AI Tools for Insurance Professionals
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This workflow demonstrates end-to-end AI-assisted instructional design: a Gemini Gem configured for SME interview synthesis converted a raw stakeholder brief into a scoped learning design — objectives, content architecture, assessment logic; in under ten minutes. That design brief directly informed the Articulate Rise module built from it. The SCORM package is available on request.
The Workflow
Input: Raw SME brief (insurance operations staff, AI tool adoption)
Input: Raw SME brief (insurance operations staff, AI tool adoption)
Created a Gemini Gem called "ID Intake and SM Synthesis Assistant" - https://gemini.google.com/gem/1pPlW2PG4GC8vzV7zprW2c5FN37NzOU7_?usp=sharing and gave an SME brief
Output: Structured learning brief — objectives, content architecture, assessment strategy, SME gaps flagged
Output: Structured learning brief — objectives, content architecture, assessment strategy, SME gaps flagged
These were clarified and a storyboard was made, which was further tweaked to ensure explainable feedback was made for every choice (correct/incorrect) in the final summative assessment scenario-based questions.
Design Rationale
Design Rationale
Why the ID Intake and SM Synthesis Assistant Gem is structured around the Analysis phase, not general ID assistance
I think ADDIE model fails most often not in development but in analysis, specifically when analysis is rushed, superficial, or skipped entirely because the SME is impatient and the designer is under pressure to start building. Configuring the Gem around the Analysis phase was a deliberate constraint: this tool does one thing and does it completely, rather than attempting to assist across the full design lifecycle at the cost of rigour. The output structure was configured to be thus — learning context, objectives, content architecture, interaction recommendations, assessment strategy. This maps directly to the deliverables a designer needs before a storyboard can responsibly be opened. By forcing the workflow to pause at analysis and produce a structured brief, the Gem builds the one phase that most ID tools skip over.
Why SME gap flagging is built into the output, not treated as optional?
Most AI tools, when given thin or vague input, generate confident-sounding output anyway. That is the failure mode this Gem was designed to prevent. In a real instructional design engagement, an incomplete SME brief produces a structurally sound course built around the wrong problem and the gap only becomes visible at pilot or evaluation, when is expensive to fix. The SME gap flags in the output are not a courtesy; they are the Gem doing the part of analysis that requires honesty: naming what is not yet known, and formulating it as a specific follow-up question rather than a vague disclaimer. This is the difference between a tool that assists and a tool that has professional judgment built into its configuration.
Why the summative assessment includes detailed explanatory feedback for every incorrect choice
Feedback on incorrect answers in most eLearning does one of two things: it says "Incorrect — the right answer is B" (which tells the learner nothing useful), or it explains why B is correct without addressing why the learner chose A or C (which leaves the misconception intact). The incorrect-choice feedback in this course is written diagnostically. Each wrong option represents a specific reasoning error, and the feedback names that error and explains why it is wrong, not just why the correct answer is right. Option A ("reject the claim immediately") represents over-trust in AI authority; the feedback addresses that directly. Option C ("ignore the flag") represents under-trust or change resistance; the feedback addresses that directly. The learner who chose wrongly leaves with a corrected mental model, not just a red X. This is the standard formative feedback should meet, and it is the standard most authoring tool defaults make easy to skip.
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