Abstract:

In 2026, artificial intelligence (AI) is reshaping teaching, learning, and assessment processes in higher education, particularly in technical programs where systems thinking and modeling are core competencies. In the course Environmental Integration in of Projects of the Interuniversity Master’s Degree in Project Management at the Universidad de La Rioja, system dynamics modeling using Vensim plays a central role in the development of student projects focused on the environmental assessment of complex engineering systems. However, the conceptual and technical complexity of system modeling often represents a significant learning barrier.

This contribution presents an AI supported pedagogical framework designed to assist students at three levels:
(i) conceptual structuring of environmental problems through guided identification of variables, causal relationships, and system boundaries;
(ii) scaffolded development of causal loop diagrams and stock-and-flow equations compatible with Vensim; and
(iii) automated formative feedback on model coherence, logical consistency, and technical quality. Rather than replacing analytical reasoning, AI functions as a support that promotes critical reflection and iterative model refinement.

From an educational perspective, this integration enhances systemic understanding, fosters self-regulated learning, and reduces time spent on formal errors, enabling students to focus on interpretation of results and sustainability-oriented decision-making. It also strengthens emerging competencies in applied AI within engineering and project management contexts.

The pedagogical redesign incorporates authentic assessment strategies—such as oral defense of the model and explicit justification of AI use—to ensure academic integrity, ethical awareness, and genuine competency development. The experience demonstrates that thoughtfully integrated AI can significantly enhance deep learning in environmental systems modeling and provides a transferable framework for advanced engineering education.

Keywords:

Artificial Intelligence in Education, System Dynamics Modeling, Environmental Systems Analysis.