DeDe — a design-thinking methods helper to speed up workshop planning

Outcome — quick summary

DeDe helps instructors and workshop owners pick suitable LUMA and design-thinking methods by asking a few simple constraints (problem type, time available, group size, goal). The expected outcome is faster workshop planning, fewer decisions for experts, and easier adoption of methods by non-designers.

The Goal

Build a tool that reduces cognitive load for facilitators and enables non-designers to choose effective methods quickly — improving the quality and speed of workshop preparation.

The Challenge

Students and novice facilitators often struggle to select the right method from LUMA's extensive set. Challenges include: translating ambiguous problems into method criteria, balancing time vs. impact, and presenting recommendations in a way that feels trustworthy to non-experts.

My Role

As product designer and creator of the initial prototype I defined the concept, mapped method metadata, prototyped flows and UI, and will run early tests with non-designers to validate assumptions.

Discovery

Problem origin

The idea originated from teaching LUMA methods: even after learning the frameworks, students struggled to know which method matched their specific problem or workshop constraints. This friction slows planning and creates dependency on experienced facilitators.

Requirements & constraints

To be useful, DeDe must surface methods using a small number of inputs (e.g., goal: generate ideas vs validate; available time; group size; material or space constraints) and explain why each method fits.

Method metadata modelling

I modelled each method with metadata tags (purpose, time range, participant count, divergence/convergence phase, required materials, difficulty) so the app can filter and rank relevant methods.

Validation & next steps

Planned testing

The prototype is a work in progress. The next step is to test DeDe with a group of non-designers and novice instructors to validate:

• Can they translate a real problem into the app inputs?
• Do the suggested methods match their expectations?
• Does the information provided feel sufficient to run a method?

Metrics for success

Success will be measured by reduced planning time (self-reported), increased confidence in choosing methods (surveyed), and observed ability to run a method without an expert present (usability observation).

Product roadmap

Short term: iterated prototype and small user test. Medium term: expand method catalogue, add saved workshop templates, and test an in-workshop mode to guide facilitators live.