2026 ASEE Annual Conference & Exposition

This study critically investigates the evolving effectiveness of teaching programming languages within contemporary engineering education settings when students leverage advanced Artificial Intelligence (AI) tools. The increasing integration of AI in student coursework presents complex challenges, notably concerning academic integrity, the authenticity of submitted work, and maintaining instructional control over the learning process. Therefore, this research urgently highlights the necessity for a comprehensive re-evaluation of established teaching methodologies and traditional assessment criteria in programming education.
Employing a robust mixed-methods approach, our investigation draws upon insights from a thorough literature review, student surveys exploring perceptions and usage patterns, and in-depth analyses of student performances, comparing outcomes both with and without AI tool usage. The investigation revealed that students utilizing AI tools demonstrated significantly better outcomes, achieving improved efficiency and notably shorter coursework completion times, primarily by overcoming challenges related to syntax errors, debugging, and general inefficiency.
However, this sharp performance disparity raises profound concerns about genuine skill development and academic honesty, as students themselves expressed apprehension about over-reliance on AI, fearing a potential reduction in their critical thinking and independent problem-solving abilities. The data also revealed a widespread ambiguity among the student population regarding how AI-generated code intersects with existing definitions of plagiarism, underscoring the urgent need for clear ethical guidance.
The findings collectively argue for a fundamental shift in current teaching methodologies, moving beyond simple prohibition to embrace a model of AI literacy and ethical collaboration. We propose that educators integrate AI literacy into the curriculum and redesign assessments toward formats that are either AI-resistant—such as live coding sessions—or AI-integrated, requiring critical review and justification of AI-generated outputs. The ultimate goal is to enhance academic outcomes and foster genuine skill development by ensuring that students are prepared to understand, leverage, and ethically navigate the complexities of an increasingly AI-driven professional landscape.