Category: Interaction Design

In the “before” scenario, Game UI is often static, cluttered, and not completely accessible. Design decisions are frequently made without a standardized framework, resulting in interfaces that annoy or confuse players of differing experience levels. These “standard” UIs look “bad” in the sense that they fail to meet the functional needs of everyone, leading to a disconnected and frustrating user experience.

In contrast, the “after” scenario highlights a shift toward a more scientific and flexible approach. By adapting and checking designs against WCAG (Web Content Accessibility Guidelines), the UI becomes a tool for education as well as interaction. The impact is a “Modular and Flexible” UI that uses minimal requirement guidelines to reduce clutter. This shift ensures the player is no longer fighting the interface but is instead empowered by a system that adapts to their specific level of experience and physical needs.

At the center of the system is the Game UI Modular Builder. This is the core tool or framework designed to bridge the gap between static design and player needs. Surrounding this center are the primary creators—UI/UX Designers and Game Designers—who directly build and implement the interface elements. The next layer includes the active users: Gamers, Students, and Visually Impaired Players, whose diverse needs and feedback loops drive the modularity of the system. On the outermost edge, the Game Company acts as the broader stakeholder, providing the professional and institutional context that allows the system to exist.

The map highlights the “stitched” connections between these groups, showing that a modular builder is not just a piece of software, but a meeting point for professional design and lived user experience. It reveals that accessible UI is a collaborative ecosystem where the designer’s tools must directly respond to the player’s specific barriers.

After starting with a giant question mark about the role played by AI nowadays in the field of data visualization, I decided to start with a literature review to narrow down and frame better on which topic/s to focus. So I have decided to investigate better how LLMs and generative models intervene in data driven storytelling (meant as turning data into easy-to-read and easy-to-understand stories that help turning insights into action).

A systematic review regarding telling stories with data deeply guided my curiosity and focus on the topic of human intentionality. In addition, many papers were also questioning the modern role of the author in the creative process. I want to understand better where the author exactly stands today and how empathy and human intention, that are not entirely replicable by machines, fit in a world dominated by algorithmic storytelling. Therefore, I am exploring the co construction of meaning to observe how humans and algorithms might merge to build these new narratives.

As my literature review expanded, several critical points have emerged. I’d like to explore how algorithm suggestions might perpetuate a structural bias compared to a possible unintentionally “human manipulation”. It is also crucial to question whether relying on automated micro narratives is always the right choice when considering a diverse user base. To conclude (as if this was not already enough qeustions) I plan to explore the sociological aspect of the epistemic control, understanding to whom may it belong.

But for now, let’s keep the focus on just the creative process adopted to create a narrative starting from data and see how the data community behave when involving AI!