AI in the Classroom: A Virtual Summit - 2025
Zoom Recording
Description
The session is grounded in two guiding frameworks. The ADDIE instructional design model (Analysis, Design, Development, Implementation, Evaluation) structures how AI can be systematically integrated into teaching practice, from identifying learning needs to evaluating outcomes. The RISEN framework (Role, Input, Steps, Evaluation, Next) provides faculty with a structured method for developing effective AI prompts and maintaining academic rigor. Together, these frameworks highlight how AI can be embedded purposefully rather than opportunistically.
Participants will leave with strategies for incorporating AI into their own courses, including techniques for building equitable group work, creating adaptable case studies, and designing assignments that encourage students to critically evaluate AI as a partner in knowledge generation. By the end of the session, attendees will be equipped with replicable models to responsibly and creatively integrate AI into teaching and learning.
Keywords
AI, nursing education
Disciplines
Higher Education | Nursing
Language
English
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Teaching, Building, Learning: AI in Nursing Education
The session is grounded in two guiding frameworks. The ADDIE instructional design model (Analysis, Design, Development, Implementation, Evaluation) structures how AI can be systematically integrated into teaching practice, from identifying learning needs to evaluating outcomes. The RISEN framework (Role, Input, Steps, Evaluation, Next) provides faculty with a structured method for developing effective AI prompts and maintaining academic rigor. Together, these frameworks highlight how AI can be embedded purposefully rather than opportunistically.
Participants will leave with strategies for incorporating AI into their own courses, including techniques for building equitable group work, creating adaptable case studies, and designing assignments that encourage students to critically evaluate AI as a partner in knowledge generation. By the end of the session, attendees will be equipped with replicable models to responsibly and creatively integrate AI into teaching and learning.
