2026 ASEE Annual Conference & Exposition

Experiential learning, spanning project-based courses, laboratory experiences, undergraduate research, internships for academic credit, and industry-sponsored design, has long served as a primary mechanism through which engineering students develop professional competencies aligned with accreditation outcomes and workforce expectations [1] – [3]. The integration of generative artificial intelligence (AI) into engineering practice is fundamentally reshaping these environments, challenging traditional assumptions about “hands-on” learning, authorship, and competency assessment. Rather than functioning solely as a productivity tool, AI increasingly acts as a cognitive collaborator that redistributes human effort and transforms how students engage in engineering problem-solving and design [8], [12].

This paper examines how AI reconfigures experiential learning across a coordinated ecosystem of academic and workplace-integrated environments within an undergraduate electrical and computer engineering program. Drawing on multi-source qualitative evidence from project-based courses, laboratory instruction, undergraduate research, internships for academic credit, and capstone design, the study analyzes how AI shifts student effort from execution toward higher-order activities such as engineering judgment, validation, and communication. Across contexts, a majority of student teams, approximately seventy to eighty-five percent, reported using AI tools, with roughly half encountering AI-related issues requiring substantial debugging and verification.

Experiential learning, spanning project-based courses, laboratory experiences, undergraduate research, internships for academic credit, and industry-sponsored design, has long served as a primary mechanism through which engineering students develop professional competencies aligned with accreditation outcomes and workforce expectations [1] – [3]. The integration of generative artificial intelligence (AI) into engineering practice is fundamentally reshaping these environments, challenging traditional assumptions about “hands-on” learning, authorship, and competency assessment. Rather than functioning solely as a productivity tool, AI increasingly acts as a cognitive collaborator that redistributes human effort and transforms how students engage in engineering problem-solving and design [8], [12].
This paper examines how AI reconfigures experiential learning across a coordinated ecosystem of academic and workplace-integrated environments within an undergraduate electrical and computer engineering program. Drawing on multi-source qualitative evidence from project-based courses, laboratory instruction, undergraduate research, internships for academic credit, and capstone design, the study analyzes how AI shifts student effort from execution toward higher-order activities such as engineering judgment, validation, and communication. Across contexts, a majority of student teams, approximately seventy to eighty-five percent, reported using AI tools, with roughly half encountering AI-related issues requiring substantial debugging and verification.

The paper introduces an AI-Augmented Experiential Learning Ecosystem model that conceptualizes how cognitive responsibility is dynamically distributed between human learners and AI systems across both phases of engineering work and learning contexts. Across learning contexts, the dominant site of learning shifts from the production of solutions to the validation and interpretation of AI-generated outputs. While AI accelerates prototyping and lowers barriers to entry, it simultaneously introduces new forms of epistemic uncertainty that require explicit emphasis on validation, transparency, and accountability. This paper contributes a theoretically grounded and empirically informed framework for aligning experiential learning with the realities of AI-mediated engineering practice.