Content Blocks

AI and Society (MASc/PGDip)

Explore our AI and Society taught Master's degree at Warwick

Page updates

We may have revised the information on this page since publication. See the edits we have made and content history.

P-I401 (MASc)
P-I402 (PGDip)

MASc/PGDip

MASc: 1 year full-time, 2 years part-time; PGDip: 9 months full-time, 21 months part-time

28 September 2026

Centre for Interdisciplinary Methodologies

University of Warwick

Join Warwick's AI and Society MASc/PGDip and learn to critically engage with AI. The Centre for Interdisciplinary Methodologies works across disciplines, drawing from the Arts, Humanities, Social Sciences and Sciences, to answer employers' demands for a new generation of researchers.

“...the UK needs a larger workforce with AI expertise. Last year there was a 16% increase for online AI and Data Science job vacancies and research found that 69% of vacancies were hard to fill”

The UK's National AI StrategyLink opens in a new window

CIM’s new MASc in AI and Society is an innovative, interdisciplinary course designed to enable students to respond creatively and critically to the rapidly evolving challenges of a world transformed by technological developments and processes. This MASc course offers an analytical and critical approach to AI, as well as a technical and practical grounding that enables students to interrogate and develop AI-based technologies. Our modules will include a focus on “doing AI”, e.g. through coding, building, testing AI as well as through engagement with governance and public policymaking in relation to AI and technological innovation in society.

During the MASc AI and Society students will develop:

• Grounding in state-of-the-art critical and interdisciplinary research to interrogate AI and its impact on society that will enable students to engage deeply with rapidly evolving challenges in the design, development, deployment and governance of AI systems.
• Comprehensive technical skills to develop and assess AI systems that will be underpinned by expertise in designing, coding and testing AI techniques along with an understanding of responsible and ethical practices.
• A distinct, comprehensive skillset to respond to policy and governance implications of AI that not only builds on one’s ability to critically analyse AI’s impacts but also provides an in-depth understanding of how and when AI works and fails.
• A unique portfolio that grounds your learning in real-world problems that is developed through engaging with case studies, working with academic and non-academic stakeholder on problems at the intersection of AI, technology, society and policy.

What is distinct about this course?

• The course uniquely combines a critical and analytical understanding of the impact of AI on society with technical understanding in how AI algorithms and architectures work along with skills in developing and evaluating AI-based systems.
• The course allows students from diverse academic backgrounds and with varying levels of technical and critical training to develop their understanding and skills in all aspects of the society-AI-technology frontier – from the technical to the social, and policy dimensions.
• The course enables students to develop in diverse ways that are suited to their career trajectories. The core modules in this course will provide all students with the foundations for a rich understanding of the AI/society frontier and the technical workings of AI. The students will then be able to specialise in ways that are best suited to them through the optional modules and a final graduation project/dissertation on a topic of their choosing.
• The course provides an emphasis on real-world problems and interdisciplinary problem-solving through applied case studies, hands-on projects, engagements with non-academic organisations and placement opportunities.

Modules in this course make use of a range of teaching and learning techniques, including, for example:

• Blended learning including the use of an online virtual learning environment
• Student group and project work
• Lectures
• Coding labs
• Seminars
• Reading and directed critical discussion
• Independent research by students
• Practice-based activities

For this course, a typical workshop contains around 20-30 students, and a typical seminar around 16 students.

There are around 8-10 hours contact hours per week for this course, depending on type and number of optional modules chosen.

A combination of essays, reports, design projects, technical report writing, practice assessments, group work and presentations and an individual research project (either as 10,000 word dissertation or as a 5000 word project with practice-led output).

Reading lists 

If you would like to view reading lists for current or previous cohorts of students, most departments have reading lists available through Warwick Library on the Talis Aspire platformLink opens in a new window. 

You can search for reading lists by module title, code or convenor. Please see the modules tab of this page or the module catalogueLink opens in a new window.  

Please note that some reading lists may have restricted access or be unavailable at certain times of year due to not yet being published. If you cannot access the reading list for a particular module, please check again later or contact the module’s host department.  

Your timetable

Your personalised timetable will be complete when you are registered for all modules, compulsory and optional, and you have been allocated to your lectures, seminars and other small group classes. Your compulsory modules will be registered for you, and you will be able to choose your optional modules shortly before joining us.

2:1 undergraduate degree (or equivalent). As an interdisciplinary course, we welcome applications from diverse academic backgrounds.

• Band B
• IELTS overall score of 7.0, minimum component scores of two at 6.0/6.5 and the rest at 7.0 or above.

There are no additional entry requirements for this course.

The course provides training in the foundations of deep learning - the use of multi-layered artificial neural network architectures in machine learning - enabling students to critically analyse and interrogate AI through working hands-on with AI models and systems.

This is combined with an introduction to social science and humanities research on the impacts of AI on contemporary society and culture, as well as the relevance of contemporary AI developments for these research fields themselves.

Students will be trained in the use of interdisciplinary methods like data mapping to analyse the interaction between AI science & innovation and society and culture.

Finally, the course will introduce students to the state-of-the-art in public policymaking and engagement in relation to AI tech innovation and its social impact. The training is enriched by a wide range of options that the students can choose from to tailor a trajectory that will suit their profiles and career plans.

Optional Core Modules

Optional modules vary from year to year, and students choose combinations worth 60 CATs (credits). The links below are to the 20-CAT (credit) versions, but many are available at 15 and 30 CATs. Example optional modules may include:

We have revised the information on this page since publication. See the edits we have made and content history.

This is a holding content block which does not currently display on the page. To make it live, update the copy above, change the Title to remove the brackets, and delete this sentence.