by lucia.hossainova -

Business idea

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.

by martina.monterisi -

The product and business ideas in pills

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.

by martina.monterisi -

Designing for different groups

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.

by Indigo Tschurtschenthaler -

Three prototypes

Design & Research 2 with Birgit Bachler

After a semester of research, I started prototyping. I thought of three possible solutions for light pollution in the interaction design field.

The first prototype I created is a reporting app for the Globe at Night project. It blends the form with a star map, allowing users to learn about the sky and contribute to the monitoring of light pollution. By adding an educational and interactive layer to the form I tested in a previous article, I want to increase its usage.

The star map works with GPS, the compass sensor and AR on your phone and is inspired by the app Stellarium. At the beginning, you are asked to find a constellation. Once you move your phone to the right position and find it on the sky (in this case a black sheet with white dots drawn on it), you can learn about the constellation or report light pollution starting from it. To report pollution, you simply have to tap on the stars you see, which change colour when selected, and then tap on “submit data”.

The second prototype is an app or web app that serves as an aid to the installation of outdoor fixtures in a way that is dark sky friendly. It has 5 steps inspired by the Five Principles for Responsible Outdoor Lighting by DarkSky:

Image by DarkSky

Some of them require the usage of the phone camera to check if the actions are performed correctly (direction, brightness, temperature), another one allows you to set timers and motion sensors for the smart fixture.

The third prototype is a game that shows the consequences of light pollution from the perspective of moths. Their sense of orientation relies on the moon, the only natural light source. When they see artificial light, they are trapped in atypical flight patterns, which are exhausting and often lead to death.

In this game, the player puts on AR glasses in a dark room and tries to find the moon, while avoiding artificial lights. They would be distributed in different locations in a dark room. The AR device mymics the vision of moths, so distinguishing natural and artificial light becomes a challenge. After getting too close to a certain amount of artificial lights, the player “dies”.

A bicycle light seen through the goggles.

In this first prototype, the device is a cardboard box that I cut in a way that allows the player to place it in front of their eyes and see through a plastic bag attached on the other side. I used various lights I had at home to represent different light sources. My version is done with “Wizard-of-Oz” prototyping, so at the beginning I play an introductory audio by National Geographic that explains why moths are attracted by lamps. After that, the player can start their mission of finding the moon and every time they reach a light source, I tell them what it is. After reaching three lamps, the game is over.

I chose the first prototype (light pollution reporting app) for a speed prototype reviewing we did in class to gather feedback. Someone asked how the app knows that you are looking at the right constellation and thought that there is not enough feedback telling you if your performed action is right or wrong. The app works with GPS and the compass sensor so it detects when you are pointing your phone in the right direction. Regarding feedback, I could integrate a vibration or sound feedback or a bright coloured outline when the right constellation is being looked at.

Other people suggested me to state clearly at the beginning that data is going to be submitted. While this is mentioned in the introductory paragraph of the first screen, I could make the information shorter and more immediate.

One persone said that if the app had a Tinder profile, its description would be “learn about astronomy and relax“. Someone else appreciated the combination of gaming and science.

Another person argued that the app only is for people interested in astronomy. This might be true, therefore I shall think of ways to make it attractive for a broader audience, in order to draw more attention on the issue.

by Indigo Tschurtschenthaler -

Product/Business idea

Design&Research 2 with Katerina Sedlackova

After creating value proposition map and business model canvas of the app to report light pollution, I wrote a product idea.

Light pollution is not talked about a lot. In addition, there is not a central platform to report it, and the ones that exist are difficult to use. With this product, I want to make light pollution reporting and research easy and accessible for normal citizens and scientists. This way, I aim to raise awareness on the cause and inform people, in order to fight the issue collectively.

We should care about light pollution and take action against it because it brightens the night sky, it harms nocturnal animals and disrupts biological cycles. By reporting it, we allow biologists to track data and solutions can be suggested to authorities.

The solution I offer is an app that merges reporting and instruction: users can fill out a simple form about the condition of the sky they see, see their contributions, sign petitions, but also navigate a light pollution map and discover the sky thanks to augmented reality. There is also a social media function to let people connect.

The target audience includes nature lovers, astronomers, people living in big cities, people with sleep issues, biologists and ecologists. The first ones can use the app to report and learn, while the other ones can analyse data for research.

With this product, attention can be drawn to the issue, changes in light pollution can be studied, solutions can be found and suggested to authorities. Other than that, users can find like-minded people, learn something new and feel like they made a difference.

The app can make money by selling a premium version with advanced features, like more details on the light pollution map and on the AR version of the sky. Other than that, it would rely on government or charity funds.

by Indigo Tschurtschenthaler -

Value Proposition Map

Design&Research 2 with Katerina Sedlackova

I created two value proposition maps for my app, one per customer. The first one is for nature lovers and aims to give them a tool to actively protect the environment.

The second one is for biologists and wants to give them a tool to access, filter, organise and export data about light pollution.

I also created a business model canvas to explain the idea.

by muhammad.turk -

Design & Research II – My Product & Business Strategy

Design & Research 2 | For: Katerina Sedlackova

After spending a lot of time mapping out the “system,” I’ve finally landed on a solid plan for what this project actually is and how it works as a business. Here is the breakdown of the Photography Co-Pilot.

The Big Idea

What’s the problem?

Cameras today are too much. You either have a professional DSLR with 100 confusing menus, or a smartphone that does everything for you. This “Automation Gap” means people never actually learn how to take a real photo. They just push a button and hope the AI makes it look good.

Why should we care?

Photography is an art. If the computer does everything, the “human” part of the art dies. Plus, so many young people are buying old cameras because they want that “analog” feel, but they give up because the settings are too hard to learn. We’re losing the craft.

The Solution: An AI Co-Pilot

Think of it as a bridge. Instead of the AI fixing the photo for you, it talks to you. It looks at what you’re trying to shoot and gives you a simple 1-2-3 checklist (on your phone or in the viewfinder). It says: “To get this look, turn this physical dial to f/2.8.” You still do the work; the AI just points the way.

Who is this for?

The main users are students and hobbyists (like the Gen Z “analog” crowd). The people who will actually pay for this are the big camera brands like Nikon or Sony who want to make their gear easier for new people to use.

The Impact

We move from being “Passive Button-Pushers” to “Active Pilots.” It turns frustration into that “Aha!” moment when you finally get a manual shot right.

How does it make money?

We license the “Logic” to camera brands so they can put a “Learning Mode” in their cameras. We also have a pro version of our app for people who want even more advanced guides.

Customer Profiles

To make sure this actually works, I looked at two types of people who really need this help.

Sarah: The Student

Lukas: The Nostalgic Hobbyist

Business Model Canvas

This is the “behind-the-scenes” look at how the project stays sustainable. To make this a real-world product, I’ve mapped out a strategy that involves partnering with the big players in the industry while keeping the focus on the student community.

by muhammad.turk -

Design & Research II – System, Impact, and Inclusion

Design & Research 2 | For: Katerina Sedlackova

Following my prototypes, I am now looking at how my project fits into the bigger world. I have broken this down into three parts: the system, the change it creates, and who can actually use it.

1/3: System Map

This diagram illustrates the broader ecosystem surrounding my camera-AI guidance system. I have mapped it from the core outwards to show how the project connects to the world.

The Core: The interaction between the Photographer and the Manual Camera.
Direct User Context: Students, hobbyists, and “Nostalgic Gen Z” looking for a creative rhythm.
External Ecology: The heavy hitters—Nikon/Sony (Hardware), Adobe/Midjourney (AI), and Instagram (Social). I also included E-waste, as the sustainability of our gear is part of the system.

2/3: Change and Impact (Before vs. After)

This comparison highlights the shift from automation-first snapping to learning-aware photography.

The Goal: The goal is to move the user from being a passive passenger of an automated process to an active “Pilot” who understands their tools.

3/3: Inclusion and Accessibility

Accessibility in photography is not just about “talent”; it is a systemic issue. Using the floating barrier map, I identified the physical and cognitive hurdles that stop people from mastering manual photography.

by muhammad.turk -

Design & Research II – Lo-Fi Prototypes 1/6

Design & Research 2 | For: Birgit Bachler

Following my research on “Automation in Photography,” I have spent this week diving deeper into my project by creating three different prototype scenarios. Even though I haven’t tested these with real users yet, the act of making them helped me see points I was missing and gave me a better direction for my Master’s thesis.

Prototype 1: The Mode Toggle

In this one, when the user opens the camera, they have to choose between two options. One is a Raw Mode where the user has all the control, and the other is an AI Automation mode.

The Goal: To see if forcing the user to pick a mode at the start makes them more intentional about how they want to take the photo.

Prototype 2: The AI Assistant

This is a digital assistant that pops up on the screen while you are shooting. It explains what is happening based on the scene. For example, it might say “increase shutter speed because you are shooting action” or “reduce ISO because there is too much light.”

The Goal: To see if giving the user a “why” helps them stay in control instead of the camera just fixing the settings automatically.

Prototype 3: The External Guidance Tool

This is for professional cameras. A separate device (like a phone) is attached to the camera to guide the user. It shows suggestions on which physical dials to turn to get the right settings.

The Goal: To see if the AI can act as a teacher that helps the user learn how to use the manual settings on their professional camera.

Personal Reflection: Why Prototype Now?

Creating these scenarios helped me see which directions I might follow, but it also left me with a big question about the design process. I understand that if you have a clear vision, prototyping early can save a lot of time. But when you are still in the early stages of defining and understanding the problem, I found it extremely difficult.

To be honest, it doesn’t make total sense to me to build a solution when I haven’t even fully decided what the actual problem is yet. While I know it is supposed to be beneficial, I personally didn’t find it that helpful at this stage. It felt a bit like guessing. However, the exercise did at least show me which side of the camera-AI idea has the most potential, even if the final direction is still a bit blurry.

by artun.sepken -

D&R2 SED – D2

Customer Profile & Value Proposition Map

Persona I – The Deep Worker

A knowledge worker or student who needs extended periods of uninterrupted focus to do their best work. They are productive in flow states but regularly pulled out of them by notifications, messages, and contextual switches. They need a system that protects their attention without requiring constant manual management.

Persona II – The Overloaded Student

A university student juggling coursework, communication apps, and social media across the same device. They struggle to distinguish between urgent and non-urgent signals, and often spend more time managing notifications than doing the actual work. They need a system that reduces the noise without them having to think about it.

Business Idea

What problem are you solving?

Digital interfaces are built to deliver information as fast as possible: but human attention does not work that way. Every interruption carries a cognitive recovery cost that current systems completely ignore. The result is a generation of users who are constantly reactive, chronically distracted, and unable to reach the deep focus states where their best thinking happens. There is no mainstream product that treats attention as a resource worth protecting at the system level.

Why should we care about it?

Attention is not just a productivity concern, it is a mental health issue. Sustained notification overload is linked to higher cortisol levels, reduced working memory performance, and increased anxiety. At the same time, the economic cost of fragmented attention in knowledge work is measurable: studies estimate billions in lost productive hours annually due to interruption-driven task switching. The problem affects every person who works or studies with a digital device which is effectively everyone.

What is the solution? How does it work?

An attention-aware notification layer that sits between the operating system and the user’s apps. It uses behavioral signals: typing rhythm, app dwell time, task duration, time of day, to infer whether the user is in a focused state. When focus is detected, non-urgent notifications are held and batched for delivery at a natural task boundary. When an interruption does occur, the system provides a resumption cue, a lightweight context snapshot that helps the user return to their previous task faster. No manual configuration required; the system learns the user’s patterns over time.

Who is the target audience / customer?

The primary users are knowledge workers and students: anyone whose productivity depends on sustained focus. The paying customers are organizations: companies that want to reduce burnout and increase deep work capacity among employees, educational institutions looking to support student focus, and productivity software companies that want to integrate attention-awareness into their existing tools as a premium feature.

What is going to happen? (Change & Impact)

We move from a model where every moment is equally interruptible to one where digital systems respect the rhythm of human cognition. Interruptions do not disappear, they are timed better. Users reclaim extended focus periods without having to fight their devices to do it. Over time, this reduces the normalization of fragmented attention and establishes a new expectation: that technology should protect focus, not just compete for it.