Machine Learning and UI/UX Design in AI and Extended Reality (XR)

The UI/UX Design field is entering a phase where Artificial Intelligence, Machine Learning, and Intelligent Interfaces are no longer confined to research labs and are becoming part of real-world projects. This shift is the backdrop of a new book by researcher Pradipta Biswas, a former Gates Cambridge Scholar, who aims to clearly explain how the latest AI technologies are transforming interface and user experience design.

The book, published by Taylor & Francis, is called Intelligent User Interface: Usable Artificial Intelligence and Artificial Intelligence for Usability, and it has a very straightforward goal: to make the UI/UX process easier to understand and apply while presenting the latest advances in AI and Machine Learning models. Instead of focusing only on heavy theory, Biswas organizes the content around real-world use cases, showing how to design intelligent interfaces for XR systems, human-robot interaction, cockpits, and trajectory prediction systems.

The author’s focus is not just talking about technology for its own sake, but connecting AI, human factors, and usability in scenarios where an interface error can have serious consequences, such as aviation, space, autonomous mobility, and accessibility. As a result, the book positions itself as a bridge between cutting-edge research in human-computer interaction and the practical work of designers, engineers, and product managers who need to build solutions that work in the real world.

What Pradipta Biswas’s book brings that is new to UI/UX

Right from the start, Biswas introduces the core concept of the book: intelligent interfaces are those that use AI not only to automate tasks, but to enhance usability. In other words, technology is not just decoration; it becomes part of the actual process of making products easier to understand, operate, and learn.

The book explains, in clear language, the main AI and Machine Learning models currently used in advanced interfaces, with a special focus on:

• Computer vision models, including recent architectures such as vision transformers, used to interpret gestures, head movements, posture, and other signals captured by cameras and sensors;
• Models based on Large Language Models (LLMs), aimed at natural communication interfaces between humans and robots or digital systems, enabling language-based commands, contextual explanations, and real-time feedback;
• Virtual reality simulation systems, such as those used to train pilots and astronauts, combining sensor data, immersive visualization, and prediction algorithms to create more realistic and safer training scenarios.

Throughout the chapters, Biswas shows how these models can be used to improve the user experience, based on layout decisions, navigation flow, visual, auditory, and haptic feedback, as well as automations that reduce cognitive load in complex activities.

Trajectory prediction and safety in critical interfaces

One of the key themes in the book is trajectory prediction, an essential area for intelligent mobility and autonomous systems. The book explains that trajectory prediction is the process of estimating where an agent – such as a vehicle or a pedestrian – will be in the future, based on variables like speed, direction, surrounding context, and recent behavior.

This capability is crucial for:

• Autonomous driving, where the system needs to anticipate the movement of cars, motorcycles, cyclists, and pedestrians to avoid collisions;
• Air traffic management and cockpits, where pilots and automated systems must predict trajectories to maintain safe distance between aircraft;
• Industrial environments and robotics, where robots and humans share the same physical space.

What stands out is that Biswas does not treat trajectory prediction as an isolated mathematical algorithm. He discusses how to present these predictions in the interface so that humans can quickly understand what is happening: for example, signaling potential conflicts with simple visual elements, consistent colors, and gradual alerts, instead of overloading the screen with hard-to-interpret charts in high-pressure situations.

XR, augmented reality, and virtual reality in practice

Another central axis of the book is the use of XR (Extended Reality), which includes virtual reality (VR), augmented reality (AR), and mixed reality. Biswas details how XR systems depend on a fine-tuned combination of hardware, AI, and interaction design.

He explains that XR includes:

• VR headsets, used for full simulations of environments such as aircraft cockpits or spacecraft cabins;
• AR glasses and head-mounted displays, which overlay digital information on the physical environment, useful in maintenance, healthcare, training, and navigation;
• Mixed reality environments, which combine virtual and real elements with a high level of interaction.

The book discusses examples of intelligent interfaces in XR, where AI decides what to display, where to place elements in the field of view, and how to adapt the interface according to the situation. In a flight simulation, for example, the system can:

• reduce less important elements when it detects a risky situation, to avoid distracting the pilot;
• highlight critical trajectories with colors and subtle animations, guiding attention without causing panic;
• use multimodal feedback, combining sound, vibration, and visual elements to reinforce messages.

Biswas also describes the equipment needed to set up an intelligent interaction lab with XR, robots, and drones, going from sensors and cameras to open-source software that can be used in experiments. The book lists free tools for those who want to explore the concepts in practice, including packages for computer vision, VR simulation, gaze analysis, and rapid interface prototyping.

From the lab to the cockpit: Pradipta Biswas’s experience

Biswas’s authority on the subject does not come only from theory. He works as an associate professor in the Department of Design and Manufacturing at the Indian Institute of Science (IISc), and he is also an associate professor at the Robert Bosch Centre for Cyber-Physical Systems at the same institution. He is actively involved in international organizations that define standards for communication technologies and accessibility.

Among his prominent roles, he has been:

• Vice-chairman of the ITU Study Group 9, linked to the International Telecommunication Union (ITU);
• Co-chair of the Intersector Rapporteur Group on Audiovisual Media Accessibility (IRG AVA), a group focused on accessibility in audiovisual media;
• A participant in ITU focus groups on Smart TV and related technologies.

Biswas’s academic journey includes Cambridge, where he completed a PhD in Computer Science. There, he researched:

• Visual and auditory perception in human-machine interaction contexts;
• Fast eye movements, relevant for precise eye- or gesture-based controls;
• Problem-solving strategies when humans interact with complex systems.

During this period, he developed new algorithms for eye gaze tracking technology, including solutions that were patented. One example is an interactive Head-Up Display (HUD) controlled by eye gaze and gestures, allowing users to navigate and trigger functions without relying on traditional manual commands.

Projects in aviation, space, and accessibility

After returning to India, Biswas kept expanding the use of eye tracking and intelligent interfaces in highly complex projects. One of the highlights mentioned in the original article is his work with the Indian Air Force, where he helped apply eye-tracking technology in aviation scenarios, combining human factors, safety, and operational performance.

Another important project is the design of a virtual reality cockpit for India’s first crewed spaceflight mission. In this context, VR is used to simulate real flight conditions and operations in space, allowing teams to test layouts, alerts, and interaction flows before taking any solution to physical hardware.

Biswas was also one of five researchers selected in India to conduct studies on human-machine interaction on the International Space Station (ISS) during the Axiom 4 mission. These studies help understand how interfaces and autonomous systems can support astronauts in critical tasks, in an environment where ergonomics, fatigue, and safety are even more sensitive.

Beyond the aerospace sector, the book and Biswas’s work address applications in accessibility. He led, for example, a pioneering toy hackathon focused on adapting toys so that children with severe disabilities could communicate using eye-controlled interfaces. This kind of initiative shows how the same technological foundation used in cockpits and space missions can be reapplied to improve the quality of life of people with motor impairments.

Technical content: AI, human factors, and usability evaluation

The book goes beyond listing technologies. It covers a broad range of topics that combine technology and human factors, including:

Tools we use daily

Translation

Text Inspection & Clipping

Social Media

Search & Research

Recruitment

Programming

Productivity & Organization

Operations

Marketing & Sales

Finance & Investment

Education

Design & Media

Data & Analytics

Content & Writing

Automation

• Human factors and ergonomics, focusing on cognitive load, physical comfort, reaction time, and common user errors;
• Computer vision applied to gesture control, posture recognition, and user attention detection;
• AR/VR systems and their specific design challenges, such as field of view, motion sickness, depth, and alignment of virtual elements with the real world;
• Large Language Models (LLMs) as the foundation for conversational interfaces in human-robot interaction and complex systems;
• Usability evaluation techniques, from classic methods to approaches that use AI to analyze logs, usage videos, and sensor data.

A recurring point in the book is the idea that AI models must be treated as part of the interface. This means designing not only the visual screen, but also how the system explains decisions, how it collects data, how it lets users review suggestions, or turn off personalization features. In particular, Biswas discusses:

• the importance of explainability in interfaces that use AI to make sensitive decisions;
• risks of bias in Machine Learning models when there is not enough diversity in the data;
• the need to include accessibility and inclusion from the very beginning of the UI/UX project, instead of treating them as optional steps.

Educational resources and target audience

To make the content more digestible, Biswas’s book offers a series of resources tailored to readers who want to learn quickly and apply concepts in real projects:

• Graphic illustrations that help visualize interaction concepts, interface flows, smart system architectures, and XR use cases;
• Quick facts lists in each chapter, working as short summaries to review main concepts;
• Project ideas in intelligent interfaces, especially aimed at undergraduate and graduate students and early-career researchers;
• Lists of free software related to each topic, including tools for simulation, data analysis, UI prototyping, and AI experiments.

The primary target audience includes:

• Engineering and design students and professors interested in experimenting with AI and Machine Learning applied to UI/UX;
• Interface and user experience designers who want to understand the possibilities of intelligent interfaces without diving too deep into advanced math;
• Product managers who need to make informed decisions about when and how to include AI in new products or features.

The goal is to offer material that explains recent advances in AI in a practical way, without requiring readers to become experts in model theory, but with enough technical grounding to collaborate with engineering teams and make more solid design decisions.

AI, LLMs, and the future of intelligent interfaces

In the most up-to-date section of the book, Biswas focuses on new-generation AI systems such as vision transformers and LLM-based human-robot interfaces. He shows how these models make it possible to build interfaces that:

• understand natural language commands with more context and nuance;
• combine language, vision, and sensor data to better interpret what users are trying to do;
• support complex VR simulations, such as those used in space training.

The author also discusses how these models can be used to evaluate and improve usability, for instance by analyzing interactions at scale, identifying error patterns, and suggesting simplified interface flows. At the same time, he emphasizes that potential and risk go hand in hand: intelligent interfaces must be carefully designed to maintain transparency, avoid negative surprises, and preserve the user’s ability to question and correct the system.

In the end, Pradipta Biswas’s work positions itself as a complete guide for anyone who wants to understand how AI, human factors, and XR intersect in modern UI/UX processes. Instead of treating Artificial Intelligence as a futuristic extra, the book shows how it is already at the core of design decisions in areas such as digital cockpits, human-robot interaction, trajectory prediction, and accessibility, offering a solid and up-to-date overview for those who build, research, or manage technology products built for people.