We are sparking new research that will deepen our understanding of learning and fundamentally change how humans and machines learn for the future. Currently, we are focusing on the following 3 themes:

Human-AI Interaction &
Human-Centred Design

Human-AI Interaction is an emerging field concerning how humans experience and interact with AI-infused systems.
Human-Centred Design is an approach to the development of interactive systems, aiming to make systems usable and useful by focusing on the users, their needs and requirements.
In the Learning Lab, we explore the techniques and principles of designing explainable and transparent AI-infused systems, towards tackling the challenges of inclusiveness and trust, such that humans and machines can effectively work with each other.

Gamification &
Cognitive Computing

Gamification is the use of game-design elements, mechanics and game principles in non-game contexts such as learning systems, to drive user engagement, loyalty and motivate desired actions and tasks.
Cognitive computing uses computerised models to simulate the human thought process in complex situations. It is based on self-learning systems to perform specific, human-like tasks.
In the Learning Lab, we use gamification techniques built upon cognitive computing, to implement learning systems for users to have effective and immersive learning experiences.

Behavioural Analytics &
User Modelling

Behavioural Analytics is the collection, analysis and reporting of data about users and their contexts, aiming to understand how the users behave and optimise the contexts where their behavioural changes occur.
User Modelling builds up and modifies a conceptual understanding of the user, in order to personalise and adapt the systems to the user's specific needs.
In the Learning Lab, we use statistical modelling and machine learning to analyse, recognise, predict users' behaviour, in order to develop personalised and adaptive learning systems.

Current Projects

Visual Attention Modelling for High-Level Immersion

In this project, we aim to enable the head-mounted display based virtual museum (HMD-based VM) to prepare in advance natural responses by predicting the user’s visual attention using their current eye movement with respect to the exhibits in the VM.

Object tracking to improve weightlifting technique

In this project, we use computer vision to track and examine the trajectories of weightlifters in order to help them improve their form and technique, with the goal of improving how efficiently they can carry out lifts with heavier weights whilst also avoiding injury.

Smart auto re-colouring tool for colour blindness suffers

In this project, we aim to develop an aid tool for suffers from colour blindness which is integrated with their current workflow and existing technology. It should be intuitive and affordable, allowing sufferers to overcome barriers to education and technology access.

Developing an intuitive, educational photo editing resource

In this project, we aim to produce an iOS-based app that teaches people how to edit photos effectively and provide an intuitive interface to practice applying these edits. The app is developed in Swift and utilises the SwiftUI framework and CIKernel language.

Joining the Learning Lab

We are looking for enthusiastic and talented people!

Open Projects

Co-Decision-Making between Humans and Machines

How can a small group of humans and machines collaborate in decision making? What mechanisms and frameworks are needed for humans and machines to coordinate their activities and efficiently make decisions? Is it possible to scale up these methods thus supporting massive activities? In this project, we will address these questions by exploring new forms of human-machine interaction and collaboration.

Designing Emotional Intervention Mechanisms Towards Ensuring Affective and Cognitive Quality

The ultimate goal of this research is to explore and implement emotional intervention mechanisms for ensuring affective and cognitive quality for distance learning that are gamified using game design strategies and game elements, by analysing and mapping relationships between key factors affecting students’ emotions and learning effectiveness, and adaptively generating and delivering personalised learning materials that can intervene students’ emotional state thus ultimately improving learning outcomes.

Wearable Haptic Technology for Immersive and Interactive Environments

Haptic technologies can contribute to improving immersion, interaction and imagination in virtual environments. They offer tactile feedback and tactile illusions on interactive virtual systems. In this project, we aim to develop novel and affordable haptic devices, towards enhancing immersion by providing meaningful haptic information that can increase the user's emotional and physiological sensations.

Below are potential L3/L4 projects for Durham UG students.

A Concept Map based Learning Tool to Supports Cognitive Behaviour

Concept Mapping has been proven to have a promising impact on learning. Some studies suggest that effective feedback, hyperlinks, expert templates, comparative strategies, etc. may support better learning behaviour. In this project, you will be developing and evaluating a mobile (iOS/Android) or web-based application providing concept mapping facilities that can help students learn more efficiently and effectively.

A Web-based System for Explaining Artificial Intelligence

Explainable AI (XAI) refers to methods and techniques in the application of Artificial Intelligence such that the results of the solution can be understood and trusted by humans. There is a strong need for quantifiable measurements used by actual humans to assess the interpretability of AI decisions. In this project, you will develop a novel, scalable web-based system that supports the quantifiable assessment of AI Explanations with humans.

Games for Learning Machine Learning

At present, Machine Learning (ML) is among the most popular modules/courses in many undergraduate programmes in the globe. However, there are many challenges in learning ML. For example, (1) to start learning ML, it is important that a student has essential math knowledge including Equations, Functions, Graphs, Differentiation and Optimisation, Vectors and Matrics, Statistics and Probability and so on; (2) ML processes and many of its algorithms are very complex and abstract, thus in need of good visualisations and explanations; and (3) students normally come with diverse background (knowledge, skills, cognitive preferences) such that the “one-size-fits-all” approach does not really work. In this project, you will develop a digital game to help undergraduate students learn ML. It could be either web-based or mobile-based application. You will also evaluate the application with real users.

Intelligent Swarm Robots for Future Classroom

Swarm Robotics is an approach to coordinating many simple physical robots which have collective behaviour when interacting with each other and with the environment. In this project, you will develop a collection of small, simple and affordable robots. These robots will be able to work together to demonstrate the different states of matter (solid, liquid, gaseous), so that the students can, e.g. reproduce the behaviour of atoms using the small robots.

Self-proposed projects are also welcome!

If your project is related to Machine Learning, Behavioural Analytics, Recommender Systems, User Modelling, Personalisation, Cognitive Computing, Affective Computing, Gamification, Serious Games, Participatory Design, Intelligent Tutoring Systems, etc., please let us know.