Performance, interaction, and ethical evaluation in the use of generative artificial intelligence in engineering education

Generative artificial intelligence is increasingly incorporated into engineering education, particularly in programming courses, raising questions about its effects on learning processes and its acceptability in academic contexts. Empirical evidence examining both interaction patterns and ethical evaluation of Generative artificial intelligence use in real classroom settings remains limited, and studies addressing these dimensions jointly are scarce. This study adopts a multi-phase empirical design to examine student engagement with Generative artificial intelligence across these two complementary dimensions. In Phase 1, a classroom-based activity was conducted with 340 first-year engineering students who completed a time-constrained debugging task using Generative artificial intelligence as the sole external assistance tool. The results indicate that access to Generative artificial intelligence did not ensure reported successful task completion. Instead, successful outcomes were associated with prompt efficiency and verification practices, whereas higher prompting frequency was negatively associated with task success under time constraints. In Phase 2, a qualitative exploratory study was conducted with 16 engineering students through a structured role-play activity simulating an ethics committee, followed by individual voting and survey-based data collection. Acceptance was higher when Generative artificial intelligence was framed as supportive or formative, and lower in scenarios involving summative assessment, surveillance, or autonomous decision making. These preliminary findings suggest that Generative artificial intelligence use in engineering education requires attention not only to tool access or technical performance, but also to students’ interaction strategies, verification practices, and ethical evaluation of context-specific uses. From a socio-technical perspective, the study advances engineering education research by linking technical interaction with GenAI to verification, self-regulation, and ethical responsibility. This highlights the need for pedagogical approaches that integrate technical guidance and ethical reflection into engineering curricula.