D-GAM

The problem

Imagine, there is a new retail store being proposed in your neighbourhood. You might have several questions and concerns. Being a resident you want to provide some insight, however, problem isnt access, the infrastructure for public participation exists, the problem is the lower motivational and sustained engagement in the infrastructure of public participation in built environment.

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

Research published venues

3 papers published

• CAAD Futures · 2025
• HCI International · 2025
• AHFE · 2026

01. Problem Identification

What existing tools get wrong

A structured 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
Participatory Budgeting Platforms	Civic Engagement	Voting, Simulations, Rewards	Local Residents, Community Members
Pol.is	Civic Engagement	Opinion Clustering, Real-Time Feedback	General Public, Policymakers
Community PlanIt	Urban Planning	Missions, Coins, Leaderboards	Local Residents, Community Stakeholders
Geo-Wiki	Environmental Monitoring	Crowdsourcing, Validation Games	Citizen Scientists, Environmentalists
E-polis	Sociological Surveys	Adventure, Exploration, Simulation	Young Adults, Students
EquiCity Game	Urban Design	Participatory Design, Simulation	Urban Planners, Community Members
Participatory Chinatown	Urban Planning	Role-Playing, Missions	Local Residents, Community Stakeholders
Unlimited Cities	Urban Planning	Simulation, Interactive Visualization	General Public, Urban Planners
UrbanSim	Urban Planning	Simulation, Scenario Analysis	Urban Planners, Policymakers
CityScopeAR	Urban Design	Augmented Reality, Collaborative Design	Urban Designers, Community Members
CityScope	Urban Planning	Tangible User Interface, Simulation	Urban Planners, Community Members
PlaceSpeak	Civic Engagement	Geo-Verification, Online Consultation	General Public, Policymakers
EquiCity Game	Urban Design	Participatory Design, Simulation	Urban Planners, Community Members

• Urban Planning
• Urban Design
• Civic Engagement

Four gaps identified in the same group

• Gap 01

  Fixed system-wide gamification

  Gamification is applied at a system level rather than a project level, lacking flexibility

• Gap 02

  No mechanism for sustained engagement

  Existing platforms attract users once but provide no reason to explore deeply, return, or contribute over multiple review stages.

• Gap 03

  Accessibility of gamified tools

  While some platforms successfully utilize online access to engage a diverse audience, other initiatives, face limiitations due to their dependency on particular technologies or the need for in-person interaction.

• Gap 04

  Gamifcation as superficial engagement

  Relying on game mechanics such as points and leaderboards can sometimes oversimplify complex issues, potentially leading to superficial engagement. Moreover, while these mechanics can motivate participation, they may inadvertently prioritize competition over meaningful contributions.

Research grounding

The framework was grounded in three theories. Self-Determination Theory defined what meaningful motivation requires: autonomy, competence, and relatedness. Operant conditioning explained reward timing. Flow theory explained how to keep task complexity matched to user capability.

Click to enlarge

02. Research Process

Four phase process

• Phase 01

  Literature review & initial prototype

  Established the foundations for D-GAM and built the first task-based prototype, grounded in Self-Determination Theory, operant conditioning, and flow theory.

• Phase 02

  Evaluation & testing

  Evaluated the first task-based prototype and tested it with a focus group of professionals.

• Phase 03

  Formation of the flexible D-GAM framework

  Based on the focus group findings, the task-based approach shifted to task-free, resulting in the adaptable gamification schema — four schemas mapped onto activities.

• Phase 04

  Re-design and testing

  Re-iterated the prototype implementing the four schemas, followed by a user study.

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.

Sessions were audio-recorded and video recorded with consent and transcribed. Field notes captured non-verbal cues and moments of disagreement between participants. Transcripts were coded inductively using AI (NotebookLM), then grouped into candidate themes through thematic analysis and reviewed against the raw data for consistency.

Four themes that redirected the framework

Theme 1 — Rewards motivated engagement but lacked transparency

I would like to know about the potential rewards in the beginning — it confuses me why there is a definite and an indefinite reward.

Theme 2 — Task rigidity caused fatigue

I have to add a comment under every section, which is a tiring process. Rather, I would prefer adding a combined comment at the end.

Theme 3 — Intrinsic motivation mattered as much as extrinsic rewards

Knowing my feedback will make a difference motivates me more than a material reward.

Theme 4 — Users needed freedom to explore non-linearly

I should not be forced to complete the tasks in order to earn rewards. I may switch to different tasks.

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.

Study participants — n=10, purposive sampling, none affiliated with the project.

Participant	Background	Age
P01	Entrepreneur	29
P02	Developer	27
P03	UX Designer	31
P04	Designer, Researcher	32
P05	Teaching Assistant	27
P06	Sales Associate	27
P07	Teacher	26
P08	Designer	25
P09	Graduate Student	29
P10	Architect	28

05. 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.