Linking skills and Learning Evidence for Engineering Education in the Age of Artificial Intelligence
DOI:
https://doi.org/10.26507/rei.v21n42.1446Keywords:
engineering education, generative artificial intelligence, learning evidence, competencies, assessment, augmented modellingAbstract
Generative artificial intelligence has changed the conditions under which academic work is produced and assessed in higher education. In engineering education, where problem solving has traditionally operated as evidence of understanding, this transformation requires a reconsideration of which competencies should be observed and how they should be assessed. This theoretical-methodological article proposes a competency framework for learning evidence in engineering education in the age of artificial intelligence. Based on the notion of augmented modelling, six assessable dimensions are identified: problem formulation, explicit assumptions, representation and modelling, critical technological mediation, validation of results, and professional judgment. The paper presents a general rubric, assessment design strategies, and transferable examples. It concludes that academic rigor does not disappear with automation; rather, it shifts toward justification, validation, and epistemological responsibility.
Author Biography
Hugo Roger Paz, Universidad Nacional de Tucumán
Doctor en Educación de la Facultad de Filosofía y Letras de la Universidad Nacional de Tucumán (2018-03-01 a 2033-10-11). Magíster en Ingeniería Hidráulica de la Universidad de Cantabria (1992-10-13 a 1993-09-27) e Ingeniero Civil de la Universidad Nacional de Tucumán (1984-03-01 a 1990-12-19). Investigador sénior y científico de datos con formación en ingeniería civil e hidráulica y más de 40 años de experiencia en educación superior.
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