Think first, code later: enhancing student reasoning and learning in practical engineering education through guided generative AI

The use of Artificial Intelligence (AI) in education has triggered discussions ranging from redesigning courses around AI to restricting its use entirely. However, before fundamentally restructuring curricula, it is essential to establish how students can be trained to use AI effectively within existing courses to strengthen reasoning and critical thinking skills. This is particularly important in practical engineering education, where simulation plays a central role in enabling students to connect theoretical models with observable system behavior through experimentation and analysis. In such courses, generative AI offers new opportunities for personalized, dialogue-based support on demand, but its value depends on how it is pedagogically integrated. Our primary
objective is to shift students from answer-seeking code generation toward a reasoning-first learning approach. To this end, this paper evaluates a Think-First workflow that restructures simulation exercises to emphasize problem formulation, result prediction, and pseudocode development prior to implementation. We examine students’ AI interaction patterns, simulation reasoning, and perceptions of the workflow in a master’s course on Simulation of Communication Networks.

Content

Subjects

Engineering Education

Generative AI

Student Autonomy

Communication Network Simulation

Simulation-Based Learning

Conceptual Reasoning

AI-Assisted Learning

DDC Class

621.38: Electronics, Communications Engineering

006.3: Artificial Intelligence