Preparing AI-Ready Engineering Talent

Abstract

Generative artificial intelligence (GenAI) is transforming engineering education and work, creating new expectations for graduate capabilities. However, its integration presents questions about academic integrity, student learning, ethical responsibility, and preparation for AI-augmented work. This basic qualitative study investigated how AI experts perceive the opportunities and risks of GenAI for developing engineering students’ knowledge and skills and the curricular conditions needed for workforce readiness. Semi-structured interviews were conducted with ten faculty members in an AI-focused engineering department at a U.S. university. Participants had teaching, research, or design experience in AI or an AI-related field. Data were analyzed through inductive thematic analysis, with attention to contrasting and negative cases. Ansbacher and Ansbacher’s Individual Psychology framed the examination of faculty perceptions, while the coding structure and themes were developed inductively.   Four themes describe GenAI as an inevitable but questioned learning tool, a source of tension between performance and learning, a catalyst for rebalancing engineering curricula, and an uncertain influence on workforce readiness. Participants identified productivity and learning opportunities, but disagreed about when GenAI should be introduced and whether traditional programming requirements should change. Their accounts converged on conditional integration: GenAI use should depend on learners’ developmental readiness, the task’s purpose, students’ ability to understand and verify outputs, ethical disclosure, and accountability for consequences. Within this focused setting, the findings suggest that AI-enabled talent development requires AI capability to be developed alongside foundational engineering competence, critical evaluation, communication, ethical responsibility, and professional judgment.