MA Educational Leadership

Fees for entry in 2027 have not yet been set. For reference, the fees for the academic year beginning September 2026 were as follows:

• MA (full-time)
  UK students (per annum): £13,700
  International, including EU, students (per annum): £29,400

The fees quoted above are fully inclusive of tuition, administration and computational costs.

Fees for entry are subject to yearly review. The University reserves the right to increase your tuition fee by up to 7% each year for courses lasting more than one year, including to reflect rising costs associated with delivering our educational and wider student experience. Postgraduate fees information .

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International student CAS deposit

Self-funded international applicants are required to pay a deposit of £2500 towards their tuition fees before a confirmation of acceptance for studies (CAS) is issued. Some applicants will be required to pay a higher deposit. More information on tuition fee deposits .

All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).

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Emerging technologies such as Generative Artificial Intelligence, Augmented/Virtual Reality, Data Visualisation and Machine Learning could transform education at primary, secondary and tertiary levels. This unit develops a critical approach to the role of emerging technologies in shaping the future of education through detailed case studies of emerging technologies, alongside more established innovations such as Big Data and Massively Open Online Courses.  

This course unit aims to provide an understanding of emerging technologies (such as machine learning, augmented/virtual reality, generative AI and data visualisation) which are used within education. It will explore the implications, for education and learning, of these technologies with a view to supporting critical but open-minded engagement with their potential. Students will develop understandings of themes and concepts relevant to these emerging technologies through a series of detailed case studies. These will be supported through critical perspectives on digital pedagogy, digital design, organisational sociology and political economy which support analysis of the claims being made about these technologies, the commercial interests underpinning them and their pedagogical potential. The course seeks to equip students with transferable skills for critical analysis of emerging technologies beyond these case studies. It will enable them to undertake independent research with a view to understanding the potential of technological innovations in applied settings, while remaining resistant to the hype which surrounds them.

1) Detailed case studies of four emerging technologies in education: machine learning, augmented/virtual reality, generative AI and data visualisation. These will be supplemented by briefer engagement with MOOCs, personalised learning and gamification to illustrate the wider concerns of the unit.

2) An overview of current debates around data, design for and analysis of data and implications for education and learning. Exploration of how data is gathered, including design principles, issues of algorithmic bias and learning analytics. The unit will position big data and machine learning as foundational to the other case studies of four emerging technologies in education: machine learning, augmented/virtual reality, generative AI and data visualisation. These will be supplemented by briefer engagement with MOOCs, personalised learning and gamification to illustrate the wider concerns of the unit.

3) Perspectives from organisational sociology and political economy to understand the commercial motivations of educational technology firms, their capacity to influence educational policy and how this manifests itself in their uptake within schools, colleges and universities. 

4) Ethical and social implications of perspectives embedded in technology design and use including security, privacy and associated debates on regulation and openness. Overview of critical perspectives on digital education with a view to achieving a fair balance between caution and pragmatism with regards to emerging technologies.

The course unit will be three blocks of four weeks, built around a two hour lecture /seminar, as well as an independent reading week exercise:        

1) Case studies of big data & machine learning, virtual/augmented reality, generative AI and data visualisation which will involve a mixture of lecture content, seminar discussion, hands on experimentation and field visits [MC1] to use these technologies in applied contexts. [MC2] This will be supplemented by a ‘taster’ session which includes overviews of MOOCs, personalised learning and gamification as once emerging technologies which are now well established in education.
           
2) Critical engagement with debates around data, design for and analysis of data and implications for education and learning. Students will be supported in identifying and analyzing their own digital environment and digital footprint, in order to ground subsequent discussion in their immediate experience as digital agents[MC3] . Perspectives from political economy and organizational sociology will be used to explore why technology is taken up in educational organizations in the way it is, as well as the wider economic factors which motivate the development of educational technology.

3) Exploration[MC4]  of the implications of emerging technologies for digital education futures. Students will form into working groups and choose a case study for assessment 2. These sessions will be organized in a tutorial format to support the students in self-directed research. One of these sessions will involve formative feedback on a presentation to the class about their case study. There will also be a podcasting workshop intended to support them in the development of the podcast.

Students will be supported through this process by an additional web resource [MC5] which capture and provide background to lecture material, as well as provide branching guidance for going further on case studies (block 1), critical perspectives (block 2) and group research (block 3).

Understand core concepts around emerging technologies in education, particularly with regards to the foundational role of digital data. [MC1] 

Recognise how design and analytical approaches draw on theories of learning and conceptual assumptions.

Identify how design decisions and commercial imperatives reinforce and privilege certain discourses about learning and education.

Critically evaluate academic literature and learning resources in terms of emerging technologies in education, particularly with regards to digital data.

Critically reflect on their own practices with technology for learning and education in terms of security and privacy issues.

Critically evaluate claims concerning the potentials and pitfalls of emerging technologies in education. 

Undertake collaborative research on emerging technologies.

Present findings of collaborative research in oral presentation.

Produce audio resources to collect and present original research.

Explore case studies on how data is used, including algorithmic bias and learning analytics

Explore ethical and social implications of perspectives embedded in technological design

Understand current debates around emerging technologies

Students submit a set of slides or explanatory audio/video on a specific theme developed from their group case study.

 30%

Students develop a twenty minute podcast in groups of 3-5 which analyses the implications of an emerging technology in education, either through a present day appraisal of the issues surrounding it or a speculative report from an imagined future where that technology has become mainstream within the education system.

20 minutes

70%

Feedback will be available via VLE

Ball, M. (2022). The metaverse: and how it will revolutionize everything. Liveright Publishing.

Bozkurt, A. (2023). Generative artificial intelligence (AI) powered conversational educational agents: The inevitable paradigm shift. Asian Journal of Distance Education.

Cooper, G. (2023). Examining Science Education in ChatGPT: An Exploratory Study of Generative Artificial Intelligence. Journal of Science Education and Technology, 1-9.

Domingos, P. (2015) The Master Algorithm: How the quest for the ultimate learning machine will remake our world. Penguin: UK.

Donohoe, D., & Costello, E. (2020). Data visualisation literacy in higher education: An exploratory study of understanding of a learning dashboard tool. International Journal of Emerging Technologies in Learning (iJET), 15(17), 115-126.

Eubanks, V. (2017) Automating Inequality. St. Martin’s Press: New York.

Eyal, N. (2014). Hooked: How to build habit-forming products.

O’Neil, C. (2016) Weapons of Math Destruction. Penguin, UK

Watters, A. (2014) The Monsters of Education Technology. Retrieved from Amazon.co.uk and at: http://monsters.hackeducation.com/

Thaler, R. H. and C. R. Sunstein. (2009). Nudge: Improving Decisions About Health, Wealth, and Happiness. New York: Penguin Books

Williamson, B. (Ed.). (2015). Coding/Learning: software and digital data in education. Stirling: University of Stirling.

Williamson, B. (2017) Big data in Education: The digital future of learning, policy and practice.  Sage: London.

Zang J, Dummit K, Graves J, Lisker P, Sweeney L. (2015). Who Knows What About Me? A Survey of Behind the Scenes Personal Data Sharing to Third Parties by Mobile Apps. Technology Science. Retrieved from: https://techscience.org/a/2015103001