2026 ASEE Annual Conference & Exposition

The rapid advancement of artificial intelligence technologies is fundamentally transforming engineering education, creating both opportunities and challenges for faculty and students alike (Chen et al., 2023; Kasneci et al., 2023). This paper presents a systematic pedagogical framework for integrating AI tools into technical engineering courses, designed to prepare students for the evolving professional landscape while maintaining academic integrity and critical thinking skills.
The study implements a structured approach across six engineering and information technology courses, utilizing weekly graded discussion assignments that progressively develop students' AI literacy and application skills. The pedagogical framework addresses three core learning objectives: (1) developing practical AI skills for solving discipline-specific engineering problems, (2) fostering autonomous learning capabilities through guided AI-assisted exploration of complex topics, and (3) establishing ethical frameworks for appropriate AI usage in professional engineering practice.
The intervention employs a scaffolded learning model where students engage with carefully designed AI prompts, document and analyze AI-generated responses, and critically evaluate the quality, accuracy, and applicability of AI outputs to engineering contexts. Throughout the semester, students assume increasing responsibility for prompt engineering and customization, transitioning from guided exercises to independent AI-assisted problem-solving activities. This progressive approach aligns with constructivist learning principles and promotes metacognitive awareness of AI capabilities and limitations (Zawacki-Richter et al., 2019).
Data collection includes longitudinal assessment of student engagement with AI tools, qualitative analysis of discussion responses, and comprehensive end-of-semester surveys measuring student perceptions of AI utility, confidence in AI application, and understanding of ethical considerations. Preliminary findings suggest significant improvements in students' ability to critically evaluate AI-generated content and apply AI tools effectively to engineering challenges.
This research contributes to the growing body of literature on AI integration in STEM education and provides a replicable framework for engineering educators seeking to prepare students for an AI-enhanced professional environment while maintaining rigorous academic standards.

References
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264-75278.

Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., ... & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274.

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education–where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1-27.