Impact of Perceived Risks on Resistance to Generative AI in Engineering Education

The integration of generative artificial intelligence in higher education has created a paradoxical scenario: while these tools offer evident learning benefits, many students choose not to use them. This study investigates whether perceived risks associated with the use of generative AI—such as the loss of critical thinking skills, technological dependency, and distrust in AI outputs—act as significant barriers to adoption in engineering academic contexts. Through a survey of 258 engineering students from Pontificia Universidad Católica del Perú, an ordinal logistic regression model was implemented to analyze the relationship between perceived risks and self-reported levels of generative AI use. The theoretical framework integrates the Unified Theory of Acceptance and Use of Technology (UTAUT) with the Technology Threat Avoidance Theory (TTAT), providing a comprehensive perspective that explains both motivating and inhibiting factors of technology adoption. Results reveal that higher levels of perceived risk significantly predict lower usage of generative AI, with this effect being more pronounced among students with lower academic performance. Contrary to expectations, high-performing students tend to use these tools less, possibly due to greater ethical awareness or academic caution. These findings suggest that non-adoption of technology may represent a rational protective strategy against perceived threats, rather than simple resistance to change. The implications for engineering education are substantial: institutions must develop clear policies that address legitimate student concerns, implement digital literacy programs that promote the ethical and responsible use of AI, and design differentiated strategies that consider diverse student academic profiles. Furthermore, addressing the adoption gap between high and low-performing students is crucial to ensure equitable access to AI-enhanced learning opportunities.