Understand how humans and AI work together, and gain tools to make better decisions in complex systems.
The future of intelligence isn’t human or machine.
It's both.
The MASc in Artificial Intelligence & Collective Intelligence equips you to understand, design, and lead systems where humans and AI work together to solve complex, real-world problems.
As global challenges grow more interconnected, solutions increasingly depend on hybrid systems that combine human judgement, institutional structures, and AI. From climate governance to content moderation, scientific discovery to policymaking, intelligent collectives are already shaping high-stakes decisions.
This MASc equips students to do just that: to become ethically grounded, method-savvy practitioners who can build and steward intelligent collectives — whether human, machine, or both.

Understand, design, and lead systems where humans and AI work together.
Master’s degree (MASc)
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September 2026
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1-year
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£14,000 / year
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Full-time
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Learning for the age of hybrid intelligence
We’ve designed a curriculum that reflects how intelligence actually operates in the real world, across people, machines, institutions, and systems. Rather than treating AI as a purely technical problem or collective behaviour as a purely social one, the programme brings them together through core modules in AI and collective intelligence, shared interdisciplinary methods, and applied learning.
You’ll build the technical literacy, critical judgement, and ethical grounding needed to interpret complex information and work across systems.
On this programme, you will:
Learn how AI systems work, how groups think and fail, and how intelligence emerges in hybrid human–machine systems.
Build a toolkit for interpreting data, language, media, and uncertainty to help reduce blindspots.
Apply what you’ve learned to a real challenge from your workplace or a cause you care about.

The curriculum
Combine core modules in AI and collective intelligence with shared interdisciplinary methods that broaden your range and sharpen your judgement.
During this degree, participants will take the following four modules on AI and Collective Intelligence.
An applied introduction to data science and machine learning, focused on understanding how models work and how choices shape outcomes. By working with your own data and problems, and by using AI-assisted development tools as part of the learning process, you will gain practical experience in building and interrogating machine learning models while retaining conceptual control.
Skills:
- Interpreting and evaluating ML models
- Working with data responsibly
- Communicating with technical teams
A deep dive into how groups make decisions, succeed, and fail, grounded in classic and contemporary research
Skills:
- Diagnosing collective bias and failure
- Designing better group decision systems
- Translating research into practice
Explores how human judgement and AI combine in real organisations, using case studies and speculative futures
Skills:
- Analysing human–AI roles and power
- Evaluating emerging AI practices
- Reasoning about future systems
A collaborative, hackathon-style module where students design and deliver a collective response to a real problem
Skills:
- Large-scale collaboration
- Collective problem framing
- Applying theory under real constraints
All students build a shared interdisciplinary foundation while tailoring their skills to their background and goals. Alongside two compulsory interdisciplinary modules, you will choose one quantitative and one qualitative methods module to develop a balanced, real-world toolkit.
QUANTITATIVE METHODS
These modules develop your ability to work confidently with data, evidence, and analysis. ‘Cracking the Code’ is mandatory for anyone who cannot yet code in Python. If you are already confident in coding, you can choose ‘Everything Counts’.
Cracking the Code teaches python through data science. Students will learn to code by engaging in practical applications of python libraries as they relate to data science problems. We’ll also encourage students to think about the role of data science in tackling complex problems, considering the ethical and logistical dimensions of what you can actually do using python.
We teach python because it’s the most popular programming language and has a variety of applications including web development and quantitative research. If you’re going to learn one coding language, we think it should be python. But if you already know python, you can learn another language (e.g., R).
Knowledge and skills:
- Programming (python)
- Data science
Everything Counts is a quantitative module that acquaints students with different approaches to statistics (Bayesian and Frequentist).
These approaches represent two interpretations of how we can use numerical data to answer questions and inform decision-making.
It will also deal with how data can be used to tell a story, including studying the essentials of data visualisation.
Knowledge and skills:
- Statistics
- Mathematics
- Critical thinking
QUALITATIVE METHODS
These modules focus on how meaning is created, communicated, and interpreted through language, visuals, and narrative. You’ll develop tools to analyse and create qualitative data that shape understanding and decision-making.
Students will choose one module from the list below.
Re:Form will teach students how to understand visual thinking and the ways in which media (photography, 3D modelling, illustration) help us communicate.
To do this, students will look at how visual media can be created, analysed and archived - from both a qualitative and quantitative perspective. Qualitative approaches centre on learning to interpret, read and curate visual media. Quantitative approaches will allow students to use their coding knowledge to engage with images at speed and scale.
Knowledge and skills:
- Media analysis
- Art
- Programming (python)
The Right Word aims to demonstrate how language can be produced and analysed using insights from linguistics. This will involve exploring the meaning of language (semantics) and its function in social context (pragmatics). The module will also explore narrative as a powerful tool in communication e.g., storytelling.
This module will also teach students the basics of methods in natural language processing (NLP). NLP allows language data to be manipulated at speed and scale, and is behind many of the advances in AI that have occurred recently.
Knowledge and skills:
- Linguistics
- Natural language processing (NLP)
- Storytelling
CORE METHODS
Throughout the programme, all students take two compulsory module in Engaging Complexity and Integration.
This module will introduce students to the topic of complexity as it is understood in the quantitative sciences. Starting with mathematical fundamentals, students will be exposed to concepts from cellular automata, dynamical systems, and information theory.
This module will allow learners to discern high-level patterns of behaviour in seemingly dissimilar systems. On the one hand, this will allow participants to accurately identify and describe similar dynamics in equivalently complex systems. On the other, an appreciation of the nature of complex systems is a prerequisite to influencing such systems.
If interdisciplinary inquiry is to be more than a collection of concepts and methods, it requires us to understand how novel and synthetic results can be achieved by integrating different bodies of knowledge. This module will take a step back from learning in practice and focus on the process of integration, if not dis-integration, in theory.
The Capstone is your chance to tackle a real problem in artificial and collective intelligence. Guided through stages of discovery, integration, and delivery, you’ll apply what you’ve learned to design, test, or evaluate a system that matters to you. Projects might include participatory AI tools, deliberative platforms, or models of group decision-making, producing outputs such as policy briefs, interactive visualisations, or working prototypes you can use as a calling card.
To gain a degree in the UK you must pass a certain number of credits in each year of the degree. Each module is given a credit, which you are awarded when you pass each module at assessment.
Students will be expected to be familiar with GCSE-level algebra to do this master's.
*The content of our modules is subject to change and approval as we revise our modules each year depending on student feedback and developments in the field.
A degree only LIS could build
Very few postgraduate degrees integrate artificial intelligence, collective intelligence, systems thinking, and participatory design into a single programme.
Where these topics appear elsewhere, they are often siloed. Technical AI without social context, or collective behaviour without computational grounding.
The MASc in AI & Collective Intelligence at LIS brings these strands together. Inspired by leading work at institutions like MIT, Stanford, and UCL, LIS goes further by offering a fully interdisciplinary, application-led degree that combines theory and practice, human and machine perspectives, and ethics by design.
It prepares graduates to shape how AI works in the real world, not just study it in isolation.

Niccolò is a senior behavioural and data scientist with a DPhil in experimental psychology from Oxford. He has held positions at the MIT Media Lab and the Max Planck Institute, and co-founded PSi, a platform for large-scale online conversations. His research in collective intelligence investigates how groups of people and machines can be smarter together, and has been featured in top journals and the international press (BBC, Business Insider, Forbes, El País).

You may be working in policy, technology, consulting, government, or large organisations. You are already encountering AI-mediated systems in decision-making, coordination, or automation, and want a deeper understanding and fluency. You are less interested in building models for their own sake and more interested in how AI reshapes power, responsibility, and outcomes at scale.
You may be working in social impact, climate, civic technology, or research. You want the technical and analytical grounding to complement your domain expertise, and the ability to engage confidently with engineers, data scientists, and decision-makers — without losing sight of ethics or social consequences.
A shared concern: existing tools, institutions, and ways of thinking are no longer sufficient.
This degree is for people who want to shape how intelligence is designed, distributed, and used.
You are curious, reflective, and comfortable working across disciplines. You want a programme that treats complexity seriously, values critical thinking, and prepares you to lead in environments where certainty is rare and consequences are real.
Careers and development
Our careers offering for master’s students revolves around three key pillars:


“The interesting thing about setting up a business is that it’s a series of problem-solving. What you need is an ability to focus in on a problem, pull back out, and connect the dots across the space. And that’s what the master’s is doing. For me, it’s exactly the kind of thing I wish I had done before I set up my first business.”

Richard Reed
Co-founder, Innocent Drinks
Why interdisciplinarity?
Learn to build on skills and knowledge from across multiple disciplines, unlock your interdisciplinary problem-solving potential, and apply it to the real world.
Study options
Deciding to study for a master’s is a significant commitment. It’s important to consider the mode of study and pace at which you are expected to complete the programme.

Full-time, campus-first
Designed for those that want an immersive social experience and to complete your master’s in one year. For those that already live in or near London or would like to.
Finance
These are the fees for the 2026/27 academic year.
Find out more about our master's course fees, financing options, and support available through bursaries and grants.
How to apply
February 2026
until we fill places on the course
September 2026
Applications to our master’s degrees are considered on a rolling basis. We will continue to accept applications until we fill places on the degree. In order to secure a place on the course, we’d encourage you to submit your application as soon as possible.





































