What question did this study set out to answer?

To examine how engineering students interact with generative AI during technical programming tasks and evaluate their ethical perceptions of AI use in academic settings.

August 16, 2026Education and Information TechnologiesOpen Access

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

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Authors

ALAscensión López-Vargas JRJavier Rodríguez‐Vidal ÁBÁngel García Beltrán

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Overview

Multi-phase empirical study reveals debugging success depends on prompt efficiency rather than frequency in engineering students, highlighting the need for structured AI literacy and ethics.

Key Points

To examine how engineering students interact with generative AI during technical programming tasks and evaluate their ethical perceptions of AI use in academic settings.
Assessed 340 first-year engineering students completing a time-constrained code debugging task using generative AI as the sole external resource (Phase 1).
Conducted a qualitative ethics committee role-play simulation and survey with 16 engineering students to assess acceptance across different educational contexts (Phase 2).
Access to generative AI did not ensure task success; completion was positively associated with prompt efficiency and verification practices, while higher prompt frequency was negatively associated with success under time constraints.
Student acceptance was higher when generative AI was utilized for supportive or formative learning, but significantly lower for summative assessment, surveillance, or autonomous decision-making.

Cite This Study

López-Vargas et al. (2026) studied this question.

synapsesocial.com/papers/6a817a4cf2fb91fc834adf4e https://doi.org/10.1007/s10639-026-14112-y

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August 16, 2026Education and Information TechnologiesOpen Access

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

View Full Paper

Ask AI

Bookmark

Authors

ALAscensión López-Vargas JRJavier Rodríguez‐Vidal ÁBÁngel García Beltrán

Discussion

Member takes

Overview

Multi-phase empirical study reveals debugging success depends on prompt efficiency rather than frequency in engineering students, highlighting the need for structured AI literacy and ethics.

Key Points

To examine how engineering students interact with generative AI during technical programming tasks and evaluate their ethical perceptions of AI use in academic settings.
Assessed 340 first-year engineering students completing a time-constrained code debugging task using generative AI as the sole external resource (Phase 1).
Conducted a qualitative ethics committee role-play simulation and survey with 16 engineering students to assess acceptance across different educational contexts (Phase 2).
Access to generative AI did not ensure task success; completion was positively associated with prompt efficiency and verification practices, while higher prompt frequency was negatively associated with success under time constraints.
Student acceptance was higher when generative AI was utilized for supportive or formative learning, but significantly lower for summative assessment, surveillance, or autonomous decision-making.

Cite This Study

López-Vargas et al. (2026) studied this question.

synapsesocial.com/papers/6a817a4cf2fb91fc834adf4e https://doi.org/10.1007/s10639-026-14112-y

View Full Paper

Ask AI

Bookmark