Category: Interaction Design

In my first blog post of this semester, written after the lo-fi prototyping session, I described a frustration that felt more important than the prototype itself. Moving from theoretical research into a quick physical test forced me to confront something I had been postponing: my topic is difficult to make visible. Attention, interruption and cognitive recovery are not things you can easily point at on a screen. They happen internally, across time and often become visible only through their consequences.

At the time, that post was mostly about the limitations of the format I had tested. Looking back, it was also the beginning of a more important question. If the topic I am researching is hard to prototype, then maybe the issue is not only the prototype. Maybe I had not yet defined clearly enough what kind of design problem I was actually trying to make visible.

This post continues that questioning.

Where the First Semester Left Off

Last semester I wrote ten posts about attention, flow, interruption, memory, emotion, and neuroadaptive interfaces. Taken one by one they look scattered. Taken together, they were building toward something I could not yet name.

The clearest position I reached was in my final post: interruption is not a problem to eliminate, it is a condition to design for. What matters is not whether a system interrupts, but whether it does so at the right moment, with the right cost, and with enough support for the user to recover.

That framing felt like a conclusion at the time. Looking back, it was more like a door. The research had built a detailed picture of what goes wrong attention fractures, cognitive load spikes, emotional friction accumulates, memory decays but it had not yet said what to do about it, in what context, using what kind of design.

That is what this semester is supposed to resolve.

A Detour That Wasn’t Really a Detour

Earlier this semester I joined a project connected to CERN’s IdeaSquare initiative through our university. My group was working on individual and communal spaces, specifically the idea of modular, adaptive environments that can shift and reconfigure themselves in response to the people using them.

At some point during the project I pushed the team toward a particular angle: spaces that adapt to their users, not the other way around. The environment should read who is there, what they are doing, and what they need, then reorganize accordingly. Less about static design and more about responsive systems.

I did not connect this to my research at the time. It just felt like the right direction for that project.

Later I realized I had done exactly the same thing I had been studying. The question I brought into that spatial design project was the same question underneath my first semester work: what would it look like if systems adapted to human state, rather than expecting humans to adapt to them? In the CERN project, the system was a physical space. In my research, it had been a digital interface. But the logic was identical.

That overlap was worth noting. Not because it proves anything, but because it confirmed something: this is not an academic interest I adopted for a class. It is genuinely how I think about design.

The Problem With Where I Was

After last semester, I had a topic cluster but not a research direction. There is a real difference. A topic cluster is a set of connected ideas you can read about indefinitely. A research direction has a specific question, a context, and a method.

My cluster was attention, interruption, cognitive load, recovery, neuroadaptive systems. Interesting and defensible, but I was circling the same ideas without committing to a specific design problem.

The first blog post of this semester already hinted at this. The lo-fi prototype did not fail because the topic was weak. It struggled because I was trying to represent an internal and temporal topic before fully deciding what form of intervention I was actually designing toward. The session was useful precisely because it exposed that gap.

The second semester also added a different kind of pressure. The business-framing exercises pushed me to specify who is affected, how and what a system would actually do. Together with the prototyping difficulties, this made clear that narrowing was not optional anymore.

What I Actually Want to Research

The direction I have arrived at is best described as adaptive interaction design.

Not notification management. Not UX for focus apps. The broader question is what it would mean for interfaces to adapt their behavior based on the user’s current cognitive state, rather than treating every moment as equally available for input, output, or interruption.

The interest in neuroadaptive and EEG-based systems from last semester was never really about brainwaves. It was about the interaction logic they represent: a closed-loop system that reads the user continuously and responds accordingly. That loop, not the hardware, is what I find compelling. The philosophical stance is that machines should adapt to humans, not the other way around. The sensor stack is just one implementation of that idea.

What has become clearer this semester is that newer forms of adaptive systems make this question even more urgent. When systems act, update, or reorganize information while the user is absent, the moment of return becomes a design problem in itself. My earlier research on interruption, memory, and resumption maps directly onto this. The cognitive science is already there. What is still missing is a clear design response.

Where This Is Heading

I am not committing to a final thesis question this semester. That is not what this phase is for. What I am committing to is a direction: adaptive interaction design, specifically the intersection of cognitive state, interruption, and resumption in contexts where systems change while the user is absent.

The medium is still open. It could remain screen-based. It could later move into spatial or wearable contexts if the research actually needs that. I do not want to decide the form too early.

What I want to do this semester is go deeper into the literature on workload-aware systems, neuroadaptive interaction, and newer adaptive interface models, then develop a prototype concept that makes the re-entry problem testable. Not to solve it immediately, but to make it visible enough to study.

The first blog post of this semester started with a prototype that revealed the limits of my current framing. This post is the continuation of that realization. The question is no longer only how to prototype interruption and recovery. It is how to define a research direction where adaptation itself becomes the design material.

That is the point I seem to have reached. Now the next task is to see whether I can narrow it without losing what made it interesting in the first place.

Digital platforms use dopamine sensitive patterns to exploit your biological reward systems. Software functions as a dopamine dispensing machine. This blogpost introduces a tool to reclaim your mental energy and autonomy.

The Problem
Addictive design removes natural stopping cues. Features like infinite scroll and autoplay bypass your conscious decision making, deceptive tactics appear in 97 percent of popular mobile apps. These dark patterns steer behavior through obscured information or emotional manipulation. Mechanisms operate below conscious awareness, you often attribute high consumption to personal weakness instead of recognizing intentional design.

Why You Should Care
Manipulation causes psychological distress as constant reward seeking erodes your dopamine household. This leads to stress and anxiety and diminishes your capacity for deep focus and can even lead to financial loss. Algorithms prioritize engagement over wellbeing, they take your attention hostage and degrade collective thought.

The Solution
The Tool is a cross platform tool, it acts as a translator between manipulative code and your mind. The tool identifies black hat gamification, it detects countdown timers and streaks that use fear of loss to coerce engagement. The tool injects constructive friction and provides prompts after 30 minutes of scrolling to bring your conscious mind back into the loop. A transparency layer highlights exit options and translates psychological tactics into actionable insights. Calm tech integration shifts non urgent information to the periphery which respects your right to be undisturbed.

Target Audience

• The Autonomy Seeker (User): Digital natives who feel burned out by social media and want to reclaim their cognitive sustainability
• The Vulnerable Transactor (User): Individuals (like children or the elderly) who are often targeted by predatory patterns due to low impulse control or digital literacy
• The Ethical Brand (Customer): Companies looking to move toward Fair UX to build long-term trust, which is becoming a more valuable asset than short-term screen time

Change and Impact
The product transitions technology from an extractive model to a humane model:

• Users regain autonomy. Eliminating persuasive elements reduces unwanted screen time by 37 to 65 percent.
• Making dark patterns visible creates market demand for Fair UX. This forces companies to prioritize your wellbeing over engagement metrics.

Starting with a problem statement

Data visualization is a field built on making information accessible, engaging and clear. AI is now present at every stage of that process, but almost no one in the field has a shared framework for how to properly use it. Most creatives are learning by trial and error, which in practice means learning by wasting: wasted prompts, wasted time, outputs that miss the mark because the input was never quite intentional. For newcomers, the barrier is even higher, since the technical language is intimidating and there is no obvious starting point.

Beyond individual frustration, the wider feeling of uncertainty is something real. Unstructured AI use in storytelling can reinforce structural bias, flatten narrative diversity, and quietly shift creative authorship away from the human without anyone deciding that should happen.

The Solution

The methodology this project is building is a practical open framework that guides creatives through the stages of data-driven storytelling, with clear indications of where AI genuinely helps, how to write prompts with purpose, and how to stay in control of the narrative throughout. It can be described as a documented design thinking process, designed to be used as a reference, taught in academic contexts, or adopted by studios building internal guidelines.

It works by breaking the creative process into stages, assigning AI a defined and intentional role at each one, and giving users the vocabulary and structure to make decisions rather than just react to output.

The target audience is anyone working at the intersection of data and narrative: visualization designers, data journalists, researchers, students, and freelancers. The customer, in an academic and institutional sense, could be universities, design programs, and creative organizations looking for a responsible framework to teach or reference.

The change is not dramatic. It looks like a field that slowly develops a common language for something it is already doing.

Should we really talk about money?

Honestly, monetizing this personally feels like the wrong frame for what it is. But if we should consider this option, there are a couple possible paths worth naming. Institutional licensing to universities or design schools that want to integrate the framework into their curricula is the most natural fit. Funded research continuation through academic grants is another. Further down the line, a workshop or short course format built around the methodology could generate income without compromising the open-access nature of the core framework. The goal is not actually profit, yet reach.

Here follows a possible business model structure that could work for such idea.

Every research project, at some point, has to find an answer to the following question: who actually needs this, and why would they reach for it? Mapping out the customer profiles and value proposition for this methodology felt like one of the more revealing steps of the process, mainly because it forced me to stop thinking like a researcher and start thinking like someone who has to explain why this matters to a stranger. Two potential users came to mind immediately, and they arrive at the same problem from different starting points.

Profile 1: the intermediate creative or researcher

This person already works in data visualization or data journalism. They know their tools, they have communicative goals, and they have probably already experimented with AI in some part of their process. The frustration is not unfamiliarity. It is the lack of structure. They write prompts without a clear framework, use AI across too many stages, and spend more time correcting output than creating. What they want is a workflow they can repeat, trust, and call their own. A process that saves resources, reduces noise, and keeps their authorial voice intact.

Profile 2: the student or freelancer new to data-driven storytelling

This person has just entered the field. They might be studying design, communication, or journalism, or picking up freelance work that is pushing them toward data narratives for the first time. AI feels both exciting and overwhelming. They do not yet have a reference point for what “good” looks like in this process, and the specialized terminology alone is enough to make them feel like they do not belong. What they need is not just a workflow but something that builds their confidence and knowledge at the same time as it guides their practice.

What this methodology could offer to both

For both profiles, the core offer is the same: a step-by-step framework that makes intentional AI use in data storytelling accessible, documented, and repeatable. For the intermediate user, it brings order to an already active practice. For the newcomer, it lowers the barrier to entry without oversimplifying the field.

The pain relievers are practical: fewer wasted prompts, a clear structure for each stage of the creative process, a built-in glossary so no one has to go looking for definitions elsewhere, and a simpler visual version for those who find dense theoretical language a barrier.

The gain creators go deeper. Both profiles walk away with more than a finished project. They build transferable skills, develop a personal voice in how they collaborate with AI, and become more conscious and ethical users of a tool that is not going away.