Implementing artificial intelligence-generated podcasts in undergraduate courses

Background Delivering expansive nursing curricula requires innovative supplemental tools that accommodate diverse learning styles without significantly increasing faculty burden. Generative AI (GenAI) presents a low-barrier solution for producing audio content, yet practical frameworks for clinical implementation remain scarce. Objective This project presents a systematic roadmap for integrating AI-generated podcasts into undergraduate nursing curricula and evaluates the feasibility and student reception of this workflow. Methods The implementation followed a three-phase framework: (1) Preproduction, aligning 24 episodes with NCLEX-RN blueprints; (2) generation and Validation, using NotebookLM for dual-speaker dialogue followed by mandatory faculty verification for clinical accuracy; and (3) evaluation, via Learning Management System analytics and student surveys ( n = 24). Results Implementation analytics showed download rates up to 38%, with 66.7% of students downloading nearly 100% of episodes. Students reported high trust (83.3%) in the AI-generated content. The roadmap successfully transitioned students from passive listeners to active users, with 37.5% expressing interest in adopting AI tools for self-regulated study. Conclusion This structured roadmap demonstrates that faculty-validated AI podcasts are a trustworthy, scalable modality. Successful integration requires early curricular alignment and “human-in-the-loop” validation to ensure clinical safety and student engagement.