Bridging the AI skills gap: A blueprint for future-proofing the workforce by including industry advisory councils for undergraduate environmental science program course redesign

Abstract

This study explores the integration of Artificial Intelligence (AI) skills into an undergraduate introductory environmental science course designed for non-traditional adult learners. Using the course redesign process Elicit, Design, Create, Deploy, and Research (EDCDR), instructional designers aligned AI competencies with Course Student Learning Outcomes (CSLOs) and career skills, embedding them into discussions and summative assessments. Using an explanatory mixed-methods approach, researchers evaluated student performance post-redesign to assess proficiency in AI, CSLOs, and career skills. A one-sample Wilcoxon signed-rank test revealed significant proficiency gains in CSLOs and career skills (p < 0.001), as well as in most AI skills, except “gathering background knowledge” (p < 0.066). Qualitative data from weekly overviews, discussions, and course evaluations highlighted student perceptions of AI tools, motivation, and challenges. Cognitive Load Theory (CLT) provided a framework for understanding barriers to skill acquisition. Findings informed course adjustments and demonstrated that aligning AI skills with course outcomes offers a scalable model for enhancing student readiness for AI-integrated careers in environmental science.