Tag: learning

Designing Learning Experiences in a Post-ChatGPT World

Transcript of a talk delivered at LXDCON’25 on June 12, 2025.

My name is Kars. I am a postdoc at TU Delft. I research contestable AI—how to use design to ensure AI systems remain subject to societal control. I teach the responsible design of AI systems. In a previous life, I was a practicing designer of digital products and services. I will talk about designing learning experiences in a post-ChatGPT world.

Let’s start at this date.

This is when OpenAI released an early demo of ChatGPT. The chatbot quickly went viral on social media. Users shared examples of what it could do. Stories and samples included everything from travel planning to writing fables to coding computer programs. Within five days, the chatbot had attracted over one million users.

Fast forward to today, 2 years, 6 months, and 14 days later, we’ve seen a massive impact across domains, including on education.

For example, the article on the left talks about how AI cheating has become pervasive in higher education. It is fundamentally undermining the educational process itself. Students are using ChatGPT for nearly every assignment while educators struggle with ineffective detection methods and question whether traditional academic work has lost all meaning.

The one on the right talks about how students are accusing professors of being hypocritical. Teachers are using AI tools for things like course materials and grading while telling students they cannot use them.

What we’re looking at is a situation where academic integrity was already in question, on top of that, both students and faculty are quickly adopting AI, and institutions aren’t really ready for it.

These transformations in higher education give me pause. What should we change about how we design learning experiences given this new reality?

So, just to clarify, when I mention “AI” in this talk, I’m specifically referring to generative AI, or GenAI, and even more specifically, to chatbots that are powered by large language models, like ChatGPT.

Throughout this talk I will use this example of a learning experience that makes use of GenAI. Sharad Goel, Professor at Harvard Kennedy School, developed an AI Slackbot named “StatGPT” that aims to enhance student learning through interactive engagement.

It was tested in a statistics course with positive feedback from students. They described it as supportive and easily accessible, available anytime for student use. There are plans to implement StatGPT in various other courses. They say it assists in active problem-solving and consider it an example of how AI can facilitate learning, rather than replace it.

The debate around GenAI and learning has become polarized. I see the challenge as trying to find a balance. On one side, there’s complete skepticism about AI, and on the other, there’s this blind acceptance of it. What I propose is that we need an approach I call Conscious Adaptation: moving forward with full awareness of what’s being transformed.

To build the case for this approach, I will be looking at two common positions in the debates around AI and education. I’ll be focusing on four pieces of writing.

Two of them are by Ethan Mollick, from his blog. He’s a professor at the University of Pennsylvania specializing in innovation and entrepreneurship, known for his work on the potential of AI to transform different fields.

The other two pieces are by Ian Bogost, published at The Atlantic. He’s a media studies scholar, author, and game designer who teaches at Washington University. He’s known for his sobering, realist critiques of the impact of technology on society.

These, to me, exemplify two strands of the debate around AI in education.

Ethan Mollick’s position, in essence, is that AI in education is an inevitable transformation that educators must embrace and redesign around, not fight.

You could say Mollick is an optimist. But he is also really clear-eyed about how much disruption is going on. He even refers to it as the “Homework Apocalypse.” He talks about some serious issues: there are failures in detection, students are not learning as well (with exam performance dropping by about 17%), and there are a lot of misunderstandings about AI on both sides—students and faculty.

But his perspective is more about adapting to a tough situation. He’s always focused on solutions, constantly asking, “What can we do about this?” He believes that with thoughtful human efforts, we can really influence the outcomes positively.

On the other hand, Ian Bogost’s view is that AI has created an unsolvable crisis that’s fundamentally breaking traditional education and leaving teachers demoralized.

Bogost, I would describe as a realist. He accepts the inevitability of AI, noting that the “arms race will continue” and that technology will often outpace official policies. He also highlights the negative impact on faculty morale, the dependency of students, and the chaos in institutions.

He’s not suggesting that we should ban AI or go back to a time before it existed. He sees AI as something that might be the final blow to a profession that’s already struggling with deeper issues. At the same time, he emphasizes the need for human agency by calling out the lack of reflection and action from institutions.

So, they both observe the same reality, but they look at it differently. Mollick sees it as an engineering challenge—one that’s complicated but can be tackled with smart design. On the other hand, Bogost views it as a social issue that uncovers deeper problems that can’t just be fixed with technology.

Mollick thinks it’s possible to rebuild after a sort of collapse, while Bogost questions if the institutions that are supposed to do that rebuilding are really fit for the job.

Mollick would likely celebrate it as an example of co-intelligence. Bogost would likely ask what the rollout of the bot would be at the expense of, or what deeper problems its deployment unveils.

Getting past the conflict between these two views isn’t just about figuring out the best technical methods or the right order of solutions. The real challenge lies in our ability as institutions to make real changes, and we need to be careful that focusing on solutions doesn’t distract us from the important discussions we need to have.

I see three strategies that work together to create an approach that addresses the conflict between these two perspectives in a way that I believe will be more effective.

First, institutional realism is about designing interventions assuming institutions will resist change, capture innovations, or abandon initiatives. Given this, we could focus on individual teacher practices, learner-level tools, and changes that don’t require systemic transformation. We could treat every implementation as a diagnostic probe revealing actual (vs. stated) institutional capacity.

Second, loss-conscious innovation is about before implementing AI-enhanced practices, explicitly identifying what human learning processes, relationships, or skills are being replaced. We could develop metrics that track preservation alongside progress. We could build “conservation” components into new approaches to protect irreplaceable educational values.

Third, and finally, we should recognize that Mollick-style solution-building and Bogost-style critical analysis serve different but essential roles. Practitioners need actionable guidance; while the broader field needs diagnostic consciousness. We should avoid a false synthesis but instead maintain both approaches as distinct intellectual work that informs each other.

In short, striking a balance may not be the main focus; it’s more about taking practical actions while considering the overall context. Progress is important, but it’s also worth reflecting on what gets left behind. Conscious adaptation.

So, applying these strategies to Harvard’s chatbot, we could ask: (1) How can we create a feedback loop between an intervention like this and the things it uncovers about institutional limits, so that those can be addressed in the appropriate place? (2) How can we measure what value this bot adds for students and for teachers? What is it replacing, what is it adding, what is it making room for? (3) What critique of learning at Harvard is implied by this intervention?

What does all of this mean, finally, for LXD? This is an LXD conference, so I don’t need to spend a lot of time explaining what it is. But let’s just use this basic definition as a starting point. It’s about experiences, it’s about centering the learner, it’s about achieving learning outcomes, etc.

Comparing my conscious adaptation approach to what typifies LXD, I can see a number of alignments.

Both LXD and Conscious Adaptation prioritize authentic human engagement over efficiency. LXD through human-centered design, conscious adaptation through protecting meaningful intellectual effort from AI displacement.

LXD’s focus on holistic learning journeys aligns with both Mollick’s “effort is the point” and Bogost’s concern that AI shortcuts undermine the educational value embedded in struggle and synthesis.

LXD’s experimental, prototype-driven approach mirrors my “diagnostic pragmatism”—both treat interventions as learning opportunities that reveal what actually works rather than pursuing idealized solutions.

So, going back one final time to Harvard’s bot, an LXD practice aligned in this way would lead us to ask: (1) Is this leveraging GenAI to protect and promote genuine intellectual effort? (2) Are teachers and learners meaningfully engaged in the ongoing development of this technology? (3) Is this prototype properly embedded, so that its potential to create learning for the organization can be realized?

So, where does this leave us as learning experience designers? I see three practical imperatives for Conscious Adaptation.

First, we need to protect meaningful human effort while leveraging AI’s strengths. Remember that “the effort is the point” in learning. Rather than asking “can AI do this?”, we should ask “should it?” Harvard’s bot works because it scaffolds thinking rather than replacing it. We should use AI for feedback and iteration while preserving human work for synthesis and struggle.

Second, we must design for real institutions, not ideal ones. Institutions resist change, capture innovations, and abandon initiatives. We need to design assuming limited budgets, overworked staff, and competing priorities. Every implementation becomes a diagnostic probe that reveals what resistance actually tells us about institutional capacity.

Third, we have to recognize the limits of design. AI exposes deeper structural problems like grade obsession, teacher burnout, and test-driven curricula. You can’t design your way out of systemic issues, and sometimes the best move is recognizing when the problem isn’t experiential at all.

This is Conscious Adaptation—moving forward with eyes wide open.

Thanks.

‘Playful Design for Workplace Change Management’ at PLAYTrack conference 2017 in Aarhus

Lase defender collab at FUSE

At the end of last year I was invited to speak at the PLAYTrack conference in Aarhus about the workplace change management games made by Hubbub. It turned out to be a great opportunity to reconnect with the play research community.

I was very much impressed by the program assembled by the organisers. People came from a wide range of disciplines and crucially, there was ample time to discuss and reflect on the materials presented. As I tweeted afterwards, this is a thing that most conference organisers get wrong.

I was particularly inspired by the work of Benjamin Mardell and Mara Krechevsky at Harvard’s Project Zero – Making Learning Visible looks like a great resource for anyone who teaches. Then there was Reed Stevens from Northwestern University whose project FUSE is one of the most solid examples of playful learning for STEAM I’ve seen thus far. I was also fascinated by Ciara Laverty’s work at PEDAL on observing parent-child play. Miguel Sicart delivered another great provocation on the dark side of playful design. And finally I was delighted to hear about and experience for myself some of Amos Blanton’s work at the LEGO Foundation. I should also call out Ben Fincham’s many provocative contributions from the audience.

The abstract for my talk is below, which covers most of what I talked about. I tried to give people a good sense of:

  • what the games consisted of,
  • what we were aiming to achieve,
  • how both the fiction and the player activities supported these goals,
  • how we made learning outcomes visible to our players and clients,
  • and finally how we went about designing and developing these games.

Both projects have solid write-ups over at the Hubbub website, so I’ll just point to those here: Code 4 and Ripple Effect.

In the final section of the talk I spent a bit of time reflecting on how I would approach projects like this today. After all, it has been seven years since we made Code 4, and four years since Ripple Effect. That’s ages ago and my perspective has definitely changes since we made these.

Participatory design

First of all, I would get even more serious about co-designing with players at every step. I would recruit representatives of players and invest them with real influence. In the projects we did, the primary vehicle for player influence was through playtesting. But this is necessarily limited. I also won’t pretend this is at all easy to do in a commercial context.

But, these games are ultimately about improving worker productivity. So how do we make it so that workers share in the real-world profits yielded by a successful culture change?

I know of the existence of participatory design but from my experience it is not a common approach in the industry. Why?

Value sensitive design

On a related note, I would get more serious about what values are supported by the system, in whose interest they are and where they come from. Early field research and workshops with audience do surface some values but values from customer representatives tend to dominate. Again, the commercial context we work in is a potential challenge.

I know of value sensitive design, but as with participatory design, it has yet to catch on in a big way in the industry. So again, why is that?

Disintermediation

One thing I continue to be interested in is to reduce the complexity of a game system’s physical affordances (which includes its code), and to push even more of the substance of the game into those social allowances that make up the non-material aspects of the game. This allows for spontaneous renegotiation of the game by the players. This is disintermediation as a strategy. David Kanaga’s take on games as toys remains hugely inspirational in this regard, as does Bernard De Koven’s book The Well Played Game.

Gamefulness versus playfulness

Code 4 had more focus on satisfying the need for autonomy. Ripple Effect had more focus on competence, or in any case, it had less emphasis on autonomy. There was less room for ‘play’ around the core digital game. It seems to me that mastering a subjective simulation of a subject is not necessarily what a workplace game for culture change should be aiming for. So, less gameful design, more playful design.

Adaptation

Finally, the agency model does not enable us to stick around for the long haul. But workplace games might be better suited to a setup where things aren’t thought of as a one-off project but more of an ongoing process.

In How Buildings Learn, Stewart Brand talks about how architects should revisit buildings they’ve designed after they are built to learn about how people are actually using them. He also talks about how good buildings are buildings that its inhabitants can adapt to their needs. What does that look like in the context of a game for workplace culture change?

Playful Design for Workplace Change Management

Code 4 (2011, commissioned by the Tax Administration of the Netherlands) and Ripple Effect (2013, commissioned by Royal Dutch Shell) are both games for workplace change management designed and developed by Hubbub, a boutique playful design agency which operated from Utrecht, The Netherlands and Berlin, Germany between 2009 and 2015. These games are examples of how a goal-oriented serious game can be used to encourage playful appropriation of workplace infrastructure and social norms, resulting in an open-ended and creative exploration of new and innovative ways of working.

Serious game projects are usually commissioned to solve problems. Solving the problem of cultural change in a straightforward manner means viewing games as a way to persuade workers of a desired future state. They typically take videogame form, simulating the desired new way of working as determined by management. To play the game well, players need to master its system and by extension—it is assumed—learning happens.

These games can be be enjoyable experiences and an improvement on previous forms of workplace learning, but in our view they decrease the possibility space of potential workplace cultural change. They diminish worker agency, and they waste the creative and innovative potential of involving them in the invention of an improved workplace culture.

We instead choose to view workplace games as an opportunity to increase the space of possibility. We resist the temptation to bake the desired new way of working into the game’s physical and digital affordances. Instead, we leave how to play well up to the players. Since these games are team-based and collaborative, players need to negotiate their way of working around the game among themselves. In addition, because the games are distributed in time—running over a number of weeks—and are playable at player discretion during the workday, players are given license to appropriate workplace infrastructure and subvert social norms towards in-game ends.

We tried to make learning tangible in various ways. Because the games at the core are web applications to which players log on with individual accounts we were able to collect data on player behaviour. To guarantee privacy, employers did not have direct access to game databases and only received anonymised reports. We took responsibility for player learning by facilitating coaching sessions in which they could safely reflect on their game experiences. Rounding out these efforts, we conducted surveys to gain insight into the player experience from a more qualitative and subjective perspective.

These games offer a model for a reasonably democratic and ethical way of doing game-based workplace change management. However, we would like to see efforts that further democratise their design and development—involving workers at every step. We also worry about how games can be used to create the illusion of worker influence while at the same time software is deployed throughout the workplace to limit their agency.

Our examples may be inspiring but because of these developments we feel we can’t continue this type of work without seriously reconsidering our current processes, technology stacks and business practices—and ultimately whether we should be making games at all.

My plans for 2016

Long story short: my plan is to make plans.

Hubbub has gone into hibernation. After more than six years of leading a boutique playful design agency I am returning to freelance life. At least for the short term.

I will use the flexibility afforded by this freeing up of time to take stock of where I have come from and where I am headed. ‘Orientation is the Schwerpunkt,’ as Boyd says. I have definitely cycled back through my meta-OODA-loop and am firmly back in the second O.

To make things more interesting I have exchanged the Netherlands for Singapore. I will be here until August. It is going to be fun to explore the things this city has to offer. I am curious what the technology and design scene is like when seen up close. So I hope to do some work locally.

I will take on short commitments. Let’s say no longer than two to three months. Anything goes really, but I am particularly interested in work related to creativity and learning. I am also keen on getting back into teaching.

So if you are in Singapore, work in technology or design and want to have a cup of coffee. Drop me a line.

Happy 2016!

A Playful Stance — my Game Design London 2008 talk

A while ago I was interviewed by Sam Warnaars. He’s researching people’s conference experiences; he asked me what my most favourite and least favourite conference of the past year was. I wish he’d asked me after my trip to Playful ’08, because it has been by far the best conference experience to date. Why? Because it was like Toby, Richard and the rest of the event’s producers had taken a peek inside my brain and came up with a program encompassing (almost) all my fascinations — games, interaction design, play, sociality, the web, products, physical interfaces, etc. Almost every speaker brought something interesting to the table. The audience was composed of people from many different backgrounds, and all seemed to, well, like each other. The venue was lovely and atmospheric (albeit a bit chilly). They had good tea. Drinks afterwards were tasty and fun, the tapas later on even more so. And the whiskey after that, well let’s just say I was glad to have a late flight the next day. Many thanks to my friends at Pixel-Lab for inviting me, and to Mr. Davies for the referral.

Below is a transcript plus slides of my contribution to the day. The slides are also on SlideShare. I have been told all talks have been recorded and will be published to the event’s Vimeo group.

Perhaps 1874 words is a bit too much for you? In that case, let me give you an executive summary of sorts:

  1. The role of design in rich forms of play, such as skateboarding, is facilitatory. Designers provide tools for people to play with.
  2. It is hard to predict what people will do exactly with your tools. This is OK. In fact it is best to leave room for unexpected uses.
  3. Underspecified, playful tools can be used for learning. People can use them to explore complex concepts on their own terms.

As always, I am interested in receiving constructive criticism, as well as good examples of the things I’ve discussed.

Continue reading A Playful Stance — my Game Design London 2008 talk

Playing with emergence is like gardening

It’s been a while since I finished reading Steven Berlin Johnson’s Emergence. I picked up the book because ever since I started thinking about what IxDs can learn from game design, the concept of emergence kept popping up.

Johnson’s book is a pleasant read, an easy-going introduction to the subject. I started and finished it over the course of a weekend. There were a few passages I marked as I went a long, and I’d like to quote them here and comment on them. In order, they are about:

  1. Principles that are required for emergence to happen
  2. How learning can be unconscious
  3. Unique skills of game players
  4. Gardening as a metaphor for using (and making) emergent systems

A cheat sheet

Let’s start with the principles.1

“If you’re building a system designed to learn from the ground level, a system where macrointelligence and adaptability derive from local knowledge, there are five fundamental principles you need to follow.”

These principles together form a useful crib sheet for designers working on social software, MMOGs, etc. I’ll summarise each of Johnson’s principles here.

“More is different.”

You need to have a sizeable amount of low-level elements interacting to get patterns emerging. Also, there is a difference between the behaviour you will observe on the microlevel, and on the macrolevel. You need to be aware of both.

“Ignorance is useful.”

The simple elements don’t have to be aware of the higher-level order. In fact, it’s best if they aren’t. Otherwise nasty feedback-loops might come into being.

“Encourage random encounters.”

You need chance happenings for the system to be able to learn and adapt.2

“Look for patterns in the signs.”

Simply put, the basic elements can have a simple vocabulary, but should be able to recognise patterns. So although you might be working with only one signal, things such as frequency and intensity should be used to make a range of meanings.

“Pay attention to your neighbours.”

There must be as much interaction between the components as possible. They should be made constantly aware of each other.

Now with these principles in mind look at systems that successfully leverage collective intelligence. Look at Flickr for instance. They are all present.

Chicken pox

I liked the following passage because it seems to offer a nice metaphor for what I think is the unique kind of learning that happens while playing. In a way, games and toys are like chicken pox.3

“[…] learning is not always contingent on consciousness. […] Most of us have developed immunity to the varicella-zoster virus—also known as chicken pox—based on our exposure to it early in childhood. The immunity is a learning process: the antibodies of our immune system learn to neutralize the antigens of the virus, and they remember those neutralization strategies for the rest of our lives. […] Those antibodies function as a “recognition system,” in Gerald Edelman’s phrase, successfully attacking the virus and storing the information about it, then recalling that information the next time the virus comes across the radar. […] the recognition unfolds purely on a cellular level: we are not aware of the varicella-zoster virus in any sense of the word, […] The body learns without consciousness, and so do cities, because learning is not just about being aware of information; it’s also about storing information and knowing where to find it. […] It’s about altering a system’s behaviour in response to those patterns in ways that make the system more successful at whatever goal it’s pursuing. The system need not be conscious to be capable of that kind of learning.”

Emphasis on the last sentence mine, by the way.

Patience

Johnson writes about his impression of children playing video games:4

“[…] they are more tolerant of being out of control, more tolerant of that exploratory phase where the rules don’t all make sense, and where few goals have been clearly defined.”

This attitude is very valuable in today’s increasingly complex world. It should be fostered and leveraged in areas besides gaming too, IMHO. This point was at the core of my Playing With Complexity talk.

Gardening

“Interacting with emergent software is already more like growing a garden than driving a car or reading a book.”5

Yet, we still tend to approach the design of systems like this from a tradition of making tools (cars) or media (books). I not only believe that the use of systems like this is like gardening, but also their creation. Perhaps they lie in each other’s extension, are part of one never-ending cycle? In any case, when designing complex systems, you need to work with it “live”. Plant some seeds, observe, prune, weed, plant some more, etc.

I am going to keep a garden (on my balcony). I’m pretty sure that will teach me more about interaction design than building cars or writing books.

  1. The following quotes are taken from pages 77-79. [↩]
  2. This reminds me of Nassim Nicholas Taleb’s The Black Swan, wherein he writes about maximising your chance of having serendipitous encounters. [↩]
  3. Taken from pages 103-104. [↩]
  4. Page 177. [↩]
  5. Page 207. [↩]

Notes on play, exploration, challenge and learning

(My reading notes are piling up so here’s an attempt to clear out at least a few of them.)

Part of the play experience of many digital games is figuring out how the damn thing works in the first place. In Rules of Play on page 210:

“[…] as the player plays with FLUID, interaction and observation reveals the underlying principles of the system. In this case the hidden information gradually revealed through play is the rules of the simulation itself. Part of the play of FLUID is the discovery of the game rules as information.”

(Sadly, I could not find a link to the game mentioned.)

I did not give Donald Norman all the credit he was due in my earlier post. He doesn’t have a blind spot for games. Quite the contrary. For instance, he explains how to make systems easier to learn and points to games in the process. On page 183 of The Design of Everyday Things:

“One important method of making systems easier to learn and to use is to make them explorable, to encourage the user to experiment and learn the possibilities through active exploration.”

The way to do this is through direct manipulation, writes Norman. He also reminds us that it’s not necessary to make any system explorable.1 But (on page 184):

“[…] if the job is critical, novel, or ill-specified, or if you do not yet know exactly what is to be done, then you need direct, first-person interaction.”

So much written after DOET seems to have added little to the conversation. I’m surprised how useful this classic still is.

I’m reminded of a section of Matt Jones’s Interaction 08 talk—which I watched yesterday. He went through a number of information visualisations and said he’d like to add more stuff like that into Dopplr, to allow people to play with their data. He even compared this act of play to Will Wright’s concept of possibility space.2 He also briefly mentioned that easily accessible tools for creating information visualisations might become a valuable tool for designers working with complex sets of data.

Norman actually points to games for inspiration, by the way. On page 184 just before the previous quote:

“Some computer systems offer direct manipulation, first-person interactions, good examples being the driving, flying, and sports games that are commonplace in arcades and on home machines. In these games, the feeling of direct control over the actions is an essential part of the task.”

And so on.

One of the most useful parts of Dan Saffer’s book on interaction design is where he explains the differences between customisation, personalisation, adaptation and hacking. He notes that an adaptive system can be designed to induce flow—balancing challenge with the skill of the user. In games, there is something called dynamic difficulty adjustment (DDA) which has very similar aims.

Salen and Zimmerman have their doubts about DDA though. In Rules of Play on page 223 they write:

“Playing a game becomes less like learning an expressive language and more like being the sole audience member for a participatory, improvisational performance, where the performers adjust their actions to how you interact with them. Are you then playing the game, or is it playing you?”

Perhaps, but it all depends on what DDA actually adjusts. The technique might be objectionable in a game (where a large part of the point is overcoming challenge) but in other systems many of these objections do not apply.

“With a successful adaptive design, the product fits the user’s life and environment as though it were custom made.”

(Designing for Interaction, page 162.)

Adaptive systems explicitly anticipate transformative play. They allow themselves to be changed through a person’s interactions with it.3

A characteristic of good interaction design is playfulness, writes Mr. Saffer in his book on page 67:

“Through serious play, we seek out new products, services and features and then try them to see how they work. How many times have you pushed a button just to see what it did?”

The funny thing is, the conditions for play according to Saffer are very similar to some of the basic guidelines Norman offers: Make users feel comfortable, reduce the chance for errors and if errors do occur, make sure the consequences are small—by allowing users to undo, for instance.

Mr. Norman writes that in games “designers deliberately flout the laws of understandability and usability” (p.205). Although even in games: “[the] rules [of usability] must be applied intelligently, for ease of use or difficulty of use” (p.208).

By now, it should be clear making interactions playful is very different from making them game-like.

  1. Apparently, “explorable” isn’t a proper English word, but if it’s good enough for Mr. Norman it’s good enough for me. [↩]
  2. I blogged about possibility space before here. [↩]
  3. Yes, I know I blogged about adaptive design before. Also about flow and adaptation, it seems. [↩]

Space to play

Tree by Pocketmonsterd on Flickr

The languages you’ve mastered shape your thinking. Nouns, verbs, adjectives…if you think of your day-to-day interactions on the web it’s clear the language you’re using is (very) limited. Does that limit your range of thoughts, and the things you’re able to express? Certainly, I’d say.

A quote from an old Ben Cerveny bio found in the Doors of Perception museum:

‘Cerveny is interested in harnessing the computational power of platforms like Playstation2 to create simulations with basic rule-sets that allow complexities to emerge, forming patterns of behaviour and interaction that people instinctively parse. He believes that this essential human ability to find patterns in complex systems remains untapped by current “click on the smiley face to buy our product” interfaces. “There is a certain algorithmic lightness to a basic ruleset, like that of the game Go,” he argues. “Especially as it replaces a top-down specification for human-computer interactions.”‘

That was in 2001. Game-like interactions have the potential for expanding your thinking. Stamen—where I’m told Cerveny is spending part of his time—is doing this with datasets.

Recently, I’ve been asked by several people to come up with concrete examples for my “playful” shtick. I’m worried that people expect stuff that makes a typical UI more playful. Like a sauce. That’s never been my intention.

The examples I’m considering (which I intend to describe as patterns) are of a more structural kind. When I point to emergent behaviour in games, I’m not kidding—the idea here is to allow for surprising results. Results that you as a designer have not foreseen. Space to play. That’s what sets the typical web interaction apart from something like Digg Labs.

“Play is free movement within a more rigid structure”. There is (almost) no free movement in your typical web app. That’s why I would not call it playful. These apps are designed to fit predefined user scenarios and evaluated based on how well they support them. No surprise they turn out boring in stead of fun.

However: Not every web app has to be playful, because not every web app is trying to teach you something.

In DOET Norman writes on p.124:

“What are not everyday activities? Those with wide and deep structures, the ones that require considerable conscious planning and thought, deliberate trial and error: trying first this approach, then that—backtracking. Unusual tasks include […] intellectual games: bridge, chess, poker, crossword puzzles, and so on.”1

So that’s why I believe much of the foundations of human-centered design are not applicable to playful experiences—the teachings of Norman are aimed at everyday activities. The activities that are not aimed at making you smarter, at giving you new insights.

On the web (and in computing in general) we’ve moved beyond utility. If we keep designing stuff using methods derived from Donald Norman’s2 (and other’s) work, we’ll never get to playful experiences.

  1. Norman has a blind spot for digital games, although he does include a NES as an example in his book. About this he admits he made “a few attempts to master the game” (p.138). [↩]
  2. I’ll be speaking at a conference that has Mr. Norman as keynote speaker. I mean no disrespect. [↩]

The experience of playful IAs

Solving a Rubik's Cube

It’s time for a short update on my thinking about Playful IAs (the topic of my Euro IA Summit talk). One of the under-served aspects so far is the actual user experience of an architecture that is playful.

Brian Sutton-Smith describes a model describing the ways in which games are experienced in his book Toys as Culture. I first came across this book in (not surprisingly) Rules of Play. He lists five aspects:

  1. Visual scanning
  2. Auditory discrimination
  3. Motor responses
  4. Concentration
  5. Perceptual patterns of learning

Of most importance to my subject is the 5th one.

Game design, like the design of emergent IAs is a 2nd order design problem. You can only shape the user’s experience indirectly. One of the most important sources of pleasure for the user is the way you offer feedback on the ways he or she has explored and discovered the information space.

Obviously, I’m not saying you should make the use of your service deliberately hard. However, what I am saying is that if you’re interested in offering a playful experience on the level of IA, then Sutton-Smith’s perceptual patterns of learning is the best suited experiential dimension.