Part VII: Capstone Projects
What These Projects Are For
Reading about dark patterns is one thing. Applying that analysis to real platforms, real behavior, and real design choices is another thing entirely. The gap between comprehension and capability is where most learning stalls. These capstone projects are designed to close that gap.
Across six parts and forty chapters, this book has built a layered argument: that the attention economy creates predictable incentive structures (Part I), that those structures exploit specific features of human neuroscience (Part II), that exploitation takes recognizable and nameable forms (Part III), that major platforms instantiate those forms in different but analyzable ways (Part IV), that the collective effects ripple outward into culture and society (Part V), and that individual, design-level, and regulatory responses are available to us (Part VI). The capstone projects ask you to pick up those analytical tools and use them.
There are three projects. They differ in scope, method, and deliverable — but all three require you to synthesize material from across the book rather than drawing on any single chapter or part. Treat the earlier chapters not as background to summarize but as instruments to deploy.
The Three Projects
Project 1: Auditing Your Own Digital Behavior is a 30-day personal investigation. You will collect systematic data on your own platform use — screen time, context, emotional state, behavioral triggers — and apply the book's frameworks to make sense of what you find. This project demands honesty. The data will be awkward. That is the point.
Project 2: Dark Pattern Analysis in the Wild asks you to choose a platform or app and conduct a structured audit, cataloging the dark patterns you find, mapping each to the cognitive mechanisms it exploits, comparing the platform's design choices against its stated values, and proposing concrete alternatives. This is analysis in the tradition of investigative criticism — rigorous, documented, arguable.
Project 3: Designing a Humane Feature inverts the critique. Instead of analyzing what is wrong with existing design, you design something better. You will identify a genuine user need, develop a feature concept, evaluate it against ethical frameworks from Chapter 39, and stress-test it against business model realities. The goal is not naive idealism but workable alternatives — designs that serve users without requiring manipulation.
Choosing Your Project
The three projects are calibrated for different learning goals and contexts.
Choose Project 1 if you want to ground the book's arguments in your own lived experience, if you are skeptical of the book's claims and want to test them empirically, or if you learn best by turning the lens inward. Be aware that this project requires sustained daily effort across a full month. Sporadic data collection undermines the analysis.
Choose Project 2 if you are drawn to investigative, critical work — if your instinct is to take a system apart and describe what you find. This project suits students with backgrounds in media studies, journalism, UX research, or policy. It also suits anyone who uses a specific platform heavily and wants to understand it analytically rather than just experientially.
Choose Project 3 if you are interested in design, product development, or technology ethics. This project is harder than it looks — designing something genuinely better requires understanding why existing designs are the way they are. Chapters 39 and 40 are essential preparation, but so are Chapters 4, 22, and the full Part III taxonomy.
What Makes a Strong Submission
Regardless of which project you choose, strong submissions share several qualities.
Specificity over generality. Vague claims about platforms being addictive or manipulative earn no points here. Name the specific pattern, the specific cognitive mechanism, the specific design element. Show the work.
Epistemic honesty. This book models appropriate uncertainty throughout. Your capstone should too. If your data is ambiguous, say so. If your analysis has limits, name them. Intellectual honesty strengthens an argument; it does not weaken it.
Synthesis across parts. The projects are capstone projects, not chapter summaries. A strong Project 2 audit, for instance, should draw on the business model logic from Part I, the neuroscience from Part II, the dark pattern taxonomy from Part III, and the platform-specific context from Part IV — not just one of these.
Engagement with counterargument. The book has been explicit throughout that many platform behaviors have multiple explanations, that research is contested in places, and that not all persuasive design is manipulative. Your analysis should grapple with these complications rather than flattening them into simple condemnation.
A clear point of view. Evidence and analysis are tools for reaching a defensible position. By the end of your project, you should know what you think — and be able to say why the evidence leads you there.
The projects that follow include detailed guidance, data collection templates, and evaluation rubrics. Use those scaffolds as starting points, not as ceilings. The most interesting work will exceed what any rubric can anticipate.