D-GAM

Overview

D-GAM turns static city-planning feedback forms into adaptive, game-based experiences that keep residents engaged. It’s built around four independent schemas — reward, feedback, project, and user — so it can be reconfigured for different civic contexts without a redesign. Two playtest-and-iterate cycles took it from a 67.46 to a 76.0 System Usability Scale score.

76.0

Final SUS score

Grade B — up from 67.46

+8.54

Improvement

Across two iterations

37

Tools audited

17 analysed in depth

3

Papers published

Peer reviewed

90%

Increase in user engagement

The problem

D-GAM framework was built to answer one question: Can gamification enhance user engagement and motivation in participatory processes?

01. Problem Identification

What existing tools get wrong

To cater the problem a review of 37 civic engagement and gamification tools, narrowed to 17 targeting general public participation, revealed a consistent pattern. Tools that attract users rarely sustain them. When gamification existed at all, it was applied at the system level: the same points and badges for every project, regardless of context or stakeholder.

37 tools reviewed, 17 analysed in depth.

Tool name	Domain / context	Game mechanics used	Target audience
Foldit	Scientific Research	Puzzle-solving, Competition	General Public, Science Enthusiasts
ESP Game	Image Tagging for AI	Points, Time Constraints, Partner Matching	General Public
Metadata Games	Cultural Heritage Archiving	Points, Leaderboards, Collaborative Tagging	Museum Visitors, Archivists, General Public
… 13 more rows

• Urban Planning
• Urban Design
• Civic Engagement

Gaps identified

Research grounding

This is the step where the foundations of the framework began. The framework was grounded in three theories.

1. 01Self-Determination Theory defined what meaningful motivation requires: autonomy, competence, and relatedness.
2. 02Operant conditioning explained reward timing.
3. 03Flow theory explained how to keep task complexity matched to user capability.

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02. Process

03. Design

From idea to wireframe to UI — V1

The first prototype followed a task-based approach. Users moved through a structured four step review: select a project, explore the design in 3D and AR, review performance data, engage in discussion, earning points and rewards at each stage. This was the version taken into the focus group study.

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  Project selection
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  AR view
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  Dedicated feedback
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  Data view
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  Data comparison
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  Engagement discourse
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  Toggled gamification
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  Reward band
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  Surprise reward notification
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  History

Initial focus group study

Based on the initial task-based prototype, a focus group study was carried out which included seven participants who were given an overview and interacted with the prototype. Four themes emerged that fundamentally changed the direction of the framework.

Four themes that redirected the framework

The D-GAM framework — four schemas

D-GAM is built on four interrelated schemas. Each operates independently — meaning the reward logic, feedback model, project structure, or user tracking can each be modified without disrupting the others. This is what makes the framework adaptable across different project types and engagement goals.

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Framework in action — four scenarios

To demonstrate adaptability, four scenarios were designed using the same four schemas — each with a different project context, stakeholder profile, and reward configuration.

Four scenarios built from the same four schemas.

Scenario	Context	Goal	Reward type
S1 · In-depth form exploration	Mixed-use building — three design alternatives posted for visual feedback	Encourage deep exploration of form and AR views, with detailed qualitative feedback	Fixed + surprise
S2 · Full participation and reward redemption	University café — designers need structured feedback across all data and form views	Ensure users complete the full review including a final survey before reward is unlocked	Fixed only
S3 · Social participation and community interaction	New school — designers seek community dialogue and collective sentiment on proposals	Encourage discussion and reply chains between participants rather than individual review	Surprise only
S4 · Full exploration for points accumulation	Any project where full exploration of all views is the primary participation goal	Increase task completion through direct redeemable rewards tied to exploration depth	Fixed only

Re-designed prototype

The redesigned prototype implemented the framework across two live project scenarios — a mixed-use building and a university café — each configured with different reward logic and navigation models. This video demonstrates the first case scenario.

Users move freely rather than through a fixed sequence. Rewards are declared upfront. Feedback can be given per view or combined at the end, which was the single most requested change from the focus group.

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04. User Study

Participants

Ten participants were recruited through purposive sampling across design, technology, and academic backgrounds — none affiliated with the D-GAM project. Sessions ran remotely via Zoom, lasting 40–45 minutes each. Each participant interacted with two scenarios, completed a post-task semi-structured interview, and filled out a System Usability Scale survey. They ranged from entrepreneurs and developers to designers, teachers, and an architect, aged 25 to 32.

05. Results

Achieved Results

The redesign is where the framework earns its claim. The task-based prototype scored below the industry average; the schema-driven redesign scored above it.

67.46

Phase 02 · focus group · n=7

Grade D · Needs improvement — below industry average, structural redesign required

76.0

Phase 04 · final study · n=10

Grade B — above industry average, framework redesign validated

+8.54

Improvement across iterations

The gain that validated the shift from task-based to task-free

90%

Increase in user engagement

Tentative, based on the 9/10 participants who found the redesign more motivating

Key findings of thematic analysis

8/10

Gamification guided the process

Strongly agreed that game elements helped them navigate the review without confusion.

9/10

Scenario 1 more motivating

Combined incremental points and surprise rewards outperformed a single upfront fixed reward.

7/10

Flexibility and adaptability

Agreed designers should be able to configure reward logic per project — validating the core premise of D-GAM.

#1

Surprise reward most engaging

Consistently identified as the most motivating element — more than progress bars or leaderboards.

06. Reflection

The next step is implementing the application with a larger pool of participants, and developing a working prototype — along with integrating AI for semantic analysis of feedback.

Three papers came out of this work, published at CAAD Futures, HCI International, and AHFE.