Maths for Pros: Matchday Challenge

Image 1: Prototype interface for Maths for Pros: Matchday Challenge, combining football-themed maths tasks, streak tracking, and rewards.

Maths for Pros: Matchday Challenge is a maths-based learning game designed for Thomas, an 11-year-old who has recently started secondary school. Thomas often finds traditional maths homework unengaging, but he enjoys football, social interaction, and games. He is also a committed Newcastle United fan. The game aims to reframe maths practice as something motivating and achievable by linking learning to his interests and by reducing the pressure often associated with homework.

Stage 1: Initial concept development (raw idea)

Figure 1: Original in-class sketch showing the early concept for Maths for Pros.

The original concept for the game was developed collaboratively in class and is shown in Figure 2. At this stage, the focus was on motivation rather than structure. The group idea centred on short maths challenges presented as “Maths for Pros,” with rewards linked to football outcomes such as unlocking players, building streaks, and working towards a Newcastle United match ticket.

This early design already reflected several strong ideas. Daily challenges helped reduce pressure, while streaks encouraged regular effort rather than one-off work. Football-themed rewards made maths feel more positive and achievement-focused. However, the early concept was still confusing. The rules were unclear, rewards lacked structure, and learning progression had not been fully developed.

Stage 2: Improving the design using AI and flow theory

To improve the design, I used AI to help review the game using flow theory, which suggests that learners are most engaged when goals are clear, feedback is immediate, and the level of challenge matches their ability (Csikszentmihalyi, 1990). I prompted AI to review the original concept and suggest ways to simplify the rules while maintaining motivation.

Image 2: Initial AI-Generated Image

First, I shared the original in-class sketch of the game concept and asked AI to generate a more polished version of the game interface. This helped visualise how the game might look in practice and clarified the overall structure. However, the initial AI-generated version was not entirely suitable for an 11-year-old learner. In particular, some of the maths questions were too advanced or did not align well with Thomas’s age and learning level.

As a result, I had to take a more active role by refining my prompts. I asked AI to simplify the maths content, adjust the difficulty to be slightly challenging but appropriate for an 11-year-old, and ensure that all answers were correct. This was an ongoing process and showed that AI does not produce effective educational designs on its own. I improved the final version by revising my prompts several times and making the final decisions about difficulty, accuracy, and presentation myself. This stage demonstrated how AI can support design refinement, but only when combined with human judgement and subject knowledge.

Stage 3: Integrating AI and looking 10 years ahead

AI plays a supportive role in the game rather than controlling the learning process. In the current design, AI analyses patterns in Thomas’s responses and adjusts the difficulty of questions to keep them challenging yet achievable. It uses Newcastle United examples, like match scores or league tables, to make maths more relevant.

Figure 2: AI-supported feedback loop showing how learner performance informs adaptive support.

In ten years, this game could become more immersive and social. AI could act as a virtual coach by giving encouragement, showing progress, and helping Thomas reflect on his learning. The game could also allow him to play with friends, which would help keep him motivated while still supporting his own learning. Notably, AI would support decision-making rather than replace it, keeping learning focused on the learner (Luckin et al., 2016).

Reflection

Overall, Maths for Pros: Matchday Challenge demonstrates how a maths game can be designed around a learner’s interests without reducing academic challenge. By using flow theory and AI to refine the original idea, the game moves beyond simple rewards and instead supports ongoing engagement, confidence, and skill development. This approach shows how AI and game design can be used thoughtfully to support learning while maintaining learner agency and enjoyment.

Reference List

Csikszentmihalyi, M (1990) Flow: The Psychology of Optimal Experience. New York: Harper & Row.

Gee, JP (2007) What Video Games Have to Teach Us About Learning and Literacy. New York: Palgrave Macmillan.

Luckin, R, Holmes, W, Griffiths, M and Forcier, LB (2016) Intelligence Unleashed: An Argument for AI in Education. London: Pearson.

Prensky, M (2001) Digital Game-Based Learning. New York: McGraw-Hill.

AI Acknowledgment Statement

AI tools were used to support the design and writing process for this blog, in line with academic integrity guidelines. I began by using the tutor-suggested prompt: “Act as an expert in game design. We have been tasked with creating a maths game for an 11-year-old learner who finds traditional homework boring. Evaluate the design using flow theory and recommend improvements.” This prompt helped me reflect on the initial game concept developed in class and consider how to improve it.

Throughout the blog's development, I used additional prompts to refine my thinking and support the design process. These included prompts focused on simplifying game rules, improving alignment with flow theory, adapting maths difficulty to an appropriate level for an 11-year-old learner, and considering how AI could support personalisation and feedback in the future. I also used prompts to generate and refine images and diagrams based on my own design ideas, and to improve the clarity and structure of the blog post.

My peers and I came up with the idea together. All design decisions, evaluations, and reflections are my own. I selected which suggestions to use, critically revised the outputs, and made final decisions about content, accuracy, and presentation. The final blog represents my independent learning, design thinking, and academic judgment.