AI, Learning Analytics & Design

The AI, Learning Analytics, and Design programme in IET brings together internationally recognised interdisciplinary expertise at the intersection of AI, Learning Analytics, Learning Design and Assessment to shape the future of digital education and EdTech in post-compulsory open, distance, and hybrid learning contexts. It investigates how emerging technologies can be harnessed to create more personalised, valued, trustworthy, and scalable teaching and learning experiences for all learners and teachers. 

Our work integrates conceptual frameworks, empirical studies, and design‑led innovation with a focus on the interaction among three forms of knowledge or ‘intelligences’: algorithmic, educator, and learner.  Research interests within the team include:  

• AI‑enabled systems for supporting teaching, learning, assessment, and feedback. 
• Learning analytics for enhancing student outcomes and experience. 
• Innovative technologies for teaching, including the design, operation, scaling and evaluation of AI tools, dashboards, pathway analysis, and institutional embedding. 
• Relational dimensions of human–AI interaction in education, the digital learner experience, personalisation and AI literacies. 
• Responsible, ethical and transparent use of data and AI, including issues of trust, fairness, and explainability and supporting educators to develop necessary literacies. 
• Educational analytics and insights to support the development of key skills using automated sensemaking technologies, including NLP, sentiment analysis, machine learning, and AI. 

With the programme's focus on the future of AI‑enabled learning and teaching, analytics‑informed practice, and evidence‑based educational design, we build on extensive expertise in this area: the OU has been ranked in the global top 5 for Learning Analytics research output, has developed the award-winning OUAnalyse predictive analytics dashboard, and was a founding leader in Learning Design methodologies.  

Our research contributes to sector‑wide debates on the role of analytics, AI and design in learning and teaching, the student experience, redesign of assessment in AI‑saturated environments, and the development of new literacies and capabilities required by both students and educators. Our recent AI-focused research examines how learning analytics and AI can augment, rather than replace, professional judgement, support diverse learner journeys, balance student workload, and strengthen meaningful student support. 

The programme provides a coherent identity for interdisciplinary collaboration, external partnerships, and competitive funding. It supports the development of innovative tools, frameworks, and methodologies to address emerging opportunities and challenges in the rapidly evolving educational landscape. Team members are regular partners in national and international funded projects, contributing research, evaluation and technical expertise. They are also active in strategic university projects, consultancy, training, and postgraduate supervision.   

This programme positions the university as a global leader influencing research, supporting opportunities for all learners and teachers, and shaping the future direction of educational technology. 

Selected insights and findings

2025

• Rienties, B., Ullmann, T., Tessarolo, F., Kwarteng, J., Domingue, J., Coughlan, T., Coughlan, E., & Bektik, D. (2025). Developing an Institutional AI Digital Assistant in an Age of Industry 5.0. Applied Sciences, 15 (12), 6640.

2024

• Aristeidou, M., Cross, S., Rossade, K-D., Wood, C., Rees, T. & Paci, P. (2024). Online exams in higher education: exploring distance learning students’ acceptance and satisfaction. Journal of Computer Assisted Learning , 40(1), 342-359.
• Olney, T., Rienties, B., Chang, D. & Banks, D. (2024). The Learning Design & Course Creation Workshop: Impact of a Professional Development Model for Training Designers and Creators of Online and Distance Learning. Technology, Knowledge & Learning . 29, pp.45–63
• Ullmann, T.D., Edwards, C., Bektik, D., Herodotou, C. and Whitelock, D. (2024). Towards generative AI for course content production: Expert reflections. European Journal of Open, Distance and e-Learning , 26 (S1) pp. 20-34.

2023

• Cross, S., Aristeidou, M., Rossade, K.D., Wood, C. & Brasher, A. (2023). The impact of online exams on the quality of distance learner’ exam and exam revision experiences: perspecitves from The Open University UK. Online Learning, 27(2) , 27-45.
• Rets, I., Herodotou, C., & Gillespie, A. (2023). Six practical recommendations enabling ethical use of predictive learning analytics in distance education. Journal of Learning Analytics , 10(1), 149-167.

2020

• Herodotou, Christothea; Rienties, Bart; Hlosta, Martin; Boroowa, Avinash; Mangafa, Chrysoula and Zdrahal, Zdenek (2020). The scalable implementation of predictive learning analytics at a distance learning university: Insights from a longitudinal case study. Internet and Higher Education, 45, article no. 100725.
• Whitelock, D., Edwards, C., & Okada, A. (2020). Can e-Authentication raise the confidence of both students and teachers in qualifications granted through the e-Assessment process? Journal of Learning for Development, JL4D, 7 (1) pp. 46-60.

2019

• Bektik, D. (2019). Issues and challenges for implementing writing analytics in higher education. In: Ifenthaler, Dirk; Mah, Dana-Kristin and Yau, Jane Yin-Kim eds. Utilizing Learning Analytics to Support Study Success Springer International Publishing, pp. 143-155.