From HAK.io to Imperfect Games: Finding Structure in Speculative Play #blogtakeover
Ben Axnick, Natasha Baldock, Alan Chen, Mengni Li, Jade Rui
Hi, we are Ben, Natasha, Alan, Jade, and Monnie. We are students in the Master of Teaching (Early Childhood, Primary and Secondary). We spent the last two weeks in SWISP Lab to explore the collision of arts and science. From this blog, you will know about what we did in the last two weeks.
Caption: team lunch SWISP and JACE at the end of WIL
Ben
My first week in the SWISP Living Lab opened new possibilities in educational design. Walking through the doors of Science Gallery into a new creative world, I found myself immediately immersed in HAK.io – a game kit about hacking the Anthropocene that works very differently from the carefully scaffolded and sequenced frameworks that I typically prefer to use in learning environments.
Caption: The HAK.io climate comedy method challenges the reader to engage through humour.
HAK.io offered compelling creative provocations through its method cards – collage it, construct flash stories with emoji, make resistance badges. When our game team drew “climate comedy” at random, my heart sank slightly. Comedy isn’t my strength, but I was determined to embrace the challenge. Over the next hour, with help from the team and an AI assistant, I crafted a John Oliver-style piece about climate hypocrisy amid a creative ala carte from the rest of the team.
Caption: An excerpt of Ben’s climate comedy piece produced during HAK.io.
Creating this piece showed me how HAK.io functions as what the team at SWISP Lab calls “transmediation” – the activity doesn’t directly instruct but facilitates discussion and thought with generative output. I was beginning to come around to the idea that younger students could navigate these open-ended possibilities too, with a mediator to bring them along on the journey.
This experience sparked a week of game iterations. I ambitiously ideated a climate superhero kit for years 3-6 – complete with scientific breakthrough cards, QR codes linking to resources, and prompts for designing superpowers that could reverse tipping points. Students would become climate heroes, given challenges they could resolve through discussion, creation, and engagement with technologies for interacting with Earth’s climate at scale.
But something felt off. The superhero framework quickly became unwieldy – multiple card types, open-ended prompts with unclear mechanics. In the hands of an experienced facilitator it could be a meaningful and generative experience, but I was building a something that still felt too “direct” and lacking engagement with systems thinking that I consider a vital element to meaningful and enduring change. Could I mediate through engagement with a more concrete exploration of a system?
The breakthrough came through developing what I now call an “imperfect game” – a fishing simulation inspired by the 1992 Atlantic cod fishery collapse. Using standard playing cards, players select catch amounts secretly each round. The fish population grows or shrinks based on collective choices. Victory is individual but collective greed leads to collapse. I crafted a scoring system intended to produce a fine tension between individual and collective outcomes. However, during playtesting with five participants, the game revealed its imperfection. Play was highly individualistic without the negotiation I’d hoped for. Players were largely locked in to their original decisions, unable to respond to new information – the game-theoretic winning move was to go all-in and never change. However, this playtesting “failure” became the pedagogical moment I’d been seeking.
Caption: Playtesting the overfishing game. Each player has revealed their secretly chosen harvest count.
Sarah Healy’s response reframed everything: what if the educational value comes from students identifying what’s missing and proposing fixes? The imperfect game becomes a springboard where students might add realistic events like mass-extinctions, foreign fishing trawlers, government interventions, or technological elements. Groups could propose systems, playtest each other’s modifications, give feedback and merge them back into coherent games with the teacher facilitating the delivery of meaningful feedback and collaboration.
This process of imagining and reimagining uses the design of the game itself as a mediating tool. As students engage, they concretely picture abstract demands of interacting systems while framing possible alterations. It encourages real-world discovery of historical events, analysis of what occurred, and speculation about alternatives. Physical cards anchor digital exploration of the cod collapse, scientific papers, and recovery efforts.
My experience with SWISP reinforced how speculative pedagogy means designing for emergence rather than predetermined outcomes. Sometimes the most structured approach is creating conditions where authentic learning unfolds through productive failure. Educational games don’t need perfect balance – they can be deliberately broken systems that generate motivation for students to ask the right questions.
Natasha & Alan
Over the past two weeks, we completed a work integrate learning experience at SWISP Lab. One aspect of our WIL experience was designing pedagogy-based games. The aim of this mini project was to create engaging and meaningful learning experiences around our learning area themes with the possibility to relate them to climate change.
For my Science Learning Area I originally created a chatterbox activity that was inspired by the common question “why are we learning this?” or “when am I going to use this?”. The chatterbox has the four main science domains outlined by the curriculum; biology, chemistry, physics, and earth and space. Under each category students were to fill how they use concepts from the science domains in their daily life. While still an important activity around reflection and the importance of science education, this activity was limited to an individual activity and did not invite peer collaboration.
Instead I was inspired by Genius Games, a company who create concept focused games around science and math topics, to create a climate change game. Using individual concepts from each of the four domains, students draw 2 concept cards to pose a solution or research question to a global challenge card.
For example, using ‘enzymes‘ that breakdown ‘polymers‘ to help fight ‘plastic pollution in waterways‘. This game can be further adapted to include definitions or examples of concepts for those with limited science understanding or further expanded to include research current uses of concepts or current solutions for global challenges. Instead of memorising facts, players experimented with different solutions and saw the impact of their choices play out within the game. This could lead to further speculation activities or inspire students how to use their science knowledge in their future.
Caption: The ‘plastic pollution’ example and card layout, including initial chatterbox creation.
The business game was a hack of Monopoly. This was the Lunar New Year edition of Monopoly which had shops instead of properties. By changing the Fortune Stick (Chance) and Pinwheel (Community Chest) to Event Cards, players would have to answer business related questions to win or lose money, or environmental impacts would affect the shops’ abilities to be purchased or charge visitors. This directly linked environmental challenges and the effects on businesses and the overly theme of ‘scarcity’.
Caption: The modified ‘Event’ cards and board layout with shops instead of properties.
This internship showed me how to create pedagogy-based games that can make complex issues like climate change accessible and engaging to students in a variety of Learning Areas.
Mengni
Jumping Letters: Connecting Data, Stories, and Childhood Learning
During my placement at SWISP Lab, I had one of those moments that really changed how I see the world. I realised that data isn’t just numbers—it can tell stories and carry emotions. In the lab, we started with student texts and zines, coded them, analysed emotions, and then used AI tools to turn that data into stories and artworks. That experience showed me that data can reflect different voices and be reimagined in creative ways.
This inspired me to design an activity I call Jumping Letters. In this game, each child holds a letter card and stands around the classroom. When I show a picture or a word—like “CAT”—the children have to move around and come together to spell it. It’s a fun way to get them thinking about letter placement, teamwork, and spatial awareness. Once they form the word, we can ask questions like, “Is CAT an animal?” or “Which letters are the same colour?” So, it mixes movement, literacy, and collaboration, making learning active and engaging.
Caption: Jumping letter examples created by Mengni
Caption: the piano art piece for jumping letters
What I really like about Jumping Letters is that it mirrors what I learned at SWISP. In the lab, we were coding messy data and looking for patterns. In the game, children are doing something similar—spotting patterns, making connections, and telling stories with words and actions. In both research and teaching, I’ve noticed the same challenge: how to balance structure and flexibility. Whether it’s coding rules in the lab or movement rules in class, I’ve learned that you need some structure, but also space for creativity and exploration.
Looking back, I’ve realised that both research and teaching aren’t just about getting the “right” result. They’re really about participation, interaction, and storytelling. Whether it’s data analysis or children’s learning, bringing in creativity and emotion makes the process—and the outcome—so much more meaningful. And that’s something I’ll carry with me into my future work, both as a researcher and a teacher.
Caption: The Letter Card created for conducting activities
I tried to design such letter cards, which have both pictures and letters. Children can also join in the creation
Data Diving (Everyone)
Over the past two weeks, we participated in an internship with SWISP LAB, where we were deeply involved in the lab’s data analysis. We learned new research approaches, transcending traditional definitions and notions of data and research. We redefined the practical and methodological approaches to data analysis for non-quantitative data.
First, we digitised SWISP LAB’s sample data, which included text stories about climate change and student-designed zines from Bangalore. We imported the data into a Word document using an iPad and used a coding tool to encode all the content and place it in a spreadsheet.
Caption: stickers used in designing zine
SWISP LAB has a basic encoding method for these texts, using colored stickers to categorize text content, such as whether it’s about digital, global, or climate issues, and whether it contains emotions like sadness or happiness. We also collected these hashtags as part of the data.
Second, we conducted additional categorisation and analysis on this data, using two different emotional models to help understand the emotions present in the texts. Ekman’s Six (later Seven) Basic Emotions and Plutchik’s Wheel of Emotions were selected and used to train ChatGPT and Copilot to help understand the definitions of various emotion terms in these emotion models. We then fed the AI with individual examples to help it better understand these terms. Finally, we analysed these texts using Copilot, achieving impressive results. We found that despite using different emotion models, the results were similar across broad categories, indirectly validating the reliability of the data and the underlying meaning.
Caption: A visualisation of the emotions within Ben’s climate comedy piece using the Plutchik colour wheel.
Caption: use Ekman’s emotion model to decoding the climate story
Finally, we used AI to innovatively design feedback reviews for these texts and, using AI, combined emotions and text to create artwork representing the data.
Caption: Copilot generated this image to show its understanding of the climate change story and the emotion embedded in it.
During the internship, we also faced several challenges. First, the data sources were diverse and unstructured, including both textual narratives and student-designed visual works, which made it difficult to organise the analysis systematically. Second, the timeline was tight, requiring us to complete the encoding, emotion modelling, and result presentation within a limited period. Despite these difficulties, the challenges pushed us to strengthen our skills in data cleaning and rapid analysis. The most valuable gain was learning how to transform the theoretical knowledge from the classroom into practical, hands-on approaches. This not only enhanced our data thinking but also improved our communication and teamwork abilities. Most importantly, it helped us realise that data is not just about numbers—it can also serve as a storytelling tool, capable of conveying complex research outcomes in a more engaging and meaningful way.
Caption: Alan’s coloured wheel based on pluchik’s emotion wheel, he talks about how balance of fear and trust gives him a motivation to prrogress.
Overall, this internship deepened my understanding of data analysis in a real-world environment and gave me clearer insights into my future career direction. I hope to carry these experiences into my upcoming studies and work, continuing to explore the value hidden behind data.