Artificial Intelligence (AI) is emerging as a transformative tool in healthcare education, offering innovative approaches to enhance clinical decision-making and learner engagement. This study explores the development and integration of “BOTs,” AI-driven educational models designed to support neonatal advanced practice education. By simulating neonatal scenarios, BOTs provide interactive that reinforce evidence-based practice and critical thinking. The presentation examines the pedagogical framework, technological design, and potential implications for competency-based education in neonatal care. Findings suggest that AI-enabled tools can augment traditional teaching methods, improve learner confidence, and foster a more personalized educational environment for advanced practice providers.
As generative AI becomes embedded in students’ everyday workflows, instructors are challenged to decide not whether AI belongs in the classroom, but how it should be positioned within learning itself. This session presents an instructional design approach that integrates a TA-like AI assistant directly alongside course materials to harness the benefits of AI while mitigating common concerns around misuse and shallow engagement.Rather than treating AI as an external tool students access independently, the model places AI support within readings, practice activities, and capstone preparation. This design leverages AI’s strengths for clarification, iteration, and reflection, while encouraging students to engage with a trusted, course-aligned assistant that reinforces instructional intent. The result is not simply AI adoption, but a restructuring of where and how AI supports learning.The presentation focuses on design decisions instructors can apply across disciplines and modalities to increase learning touchpoints, reinforce repetition, and guide productive AI use without banning tools or relying on enforcement. Participants will leave with practical ideas for integrating AI in ways that are both pedagogically intentional and adaptable to their own teaching contexts.
As generative artificial intelligence (GenAI) tools become increasingly accessible, teacher preparation programs face urgent questions about how preservice teachers are actually using these tools and what they perceive as appropriate, ethical, and useful practice. This session presents findings from a mixed-methods study examining preservice teachers’ use and perceptions of GenAI within special education coursework at a public university. Using the Technology Acceptance Model as a guiding framework, the study explored how teacher candidates engaged with GenAI across different assignment types, their perceived usefulness and ease of use, and the alignment (or misalignment) between use and trust.Results indicate that while most candidates used GenAI for brainstorming and editing, they expressed uncertainty about ethical boundaries, instructional reliability, and GenAI’s appropriateness for supporting students with disabilities. Participants reported a clear need for explicit guidance, ethical instruction, and modeling of responsible classroom integration.This session will share key findings and translate them into actionable implications for teacher educators, focusing on assignment design, policy clarity, and instructional supports that promote responsible, equity-centered GenAI use in teacher preparation programs.
