Playful Design for Smart Cities

Earlier this week I escaped the miserable weather and food of the Netherlands to spend a couple of days in Barcelona, where I attended the ‘Playful Design for Smart Cities’ workshop at RMIT Europe.

I helped Jussi Holopainen run a workshop in which participants from industry, government and academia together defined projects aimed at further exploring this idea of playful design within the context of smart cities, without falling into the trap of solutionism.

Before the workshop I presented a summary of my chapter in The Gameful World, along with some of my current thinking on it. There were also great talks by Judith Ackermann, Florian ‘Floyd’ Müller, and Gilly Karjevsky and Sebastian Quack.

Below are the slides for my talk and links to all the articles, books and examples I explicitly and implicitly referenced throughout.

Adapting intelligent tools for creativity

I read Alper’s book on conversational user interfaces over the weekend and was struck by this paragraph:

“The holy grail of a conversational system would be one that’s aware of itself — one that knows its own model and internal structure and allows you to change all of that by talking to it. Imagine being able to tell Siri to tone it down a bit with the jokes and that it would then actually do that.”

His point stuck with me because I think this is of particular importance to creative tools. These need to be flexible so that a variety of people can use them in different circumstances. This adaptability is what lends a tool depth.

The depth I am thinking of in creative tools is similar to the one in games, which appears to be derived from a kind of semi-orderedness. In short, you’re looking for a sweet spot between too simple and too complex.

And of course, you need good defaults.

Back to adaptation. This can happen in at least two ways on the interface level: modal or modeless. A simple example of the former would be to go into a preferences window to change the behaviour of your drawing package. Similarly, modeless adaptation happens when you rearrange some panels to better suit the task at hand.

Returning to Siri, the equivalence of modeless adaptation would be to tell her to tone it down when her sense of humor irks you.

For the modal solution, imagine a humor slider in a settings screen somewhere. This would be a terrible solution because it offers a poor mapping of a control to a personality trait. Can you pinpoint on a scale of 1 to 10 your preferred amount of humor in your hypothetical personal assistant? And anyway, doesn’t it depend on a lot of situational things such as your mood, the particular task you’re trying to complete and so on? In short, this requires something more situated and adaptive.

So just being able to tell Siri to tone it down would be the equivalent of rearranging your Photoshop palets. And in a next interaction Siri might carefully try some humor again to gauge your response. And if you encourage her, she might be more humorous again.

Enough about funny Siri for now because it’s a bit of a silly example.

Funny Siri, although she’s a bit of a Silly example, does illustrate another problem I am trying to wrap my head around. How does an intelligent tool for creativity communicate its internal state? Because it is probabilistic, it can’t be easily mapped to a graphic information display. And so our old way of manipulating state, and more specifically adapting a tool to our needs becomes very different too.

It seems to be best for an intelligent system to be open to suggestions from users about how to behave. Adapting an intelligent creative tool is less like rearranging your workspace and more like coordinating with a coworker.

My ideal is for this to be done in the same mode (and so using the same controls) as when doing the work itself. I expect this to allow for more fluid interactions, going back and forth between doing the work at hand, and meta-communication about how the system supports the work. I think if we look at how people collaborate this happens a lot, communication and meta-communication going on continuously in the same channels.

We don’t need a self-aware artificial intelligence to do this. We need to apply what computer scientists call supervised learning. The basic idea is to provide a system with example inputs and desired outputs, and let it infer the necessary rules from them. If the results are unsatisfactory, you simply continue training it until it performs well enough.

A super fun example of this approach is the Wekinator, a piece of machine learning software for creating musical instruments. Below is a video in which Wekinator’s creator Rebecca Fiebrink performs several demos.

Here we have an intelligent system learning from examples. A person manipulating data in stead of code to get to a particular desired behaviour. But what Wekinator lacks and what I expect will be required for this type of thing to really catch on is for the training to happen in the same mode or medium as the performance. The technology seems to be getting there, but there are many interaction design problems remaining to be solved.


I am back in the Netherlands for over seven weeks now but I am still busy running around the country reconnecting to people and telling them about my experiences in Singapore.

It was great.

I really did manage to reconsider a lot of things and got reoriented, about which more later.

Learned a lot about myself and others by meeting and working with lots of new people from different backgrounds.

And I do miss the city already. The iron-clad guarantee of sun and warmth. Many places to explore offering lots of surprising experiences. And on every street corner, amazing affordable food.

I will miss Singapore, and I am thankful for all it has done for me in the brief period I got to call it my home.

Below are some photos. More are over at Flickr. Happy scrolling and maybe don’t look at these if you’re hungry.

Continue reading Singapore

Generating UI design variations

AI design tool for UI design alternatives

I am still thinking about AI and design. How is the design process of AI products different? How is the user experience of AI products different? Can design tools be improved with AI?

When it comes to improving design tools with AI my starting point is game design and development. What follows is a quick sketch of one idea, just to get it out of my system.

‘Mixed-initiative’ tools for procedural generation (such as Tanagra) allow designers to create high-level structures which a machine uses to produce full-fledged game content (such as levels). It happens in a real-time. There is a continuous back-and-forth between designer and machine.

Software user interfaces, on mobile in particular, are increasingly frequently assembled from ready-made components according to more or less well-described rules taken from design languages such as Material Design. These design languages are currently primarily described for human consumption. But it should be a small step to make a design language machine-readable.

So I see an opportunity here where a designer might assemble a UI like they do now, and a machine can do several things. For example it can test for adherence to design language rules, suggest corrections or even auto-correct as the designer works.

More interestingly, a machine might take one UI mockup, and provide the designer with several more possible variations. To do this it could use different layouts, or alternative components that serve a same or similar purpose to the ones used.

In high pressure work environments where time is scarce, corners are often cut in the divergence phase of design. Machines could augment designers so that generating many design alternatives becomes less laborious both mentally and physically. Ideally, machines would surprise and even inspire us. And the final say would still be ours.

Engagement design worksheets

Engagement design workshop at General Assembly Singapore

In June/July of this year I helped Michael Fillié teach two classes about engagement design at General Assembly Singapore. The first was theoretical and the second practical. For the practical class we created a couple of worksheets which participants used in groups to gradually build a design concept for a new product or product improvement aimed at long-term engagement. Below are the worksheets along with some notes on how to use them. I’m hoping they may be useful in your own practice.

A practical note: Each of these worksheets is designed to be printed on A1 paper. (Click on the images to get the PDFs.) We worked on them using post-it notes so that it is easy to add, change or remove things as you go.

Problem statement and persona


We started with understanding the problem and the user. This worksheet is an adaptation of the persona sheet by Strategyzer. To use it you begin at the top, fleshing out the problem in the form of stating the engagement challenge, and the business goals. Then, you select a user segment which is relevant to the problem.

The middle section of the sheet is used to describe them in the form of a persona. Start with putting a face on them. Give the persona a name and add some demographic details relevant for the user’s behaviour. Then, move on to exploring what their environment looks and sounds like and what they are thinking and feeling. Finally, try to describe what issues the user is having that are addressed by the product and what the user stands to gain from using the product.

The third section of this sheet is used to wrap up the first exercise by doing a quick gap analysis of what the business would like to see in terms of user behaviour and what the user is currently doing. This will help pin down the engagement design concept fleshed out in the next exercises.

Engagement loop


Exercise two builds on the understanding of the problem and the user and offers a structured way of thinking through a possible solution. For this we use the engagement loop model developed by Sebastian Deterding. There are different places we can start here but one that often works well is to start imagining the Big Hairy Audacious Goal the user is looking to achieve. This is the challenge. It is a thing (usually a skill) the user can improve at. Note this challenge down in the middle. Then, working around the challenge, describe a measurable goal the user can achieve on their way to mastering the challenge. Describe the action the user can take with the product towards that goal, and the feedback the product will give them to let them know their action has succeeded and how much closer it has gotten them to the goal. Finally and crucially, try to describe what kind of motivation the user is driven by and make sure the goals, actions and feedback make sense in that light. If not, adjust things until it all clicks.



The final exercise is devoted to visualising and telling a story about the engagement loop we developed in the abstract in the previous block. It is a typical storyboard, but we have constrained it to a set of story beats you must hit to build a satisfying narrative. We go from introducing the user and their challenge, to how the product communicates the goal and action to what a user does with it and how they get feedback on that to (fast-forward) how they feel when they ultimately master the challenge. It makes the design concept relatable to outsiders and can serve as a jumping off point for further design and development.

Use, adapt and share

Together, these three exercises and worksheets are a great way to think through an engagement design problem. We used them for teaching but I can also imagine teams using them to explore a solution to a problem they might be having with an existing product, or as a way to kickstart the development of a new product.

We’ve built on other people’s work for these so it only makes sense to share them again for others to use and build on. If you do use them I would love to hear about your experiences.

Doing UX inside of Scrum

Some notes on how I am currently “doing user experience” inside of Scrum. This approach has evolved from my projects at Hubbub as well as more recently my work with ARTO and on a project at Edenspiekermann. So I have found it works with both startups and agency style projects.

The starting point is to understand that Scrum is intended to be a container. It is a process framework. It should be able to hold any other activity you think you need as a team. So if we feel we need to add UX somehow, we should try to make it part of Scrum and not something that is tacked onto Scrum. Why not tack something on? Because it signals design is somehow distinct from development. And the whole point of doing agile is to have cross-functional teams. If you set up a separate process for design you are highly likely not to benefit from the full collective intelligence of the combined design and development team. So no, design needs to be inside of the Scrum container.

Staggered sprints are not the answer either because you are still splitting the team into design and development, hampering cross-collaboration and transparency. You’re basically inviting Taylorism back into your process—the very thing you were trying to getting away from.

When you are uncomfortable with putting designers and developers all in the same team and the same process the answer is not to make your process more elaborate, parcel things up, and decrease “messy” interactions. The answer is increasing conversation, not eliminating it.

It turns out things aren’t remotely as complicated as they appear to be. The key is understanding Scrum’s events. The big event holding all other events is the sprint. The sprint outputs a releasable increment of “done" product. The development team does everything required to achieve the sprint goal collaboratively determined during sprint planning. Naturally this includes any design needed for the product. I think of this as the ‘production’ type of design. It typically consists mostly of UI design. There may already be some preliminary UI design available at the start of the sprint but it does not have to be finished.

What about the kind of design that is required for figuring out what to build in the first place? It might not be obvious at first, but Scrum actually has an ongoing process which readily accommodates it: backlog refinement. These are all activities required to get a product backlog item in shape for sprint planning. This is emphatically not a solo show for the product manager to conduct. It is something the whole team collaborates on. Developers and designers. In my experience designers are great at facilitating backlog refinement sessions. At the whiteboard, figuring stuff out with the whole team ‘Lean UX’ style.

I will admit product backlog refinement is Scrum’s weak point. Where it offers a lot of structure for the sprints, it offers hardly any for the backlog refinement (or grooming as some call it). But that’s okay, we can evolve our own.

I like to use Kanban to manage the process of backlog refinement. Items come into the pipeline as something we want to elaborate because we have decided we want to build it (in some form or other, can be just an experiment) in the next sprint or two. It then goes through various stages of elaboration. At the very least capturing requirements in the form of user stories or job stories, doing sketches, a lo-fi prototype, mockups and a hi-fi prototype and finally breaking the item down into work to be done and attaching an estimate to it. At this point it is ready to be part of a sprint. Crucially, during this lifecycle of an item as it is being refined, we can and should do user research if we feel we need more data, or user testing if we feel it is too risky to commit to a feature outright.

For this kind of figuring stuff out, this ‘planning’ type of design, it makes no sense to have it be part of a sprint-like structure because the work required to get it to a ‘ready’ state is much more unpredictable. The point of having a looser grooming flow is that it exists to eliminate uncertainty for when we commit to an item in a sprint.

So between the sprint and backlog refinement, Scrum readily accommodates design. ‘Production’ type design happens inside of the sprint and designers are considered part of the development team. ‘Planning’ type of design happens as part of backlog refinement.

So no need to tack on a separate process. It keeps the process simple and understandable, thus increasing transparency for the whole team. It prevents design from becoming a black box to others. And when we make design part of the container process framework that is Scrum, we reap the rewards of the team’s collective intelligence and we increase our agility.

Prototyping is a team sport

Lately I have been binging on books, presentations and articles related to ‘Lean UX’. I don’t like the term, but then I don’t like the tech industry’s love for inventing a new label for every damn thing. I do like the things emphasises: shared understanding, deep collaboration, continuous user feedback. These are principles that have always implicitly guided the choices I made when leading teams at Hubbub and now also as a member of several teams in the role of product designer.

In all these lean UX readings a thing that keeps coming up again and again is prototyping. Prototypes are the go-to way of doing ‘experiments’, in lean-speak. Other things can be done as well—surveys, interviews, whatever—but more often than not, assumptions are tested with prototypes.

Which is great! And also unsurprising as prototyping has really been embraced by the tech world. And tools for rapid prototyping are getting a lot of attention and interest as a result. However, this comes with a couple of risks. For one, sometimes it is fine to stick to paper. But the lure of shiny prototyping tools is strong. You’d rather not show a crappy drawing to a user. What if they hate it? However, high fidelity prototyping is always more costly than paper. So although well-intentioned, prototyping tools can encourage wastefulness, the bane of lean.

There is a bigger danger which runs against the lean ethos, though. Some tools afford deep collaboration more than others. Let’s be real: none afford deeper collaboration than paper and whiteboards. There is one person behind the controls when prototyping with a tool. So in my view, one should only ever progress to that step once a team effort has been made to hash out the rough outlines of what is to be prototyped. Basically: always paper prototype the digital prototype. Together.

I have had a lot of fun lately playing with browser prototypes and with prototyping in Framer. But as I was getting back into all of this I did notice this risk: All of a sudden there is a person on the team who does the prototypes. Unless this solo prototyping is preceded by shared prototyping, this is a problem. Because the rest of the team is left out of the thinking-through-making which makes the prototyping process so valuable in addition to the testable artefacts it outputs.

It is I think a key oversight of the ‘should designers code’ debaters and to an extent one made by all prototyping tool manufacturers: Individuals don’t prototype, teams do. Prototyping is a team sport. And so the success of a tool depends not only on how well it supports individual prototyping activities but also how well it embeds itself in collaborative workflows.

In addition to the tools themselves getting better at supporting collaborative workflows, I would also love to see more tutorials, both official and from the community, about how to use a prototyping tool within the larger context of a team doing some form of agile. Most tutorials now focus on “how do I make this thing with this tool”. Useful, up to a point. But a large part of prototyping is to arrive at “the thing” together.

One of the lean UX things I devoured was this presentation by Bill Scott in which he talks about aligning a prototyping and a development tech stack, so that the gap between design and engineering is bridged not just with processes but also with tooling. His example applies to web development and app development using web technologies. I wonder what a similar approach looks like for native mobile app development. But this is the sort of thing I am talking about: Smart thinking about how to actually do this lean thing in the real world. I believe organising ourselves so that we can prototype as a team is absolutely key. I will pick my tools and processes accordingly in future.

All of the above is as usual mostly a reminder to self: As a designer your role is not to go off and work solo on brilliant prototypes. Your role is to facilitate such efforts by the whole team. Sure, there will be solo deep designerly crafting happening. But it will not add up to anything if it is not embedded in a collaborative design and development framework.


Running the bay

I have started running. I never thought I would. I think I was put off running in high school when during physical education they would occasionally force us to run 5k out of the blue. I remember it was torture. I never did particularly well and was sore afterwards so I came to associate it with failure and tedium and so on.

When we moved to Singapore I decided I would do some more exercise and at some point read this thing about how running is a cheap and easy way to get some exercise in and that it is particularly fun to do with your partner.

My wife has always been a runner so I suggested we started running together. She was surprised I think but agreed it would be a good idea. The next weekend I went and got a pair of shoes and that same day we were off to our first run.

In the beginning it was a hard to find good routes. I could not manage much more than 3k which did not help. But 3k turned into 5k. Around that time we found a field in the neighbourhood we could do laps around. When that became boring we decided to switch to the bay, a very popular spot, and we started doing 6–8k runs there. We’ve been sticking to that ever since.

Now we run roughly two to three times a week. Sometimes we try one of the parks and run some trails. I find I enjoy trail running even more because it is less about speed and more about awareness. And there is more to see so it never gets boring.

We were on holiday on Bali the other week and I brought my gear and ran some trails through rice fields and along the beach there as well and it struck me that this is possibly the greatest thing about running. You can do it anywhere you go, and once you build up a decent amount of stamina, your legs will take you wherever it is you want to go. It is an incredibly liberating feeling.

It took a bit of doing to get to that point. But now that I’ve reached it, I’m hooked.


Time for a status update on my stay in Singapore. I have already entered the final three months of my time here. Time flies when you’re having fun eating everything in sight, it turns out.

On the work front I have indeed found the time to do some thinking about what my next big thing will be. Nothing has firmed up to the point where I feel like sharing it here but I am enjoying the conversations I am having with various people about it.

In the meantime, I have been keeping busy working with a local startup called ARTO. I have taken on the role of product designer and I am also responsible for product management of the user-facing parts of the thing we are building.

That “thing” is about art. There are many people who are interested in art but don’t know where to start when it comes to finding, enjoying and acquiring it. We’re building a mobile and TV app that should make that a whole lot more easy and fun.

When I say art I mean commercial, popular and contemporary art of the 2D variety. So painting, illustration, photography, etc. Things you might buy originals or prints of and put on your living room wall. Others are doing a fine job on the high end of the art market. We think there are parts remaining that have been underserved to date.

There are many moving parts to this product, ranging from a recommendation engine, content management system, mobile app, TV app and more so I am never bored. There is always something to figure out in terms of what to build and how it should work and look. For the past couple of years I was always too busy managing the studio to really get into the details of design but now I can totally focus on that and it really is a pleasure.

On the people side we have a small but growing team of brilliant individuals haling from various parts of the region including Vietnam, Myanmar and India. This lends an additional layer of fun challenge to the goings on as we constantly negotiate our differences but also discover the many commonalities afforded by the globalised tech industry. I also get to travel to Ho Chi Minh City regularly which is a nice change from the extreme order that is Singapore.

It is early days so I not only get to help shape the product from the very start but also the company itself. This includes figuring out and maintaining design and development processes. For this I find my Boydian explorations quite useful, paired with what is now more than 13 years of industry experience (how did that happen?) I have also conducted more hiring interviews in the past few months than I did in the ten years before.

In a month or two a first version of the product should be in the market. When we’ve gotten to that point I will do another of these updates. In the meantime just know I am up to my armpits in thinking-through-making about art discovery and enjoyment on screens small and large. If you have anything related to share, or would like to be one of the first to test-drive the thing when it arrives, let me know.

Artificial intelligence, creativity and metis

Boris pointed me to CreativeAI, an interesting article about creativity and artificial intelligence. It offers a really nice overview of the development of the idea of augmenting human capabilities through technology. One of the claims the authors make is that artificial intelligence is making creativity more accessible. Because tools with AI in them support humans in a range of creative tasks in a way that shortcuts the traditional requirements of long practice to acquire the necessary technical skills.

For example, ShadowDraw (PDF) is a program that helps people with freehand drawing by guessing what they are trying to create and showing a dynamically updated ‘shadow image’ on the canvas which people can use as a guide.

It is an interesting idea and in some ways these kinds of software indeed lower the threshold for people to engage in creative tasks. They are good examples of artificial intelligence as partner in stead of master or servant.

While reading CreativeAI I wasn’t entirely comfortable though and I think it may have been caused by two things.

One is that I care about creativity and I think that a good understanding of it and a daily practice at it—in the broad sense of the word—improves lives. I am also in some ways old-fashioned about it and I think the joy of creativity stems from the infinitely high skill ceiling involved and the never-ending practice it affords. Let’s call it the Jiro perspective, after the sushi chef made famous by a wonderful documentary.

So, claiming that creative tools with AI in them can shortcut all of this life-long joyful toil produces a degree of panic for me. Although it’s probably a Pastoral worldview which would be better to abandon. In a world eaten by software, it’s better to be a Promethean.

The second reason might hold more water but really is more of an open question than something I have researched in any meaningful way. I think there is more to creativity than just the technical skill required and as such the CreativeAI story runs the risk of being reductionist. While reading the article I was also slowly but surely making my way through one of the final chapters of James C. Scott’s Seeing Like a State, which is about the concept of metis.

It is probably the most interesting chapter of the whole book. Scott introduces metis as a form of knowledge different from that produced by science. Here are some quick excerpts from the book that provide a sense of what it is about. But I really can’t do the richness of his description justice here. I am trying to keep this short.

The kind of knowledge required in such endeavors is not deductive knowledge from first principles but rather what Greeks of the classical period called metis, a concept to which we shall return. […] metis is better understood as the kind of knowledge that can be acquired only by long practice at similar but rarely identical tasks, which requires constant adaptation to changing circumstances. […] It is to this kind of knowledge that [socialist writer] Luxemburg appealed when she characterized the building of socialism as “new territory” demanding “improvisation” and “creativity.”

Scott’s argument is about how authoritarian high-modernist schemes privilege scientific knowledge over metis. His exploration of what metis means is super interesting to anyone dedicated to honing a craft, or to cultivating organisations conducive to the development and application of craft in the face of uncertainty. There is a close link between metis and the concept of agility.

So circling back to artificially intelligent tools for creativity I would be interested in exploring not only how we can diminish the need for the acquisition of the technical skills required, but to also accelerate the acquisition of the practical knowledge required to apply such skills in the ever-changing real world. I suggest we expand our understanding of what it means to be creative, but without losing the link to actual practice.

For the ancient Greeks metis became synonymous with a kind of wisdom and cunning best exemplified by such figures as Odysseus and notably also Prometheus. The latter in particular exemplifies the use of creativity towards transformative ends. This is the real promise of AI for creativity in my eyes. Not to simply make it easier to reproduce things that used to be hard to create but to create new kinds of tools which have the capacity to surprise their users and to produce results that were impossible to create before.