2026 ASEE Annual Conference & Exposition

Capstone projects represent the culminating experience for both Engineering (EAC) and Engineering Technology (ETAC) students, requiring them to synthesize design knowledge, technical analysis, and professional standards. Capstone project design courses offer a critical opportunity to help students develop technical competence and acumen. These projects reinforce ABET criteria; part of the ABET Criteria 5d. for Accrediting Engineering Technology Programs (2025 – 2026) for Baccalaureate degree states that “The discipline specific content of the curriculum must focus on the applied aspects of science and engineering and must include design considerations appropriate to the discipline and degree level such as: industry and engineering standards and codes". Students, however, often struggle to locate and interpret relevant standards such as IEEE, NEC, IEC, and UL codes. The current challenge for a successful capstone project is to ensure students meet standards-based design expectations while fostering critical AI literacy.

Artificial intelligence (AI) tools increasingly influence engineering education. A 2025 Inside Higher Ed survey found that 85% of college students now use AI for coursework. AI tools like ChatGPT, GitHub Copilot, MATLAB with AI Toolbox, and Elicit can generate design insights and summarize standards. However, their reliability remains uncertain. Faculty, therefore, have a responsibility to teach students how to use these tools effectively, ethically, and with critical awareness.

This paper presents a pedagogical framework for integrating AI tools into senior capstone education through a structured sequence of four assignments. Developed for an Electrical Engineering and Renewable Energy capstone course at XYZ University, this new approach guides students to engage with AI not just as a design aid but as a subject of analysis, evaluating how AI contributes meaningfully to engineering standard design processes. The assignments emphasize identifying and applying industry standards, engineering codes, and ethical decision-making. Each assignment contrasts traditional research and reasoning with AI-assisted approaches. It also prompts students to reflect on the value, risks, and credibility of AI-generated outputs. By coupling technical rigor with critical reflection, the framework prepares students to navigate a profession increasingly shaped by AI while maintaining compliance with ABET EAC and ETAC outcomes.

This framework is adaptable to multiple engineering disciplines. It provides a pathway for programs seeking to incorporate responsible AI practices into accreditation-aligned outcomes, such as ABET criteria related to design under constraints with ethical responsibility.