AI in the Classroom: A Virtual Summit - 2026
Teaching in the AI Era: Studying Faculty Course Redesign, Visible Thinking, and Sustainable Implementation
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Nevada State University
Description
How can faculty development move beyond generic AI guidance toward redesigns that make student thinking visible, clarify AI expectations, and remain feasible for teaching-intensive institutions? This presentation shares the design and emerging findings from Nevada State University’s AI & Accessibility Academy study, which follows faculty from a summer course redesign academy into fall implementation. The study examines changes in faculty self-efficacy around AI-era pedagogy and accessibility, the redesign practices faculty implement, how course artifacts reflect process-focused assessment and accessible design, and what factors support or constrain sustained implementation.
The session describes the study’s mixed-methods design, including faculty pre/post/follow-up surveys, artifact analysis, and a planned student survey on AI-expectation clarity and whether students perceive their thinking processes as valued, sharing preliminary patterns related to faculty confidence, beliefs about AI and academic integrity, redesign artifacts, and implementation intentions. Attendees will then examine 3 anonymized examples of AI-era redesign moves, such as syllabus AI-use language, process-based assignments, reasoning-focused rubrics, or accessibility improvements.
By the end of the session, participants will be able to identify research-informed indicators of meaningful AI-era course redesign, describe how faculty self-efficacy, beliefs about AI, accessibility practices, and workload concerns shape implementation, and consider how to support faculty in moving from AI policies toward assignments and assessments that emphasize transparency, judgment, revision, and student reasoning. Participants will leave with an adaptable framework for evaluating AI-related teaching initiatives and designing sustainable faculty support in high-teaching-load environments.
Keywords
AI and Teaching
Disciplines
Teacher Education and Professional Development
Language
English
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
COinS
Teaching in the AI Era: Studying Faculty Course Redesign, Visible Thinking, and Sustainable Implementation
How can faculty development move beyond generic AI guidance toward redesigns that make student thinking visible, clarify AI expectations, and remain feasible for teaching-intensive institutions? This presentation shares the design and emerging findings from Nevada State University’s AI & Accessibility Academy study, which follows faculty from a summer course redesign academy into fall implementation. The study examines changes in faculty self-efficacy around AI-era pedagogy and accessibility, the redesign practices faculty implement, how course artifacts reflect process-focused assessment and accessible design, and what factors support or constrain sustained implementation.
The session describes the study’s mixed-methods design, including faculty pre/post/follow-up surveys, artifact analysis, and a planned student survey on AI-expectation clarity and whether students perceive their thinking processes as valued, sharing preliminary patterns related to faculty confidence, beliefs about AI and academic integrity, redesign artifacts, and implementation intentions. Attendees will then examine 3 anonymized examples of AI-era redesign moves, such as syllabus AI-use language, process-based assignments, reasoning-focused rubrics, or accessibility improvements.
By the end of the session, participants will be able to identify research-informed indicators of meaningful AI-era course redesign, describe how faculty self-efficacy, beliefs about AI, accessibility practices, and workload concerns shape implementation, and consider how to support faculty in moving from AI policies toward assignments and assessments that emphasize transparency, judgment, revision, and student reasoning. Participants will leave with an adaptable framework for evaluating AI-related teaching initiatives and designing sustainable faculty support in high-teaching-load environments.
