What happened
In a September 30 Learning Guild article, instructional designer Steven Shisley proposes a six-step framework for AI-generated assessments in education and corporate training: set goals, collect materials, select a tool, design prompts, conduct human review, and evaluate assessments after deployment.
Why it matters
The framework treats question generation as one part of assessment design, not a substitute for deciding what learners should demonstrate. For educators and L&D teams, curriculum documents, training manuals, and job competency profiles can provide reference points for checking generated items. Shisley also identifies tool fit, learning-platform integration, data privacy, and organizational policies as selection considerations. His emphasis on expert review puts accuracy, clarity, bias, inclusivity, and alignment with learning goals ahead of deployment.
What to do next
Start by defining the assessment’s purpose, audience, target knowledge or skills, and constraints. Use relevant materials to draft prompts that specify subject matter, format, and complexity, then pilot the outputs. Ask educators, trainers, or subject matter experts to review and revise questions, refining prompts where needed. At deployment, check integration and accessibility; afterward, gather learner feedback and performance data to guide revisions. Shisley presents a process rather than evidence that generated questions will meet a particular quality standard, so keep human judgment central.
About this briefing
Reviewed by TutorFlow Editorial. We link the primary source, preserve its publication date, and distinguish reported claims from TutorFlow analysis. Our commentary focuses on practical decisions for educators and training teams.
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