“Vibe coding” is an informal way to describe building software by describing the goal and feel (“the vibe”) in plain language and letting an AI generate most of the code (or even the whole app), while you guide it with small tweaks and feedback. It’s less about writing code line‑by‑line and more about iterating: you prompt what you want, test what you get, then ask for specific changes until it matches your vision.
Instructional designers often depend on subject matter experts (SMEs) to clarify what learners need. But these conversations are typically unstructured, resulting in vague directions like “they just need to understand the system.” The outcome? Training that misses real performance gaps, unclear objectives, and wasted development cycles.
I realized the core problem isn’t SME expertise—it’s the lack of structured, purposeful questioning. Effective instructional design starts with asking, “What does good performance look like? Where do learners stumble? Which decisions matter most?” Without these answers, even the best-designed programs can fall short.
To address this, I created a simple, AI-supported tool that generates targeted SME interview questions tailored to each project’s context. The tool guides designers to clarify project basics, then produces categorized, performance-focused questions. This transforms SME conversations from vague to highly productive.
Key features include context-aware question generation, alignment with proven instructional design categories (like business goals, performance, errors, and decision-making), and easy export for immediate use.
Grounded in performance-based design and task analysis, this solution prioritizes quality over quantity—focusing on questions that yield actionable, relevant insights.
The impact? Designers move from unclear SME input to sharp, actionable findings. Learning objectives become clearer, development rework drops, and final training better matches real-world needs.
Ultimately, this project marks a shift in instructional design—from simply creating content to expertly framing problems. By improving the quality of early conversations, we can dramatically enhance learning outcomes.
