Augmented reality (AR) is a three-dimensional (3D) technology that merges the digital and physical worlds in real time. It enhances the user’s sensory perception of the real world with a contextual layer on information . To date, AR technology has been used in many fields, such as medicine, architecture, clinical psychology etc. . The evolution of AR has been fast and global, very recently, AR technology has attracted more attention in the educational system, e.g., it is capable of offering additional information about the physical environment and providing enriching learning experiences.
The superposition of diverse multimedia items to physical world scenes makes AR a cognitive support in understanding and performing complex tasks with special contribution to educational field [3, 4]. It is especially useful in the science and engineering fields, such as spatial ability, conceptual understanding, practical skills and scientific inquiry learning [5-8]. The present project shall focus on science and engineering contexts.
Unlike Virtual Reality (VR), which creates a totally artificial environment, AR is the integration of digital information with the user's environment in real time. Although some good contributions of AR in education system and offer greater clarity of insight than traditional reports crammed with words, stats, and figures, it was majorly used as a data visualization tool and lack of ability of advanced data analytics. A smart learning environment should be context based and adaptive to the individual learner’s behaviour. There is an urgent need and challenge to enhance AR by data engineering and AI so that it can provide both contextual, personalized learning experiences and offer simulation or exploration activities with features that are based on digital knowledge discovery mechanisms to utilize information via interaction with digital elements. It will set up a link between what is real and what is computer-generated by enhancing what is perceived for enhanced teaching and learning.
Enhanced AR with Data engineering and AI, encompassing visual analytics, computer imaging and data visualisation, seeks to bridge extant gaps in digital learning and teaching by using more intelligent means in the process of data. This project will make an innovative contribution by introducing inter-disciplinary state of the art data analytic and artificial intelligence techniques  integrated in AR technology [1, 5, 8] to address the above key challenges, specially to synthesize information and derive insight from massive, dynamic and diverse data by providing timely, defensible and understandable assessments for enhanced learning.
Aim and Objectives
In this project we propose to investigate the characteristics of AR in education and develop affordable prototype AI driven AR platforms to enhance the teaching and learning in the science and engineering context. Specifically, the objectives of this project are to:
(i) Evaluate the current state of the art of AR used in digital education, especially opportunities, and technical limitations as well as the challenges of building AR applications enhanced by the data engineering and AI features;
(ii) Investigate key characteristics and design features of ARbased learning technology for science and engineering education;
(iii) Investigate and deploy the integration of the state of art AI and data analytic technologies into AR technology;
(iv) Develop affordable prototype AI driven AR platforms;
(v) Evaluate and measure outcomes and impacts addressed in AR for science and engineering studies.
This interdisciplinary project brings together expertise in data engineering, AI, STEAM, and AR from the School of Engineering, School of Computing, and expertise in research-informed teaching from the Ulster Business School.
If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.
The University offers the following awards to support PhD study and applications are invited from UK, EU and overseas for the following levels of support:
The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £ 15,009 per annum for three years. EU applicants will only be eligible for the fee’s component of the studentship (no maintenance award is provided). For Non-EU nationals the candidate must be "settled" in the UK. This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
Due consideration should be given to financing your studies; for further information on cost of living etc. please refer to: www.ulster.ac.uk/doctoralcollege/postgraduate-research/fees-and-funding/financing-your-studies
As Senior Engineering Manager of Analytics at Seagate Technology I utilise the learning from my PhD ever day
Adrian Johnston - PhD in InformaticsWatch Video
Friday 7 February 2020
Late March 2020
The largest of Ulster's campuses
When applying for this PhD opportunity please quote reference number: