Process Brainstorming MS3 - G33-Moviles-2026-1/Wiki GitHub Wiki

The brainstorming process followed a diverge–converge structure, allowing the team to first explore a wide solution space and then progressively narrow it down into a coherent and feasible product direction. This approach ensured that ideas were not prematurely limited, while still resulting in a focused and justifiable set of features aligned with user needs and project constraints.

Divergence Phase

During the divergence phase, the team encouraged open and rapid idea generation without evaluating feasibility or relevance. Each participant built on previous contributions, proposing features that addressed different aspects of the core problem: finding available study spaces efficiently. Ideas ranged from basic functional needs—such as showing empty classrooms and reporting incorrect availability—to more advanced and “smart” features like personalized recommendations, schedule-based room suggestions, chatbot interaction, voice input, indoor navigation, and camera-based room recognition. This phase intentionally prioritized quantity and variety, capturing ideas related to reliability, personalization, social coordination, accessibility, and automation. The goal was to surface as many potential solutions as possible, including aspirational ones, to fully explore how technology could support students during gaps between classes.

Convergence Phase

In the convergence phase, the team shifted from idea generation to critical evaluation. Each idea was assessed against several criteria: relevance to the core user problem identified in interviews, alignment with the project scope (undergraduate students and classrooms only), technical feasibility given available resources and APIs, and added value compared to complexity. Core features that directly addressed the main pain points—such as real-time room availability, user reporting for reliability, room profiles (capacity, outlets, equipment), location-based recommendations, and schedule integration—were consolidated into clear solution directions. Overlapping ideas were grouped, refined, or merged to reduce redundancy (for example, multiple personalization-related ideas were unified under a single personalization strategy).

More complex or exploratory features, such as chatbot interaction, speech-to-text, indoor pathfinding, and camera-based room identification, were not discarded outright but deprioritized or positioned as potential future enhancements. These features were recognized as valuable but non-essential for solving the primary problem in an initial design phase, and they carried higher technical and implementation costs. Through this convergence process, the team distilled a broad set of ideas into a smaller, coherent feature set that balanced user impact, feasibility, and clarity, forming a strong foundation for subsequent design and development decisions.