Models of AI integration in engineering education: A practical framework for learning outcomes, retention, and academic integrity

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Abstract
This paper proposes a practical, program-level framework for integrating generative AI into engineering education while safeguarding academic integrity and improving learning outcomes and retention. Drawing on recent evidence from engineering and STEM education, the framework aligns curriculum outcomes with ABET expectations, embeds AI-supported active learning (e.g., retrieval practice, automated feedback), and redesigns assessment to prioritize process evidence and oral/interactive defenses. We describe governance mechanisms, recommended tool chains, and analytics for continuous improvement. A comparative table and implementation roadmap illustrate how departments can sequence adoption across courses and years of study. The approach is intended for resource-constrained programs seeking measurable, ethically grounded gains in performance, persistence, and fairness.

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