UI/UX design and Artificial Intelligence have never been this close — and a new book just proved it
Pradipta Biswas, researcher and associate professor at the Indian Institute of Science, and a former Gates Cambridge scholar, has just released a book through Taylor & Francis that promises to reshape how designers, engineers, and students approach the development of intelligent interfaces. The book is called Intelligent User Interface: Usable Artificial Intelligence and Artificial Intelligence for Usability, and its core goal is not to overcomplicate a topic that already feels dense enough. Quite the opposite — the idea is pretty straightforward: bring together the latest advances in AI and Machine Learning in a way that any professional in the field can absorb and apply on a daily basis, without needing to become a data scientist in the process.
Biswas is no stranger to the academic tech world. He earned his PhD in Computer Science at Cambridge, where he studied visual and auditory perception, rapid aiming movements, and problem-solving strategies within the context of human-machine interaction. During that time, he invented new algorithms — including applications focused on eye-tracking technology — and patented technologies such as a gaze-and-gesture-controlled Head Up Display. In other words, we are talking about someone who has been moving comfortably between theory and practice for nearly two decades.
Currently, in addition to serving as associate professor in the Department of Design and Manufacturing at the Indian Institute of Science, he is also an associate faculty member at the Robert Bosch Centre for Cyber Physical Systems. On the international stage, he was elected vice chair of ITU Study Group 9 and served as co-chair of IRG AVA, the intersectoral group on accessibility in audiovisual media, as well as the Focus Group on Smart TV at the International Telecommunication Union. That kind of resume helps explain how the book manages to cover so much ground without losing its thread.
What stands out right away is the framing he chose. Instead of focusing solely on theory or advanced algorithms that few people will ever implement day to day, Biswas connects the concepts directly to the decisions designers and engineers make when building digital products. That puts the book in a pretty unique position within the tech publishing landscape, because it speaks simultaneously to those who think about aesthetics and user experience and to those on the technical side, architecting complex systems. It is rare to find a publication that moves between these two worlds so fluidly.
Among the topics covered are Human-Computer Interaction, Augmented Reality and Virtual Reality systems, Large Language Models applied to robotics, computer vision, human factors, usability evaluation techniques, and a concept that has been gaining more and more attention in the autonomous vehicle space: trajectory prediction. 🚗🤖 If you work in tech and want to understand how AI is reshaping the user experience, this release deserves a spot on your radar.
What Human-Computer Interaction has to do with all of this
Human-Computer Interaction, commonly known as HCI, is the field of study that investigates how people interact with digital systems — and why those interactions succeed or fail. For decades, HCI was confined to usability labs and testing with small groups of users in a slow, expensive process. But with the arrival of Artificial Intelligence, that landscape changed dramatically. Today it is possible to collect behavioral data in real time, identify usage patterns almost instantly, and adapt interfaces dynamically, without needing a full round of testing for every single adjustment. Biswas explores exactly this transformation in the book, showing how AI has moved beyond being just a back-end tool to become an active component in the user experience layer.
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What makes this discussion even more relevant is that HCI has never been a siloed subject. It has direct connections to cognitive psychology, product design, accessibility, and now, increasingly, to machine learning systems. When an algorithm can predict a user’s next step within an app or identify a frustration point before they even leave the screen, we are looking at a new generation of interfaces that do not just react — they anticipate. That completely changes the logic of traditional UI/UX design, which has historically relied on fixed heuristics and established patterns.
Biswas also discusses how large-scale language models, known as Large Language Models, are starting to show up in robotics and automation contexts, expanding the scope of HCI even further. When a robot can interpret commands in natural language and deliver contextualized responses, the interface between human and machine stops being a control panel and becomes a conversation. That is a massive conceptual leap, and the book provides a solid foundation for understanding how designers and engineers can work together in this new territory. The book even features case studies on intelligent interfaces for human-robot interaction using LLMs, which makes the discussion far more concrete than usual. 🤝
XR systems and the new stage for UI/UX design
XR systems — encompassing Virtual Reality, Augmented Reality, and Mixed Reality — are one of the most exciting topics in the book and also one of the most challenging from a UI/UX design standpoint. For those unfamiliar with the term, XR systems are tools, platforms, and digital technologies that allow users to experience and interact with virtual, augmented, and mixed environments through advanced hardware like headsets and smart glasses. Unlike traditional interfaces that exist within a screen with defined edges, these technologies project digital elements onto the physical world or create entirely new environments, which generates a completely new set of design problems.
Where do you place a button when there is no screen? How do you ensure readability when the background is constantly changing? How does the user know what is interactive and what is just decorative in a three-dimensional environment? These questions do not have simple answers, and that is precisely why the intersection of XR and Artificial Intelligence has become one of the most active research areas in HCI in recent years.
AI enters this equation in very tangible ways. Computer vision algorithms — including the so-called vision transformers, which Biswas covers in the book — allow Augmented Reality systems to recognize objects, surfaces, and people in the physical environment, adjusting digital elements according to real-time context. This means the interface adapts not only to the user’s behavior but also to the physical space they are in. An AR application in an industrial warehouse will behave differently from the same application in a compact office, and AI is what makes that adaptation possible without requiring the designer to create separate versions for each scenario.
Another important point the book addresses is the issue of accessibility in XR environments. When you design an interface for a conventional screen, there are well-established guidelines — contrast, font size, spacing between elements. In Augmented or Mixed Reality, those guidelines need to be rethought from scratch, taking into account variables like ambient lighting, the user’s distance from the digital object, and even head movement during the interaction. Artificial Intelligence plays a critical role here because it is what can monitor these variables in real time and make the necessary adjustments so the experience is comfortable and accessible to the widest possible audience. The book even discusses the latest standards and guidelines relevant to UI/UX layout and design, as well as detailing the equipment needed to set up a lab focused on intelligent interaction design involving robots, drones, and XR systems. 🎯
Trajectory prediction: when AI anticipates the move
The concept of trajectory prediction is perhaps one of the most fascinating points in the entire work. The core idea is relatively easy to understand but complex to implement: using Artificial Intelligence algorithms to predict the future positions of agents — such as vehicles or pedestrians — over time, based on historical data and real-time information. In the context of autonomous vehicles, this is absolutely crucial. The system needs to anticipate the path of a pedestrian, another car, or even an unexpected obstacle to ensure safe navigation. This ability to anticipate is what sets a truly intelligent system apart from one that merely reacts to what has already happened.
But trajectory prediction goes far beyond self-driving cars. Biswas presents applications of this concept in Augmented Reality systems designed for industrial training, where AI needs to predict an operator’s movement to overlay visual instructions at the right time and in the right place. There are also examples involving drone control interfaces, virtual reality spacecraft simulation systems, and even monitoring scenarios. The common thread across all these cases is the need for the interface to stay ahead of the user, reducing cognitive effort and making the interaction smoother and more natural.
When implemented well, trajectory prediction transforms an interface from something the user needs to actively operate into something that feels like an extension of their own thought process. And that raises some really interesting design questions. If the interface anticipates the user’s movement, at what point is it helping, and when does it start becoming intrusive? How do you ensure the system is transparent enough that the user understands what is happening without needing a manual? Biswas does not shy away from these questions. On the contrary, he places them at the center of the discussion on intelligent Human-Computer Interaction, advocating for design that clearly communicates its intentions and always keeps final control in the hands of the user, even when the system already knows what is going to happen. This balance between system autonomy and user agency is one of the great challenges for the next generation of digital interfaces. 🧠
From academic research to outer space
One aspect that makes the book especially interesting is the fact that Biswas is not just a theorist. Since returning to India after his PhD at Cambridge, he has built an impressive track record on the practical side. He expanded his work with eye-tracking technology in partnership with the Indian Air Force, taking his research into real-world, high-complexity scenarios. He also led a project to develop a virtual reality cockpit for India’s first crewed space mission — work that directly connects several of the concepts covered in the book, from cockpit design to VR simulation.
More recently, Biswas was one of five researchers in India selected to conduct research on human-machine interaction aboard the International Space Station during the Axiom 4 mission. This kind of hands-on experience feeds directly into the book’s content, which features concrete case studies rather than limiting itself to purely theoretical discussions.
Another notable initiative was organizing the first-of-its-kind toy hackathon, aimed at helping children with severe disabilities communicate through gaze-controlled interfaces. This accessibility thread runs throughout the entire book and reinforces an important message: intelligent interfaces are not just about efficiency and technical performance — they are also about inclusion. When eye-tracking technology enables a child who cannot speak to communicate with the world around them, we are talking about a human impact that goes far beyond usability metrics.
Who this book is for and why it matters right now
The stated target audience includes engineering and design students and professors, interface designers, and product managers who want to understand the latest developments in AI and Machine Learning without diving into excessive theoretical detail, using that knowledge for their projects or product development. But in practice, any tech professional who deals with user experience will find value here.
The book features graphic illustrations and quick fact lists to make reviewing and memorizing the core concepts in each chapter easier. It also includes ideas for new intelligent interface projects that can be explored by students and early-career researchers, which makes it not just a reading reference but also a working tool. On top of that, it offers a list of free software available for download related to the topics covered — a practical perk that few academic publications provide.
Pradipta Biswas’s book arrives at a moment when Artificial Intelligence has shifted from being a differentiator to being an expectation. Users already expect apps to learn from their habits, interfaces to adapt to context, and digital systems to be proactive rather than passive. For UI/UX design professionals, that means mastering the fundamentals of AI is no longer optional. Understanding how machine learning algorithms influence design decisions is just as essential today as knowing how to use a prototyping tool. And that is exactly the bridge between these two worlds that this book builds with skill and clarity, making it an important reference for anyone who wants to stay relevant in a market that is evolving way too fast to sit around and wait. 🚀
