to be unethical. Research has provided valuable insights into the factors behind students’ initial genAI adoption, but empirical evidence on what drives continuous-use intent is Limited. We draw on the Unified Theory of Acceptance and Use of Technology and reflect voiced ethical concerns regarding genAI use in higher education by incorporating ethical judgment into the model. Original data from 117 student ChatGPT users demonstrate that performance expectancy is the strongest driver of continuous-use intent, followed by effort expectancy and ethical judgment. Thus, the perceived ethicality of genAI use in higher education represents an additional relevant predictor of further use. Contrary to theoretical expectations, the data do not support a significant effect of social influence on the continuous use of genAI tools. This study contributes to the emerging literature on genAI adoption and use in higher education by introducing the novel focus on continuous-use determinants. Further, it demonstrates ethical judgement’s relevance to technology adoption in controversial contexts. Moreover, it offers several practical implications regarding how higher education institutions can cope with genAI.