University of York

What you'll study

This interdisciplinary master's brings together AI methods and pedagogical theory to prepare you to design and evaluate intelligent learning technologies. Teaching typically covers foundational AI and machine learning techniques alongside subjects specific to education such as learning analytics, instructional design and the psychology of learning.

• Core AI and data methods – machine learning, data mining, basic statistical and computational approaches used to model learners and learning processes.
• Learning analytics and educational data – collecting, processing and interpreting learner data; dashboards and visualisation for decision-making in education.
• Technology-enhanced learning theory – theories of learning, digital pedagogy, and the design of technology-mediated learning experiences.
• Adaptive and intelligent tutoring systems – architectures for adaptive instruction, student modelling and personalisation strategies.
• Human–computer interaction and UX for learning – designing usable interfaces and evaluating user experience for learners and teachers.
• Ethics, policy and accessibility – ethical considerations, data protection, fairness and inclusive design in AI for education.
• Research methods and dissertation/project – qualitative and quantitative research methods, culminating in an independent research project or industry-linked dissertation that applies AI to a real learning problem.

Modules and assessment methods typically combine lectures, practical computing labs, group design work, case studies and a substantial project or dissertation that demonstrates your ability to apply AI techniques to educational contexts.

Entry requirements

Applicants are usually expected to hold an undergraduate degree in a relevant subject, such as computer science, education, psychology, data science or a related discipline. A strong 2:1 honours degree (or equivalent) is commonly required, although applicants with a lower honours degree plus relevant professional experience in education or educational technology may also be considered.

Because the programme integrates technical and educational content, demonstrable experience or aptitude in quantitative methods, programming or educational practice strengthens an application. All applicants whose first language is not English will need to meet the University’s English language requirements.

Career prospects

Graduates enter a range of roles where AI and educational expertise are valued. Typical career paths include:

• Learning technologist or digital learning specialist in schools, colleges or universities
• Learning/data analyst or learning scientist using analytics to support curriculum design and institutional decision-making
• Instructional designer or curriculum developer for online and blended learning
• Product manager or developer roles in EdTech companies building adaptive learning platforms, assessment tools or personalised learning services
• Research careers in higher education or research institutes focused on AI in education, human–computer interaction or learning sciences
• Policy or consultancy roles advising education providers and organisations on the deployment and governance of AI in learning

The programme’s project component also offers opportunities to build a portfolio of applied work attractive to employers or to progress to doctoral research.

Why study at University of York

The University of York offers an interdisciplinary environment that connects expertise across education, computer science and psychology. The programme benefits from staff who conduct active research into technology-enhanced learning, learning analytics and human–computer interaction, enabling teaching that is informed by current research.

Students have access to University computing facilities, specialist software and the library’s digital resources, as well as career services and support for enterprise and industry engagement. Collaboration opportunities with schools, colleges and EdTech organisations help you apply learning in real-world settings and build professional networks.

York’s emphasis on combining rigorous technical training with pedagogical insight makes this MA suitable for those who want to shape how AI is responsibly and effectively used in education.