Use the DOT Framework's Empathize and Test/Feedback stages as practical checklists, and build professional learning communities around shared prompting and critique practices¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article recommends that instructors treat the design thinking stages, particularly Empathize and Test/Feedback, as "practical checklists for AI integration, moving beyond ad hoc experimentation toward structured, needs-driven, iteratively evaluated AI use." Because practitioners already use progressive prompt refinement (82%) and cross-checking (79%), the authors recommend professional learning communities centered on sharing and critiquing these practices, with deliberate attention to the missing beginning-of-cycle needs assessment and end-of-cycle feedback collection.

Design Implications¶

Context¶

Requirements¶

• Peer-learning formats where faculty share AI outputs, prompts, and design processes together, to build shared norms that self-directed learning cannot provide

Constraints¶

• The data show these stages are currently the least-developed dimensions of practice, and the recommendation's effect on instructional outcomes is untested

Target Learners¶

• higher education faculty
• instructional designers

Target Learning Goals¶

• structured needs assessment before AI use
• structured feedback collection and iteration on AI-generated outputs

Affordances¶

• Dot Framework Ai Enhanced Teaching

Related Strategies¶

• Use a design thinking process with Empathize, Define, Ideate, Prototype, and Test stages to reimagine school safety
• Critique Protocol
• Integrate co-design and continuous feedback loops into AI evaluation processes
• Working Out Loud
• Evaluate instructor-authored AI teaching cases by checking seeds, attempting the analysis with student materials only, and keeping data and teaching materials under joint review
• Staged institutional implementation: audit interactions, map to teaming levels, build AI literacy, co-design, and evaluate iteratively
• Use findings on instructor power relations to inform professional development for responding to Generative AI
• Co-create micro-intervention repertoires with generative AI through a seed-request-refine prompt sequence, with the instructor curating output

Examples¶

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Key Sources¶

• Gibson, D., Azukas, M. E., & Knezek, G. (2025). Practitioner Beliefs and Behaviors in AI-Enhanced Education: DOT Framework Survey Evidence.