Dt Curriculum

title: 'DT Curriculum Module 4: Brainstorming' description: DT curriculum reference for Module 4 brainstorming; load when teaching divergent-convergent phases, constraint-driven creativity, and philosophy-based clustering.

Brainstorming is the entry point to the Solution Space. After the Problem Space established what problems exist and for whom, brainstorming generates the widest possible range of approaches before evaluating any of them. This module teaches learners why strict phase separation and quantity-first thinking produce stronger solution portfolios than careful early filtering.

Key Concepts

Divergent vs convergent phases — Brainstorming operates in two strictly separated phases. Divergent thinking generates as many ideas as possible without evaluation. Convergent thinking then groups and selects from that pool. Mixing the phases — evaluating ideas while generating them — causes premature convergence where early ideas anchor thinking and psychologically safer options dominate. Learners often believe they can generate and evaluate simultaneously; research consistently shows this produces fewer and less creative ideas.

Constraint-driven creativity — Environmental limitations from research become creative drivers rather than barriers. "Operators have greasy hands" is not a dead end — it is a creative prompt that leads to voice activation, gesture control, foot pedals, and elbow-operated interfaces. Learners commonly treat constraints as reasons solutions will not work rather than as parameters that shape novel approaches.

Philosophy-based clustering — Ideas are grouped by underlying solution approach ("hands-free interaction," "collaborative knowledge sharing") rather than surface features ("voice chatbot," "wiki page"). This reveals solution themes where each theme represents a different philosophy for addressing the problem. Learners tend to group by technology ("AI solutions," "mobile solutions") which obscures the underlying design intent.

Minimum idea threshold — Teams generate at least 15 ideas before any evaluation begins. The threshold pushes past obvious first responses into creative territory. Learners resist this, believing their first 5 ideas cover the space; in practice, the most innovative concepts typically emerge in the second half of ideation.

Techniques

AI spring-boarding uses AI to generate a rapid initial set of ideas that the team then builds on, redirects, or inverts. The AI output is a starting point for human creativity, not the answer. Good output is a set of AI-generated starters that the team has modified, combined, or used as inspiration for entirely different approaches. The pitfall is accepting AI output as the final idea set.

Perspective multiplication reframes the problem from different stakeholder viewpoints: "How would a night-shift operator solve this?" → "How would a safety officer solve this?" → "How would a new hire solve this?" Each perspective generates ideas the others miss. Good output is ideas tagged by the perspective that generated them. The pitfall is defaulting to the most powerful stakeholder's perspective.

Scenario expansion takes a single idea and maps it across different use cases: normal operation, emergency situations, shift changes, training new workers. This reveals whether an idea is robust or fragile. Good output is a set of ideas stress-tested against realistic scenarios. The pitfall is only considering ideal conditions.

Comprehension Checks

1. A team generated 8 ideas and immediately began voting on the best one. What two problems does this approach create?

2. During brainstorming, a team member says "Voice control would never work, it is too noisy." Explain why this statement is harmful during the divergent phase and how it should be handled.

3. A team clustered their ideas into "AI solutions," "mobile solutions," and "hardware solutions." What is wrong with this clustering approach, and how should they recluster using philosophy-based grouping?

4. Why does the manufacturing scenario constraint of greasy hands improve brainstorming rather than limit it? Provide an example idea that exists only because of this constraint.

Practice Exercises

Exercise: Constraint-to-idea generation — Starting from three manufacturing constraints (85-90 dB noise, greasy hands, limited floor space), generate at least 5 ideas per constraint. Then identify which ideas address multiple constraints simultaneously — these are often the strongest candidates.

Exercise: Philosophy-based clustering — Given these 10 ideas for the manufacturing scenario: (1) voice-activated repair guide, (2) buddy system app for shift pairs, (3) AR overlay on equipment, (4) senior operator video library, (5) predictive maintenance alerts, (6) gesture-controlled display, (7) mentor matching across shifts, (8) vibration-pattern notifications, (9) knowledge capture during repairs, (10) foot-pedal interface. Group them into 3-4 philosophy-based themes and name each theme.

Learner Level Adaptations

Beginners should focus on the divergent-convergent distinction and practice generating ideas without evaluation.

Intermediate learners benefit from comparing philosophy-based and technology-based clustering approaches and understanding how synthesis themes from Method 3 scope the brainstorming space.

Advanced learners should explore how group dynamics (seniority, personality, domain expertise) affect which ideas get generated and suppressed, and analyze when a brainstorming session signals the need to return to Method 2 for more research.

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