Human-Centred Artificial Intelligence

Program overview

This English-taught MSc focuses on designing and building AI systems that prioritise human needs and behaviour. You will learn to develop, implement and evaluate human-centred AI applications — from search and recommendation to natural language processing tasks such as hate-speech detection and personalised dialogue systems — by combining advanced computer science and data-science techniques with human–computer interaction and psychological insights.

The curriculum comprises 14 compulsory modules (including the Master’s thesis) totaling 120 CP. After an introductory overview of the field, you complete four core modules: AI Ethics; Technologies for Human-Centred AI 1 and 2 (where you apply human-centred design principles to areas like NLP, recommender systems and information retrieval); and a Free Elective for deepening or expanding your knowledge, including topics in Computer Science and Data Science. Many compulsory modules offer thematic options so you can shape the programme around your interests.

A specialisation component lets you focus more deeply on topics that interest you: you choose seven modules from a set of courses and projects drawn from Information Science, Computer Science, Data Science and Psychology. Examples of specialisation modules include Explainable AI, Generative AI, Information Behaviour, Deep Reinforcement Learning, Current Topics in Human-Centred AI, Empirical Human-Centred AI and Computational Human-Centred AI. Towards the end of the degree you apply what you’ve learned in a project seminar — planning and implementing a human-centred AI project individually or in a team — which can be carried forward into your Master’s thesis.

Requirements and key facts

• Degree: Master of Science (MSc); language of instruction: English
• Total workload: 120 CP across 14 compulsory modules (includes Master’s thesis)
• Core modules: introductory overview + AI Ethics; Technologies for Human-Centred AI 1 & 2; Free Elective
• Specialisation: seven modules chosen from interdisciplinary offerings (Information Science, Computer Science, Data Science, Psychology)
• Many compulsory modules include thematic options to tailor studies to your interests
• Final project: project seminar with the option to continue into the Master’s thesis

The programme combines classroom learning, guided exercises, seminars and project work to build both theoretical knowledge and hands-on skills in human-centred AI. Core thematic areas include natural language engineering, information behaviour, and user-centred approaches to information retrieval and recommender systems. Courses are taught by researchers across the Faculty of Informatics and Data Science and other faculties, giving you the flexibility to tailor your studies to your interests while staying closely aligned with active research in the field.

Teaching formats emphasize applied learning and independent scholarship. Lectures with accompanying practical classes teach methods you then apply in exercises; seminars train you to investigate current research topics, prepare scientific presentations and write critical papers; and project seminars or group projects let you practise research methods and develop a substantial project that leads into your Master’s thesis. Assessment combines written or oral examinations, coursework presentations, seminar papers, and project documentation, all designed to demonstrate both subject knowledge and your ability to apply methods in practice.

Coursework & assessment requirements (what you will do)

• Attend lectures and accompanying practical classes where you learn methods and apply them in-class.
• Complete exercises (may be compulsory or optional) typically assessed as coursework.
• Sit written or oral examinations to test acquired knowledge and method application.
• Prepare and deliver seminar presentations and submit a written seminar paper that critically reviews current research.
• Participate in project seminars or group projects, present your planned research as coursework, and submit a project paper or documentation as the project assessment—these projects prepare you for the final thesis.

Key modules and learning outcomes

• Key thematic modules: natural language engineering; information behaviour; user-centred information retrieval and recommender systems.
• You will gain interdisciplinary expertise in human-centred AI, practical experience in research methods, the ability to design and carry out a research project, and skills in preparing and critically evaluating scientific presentations and written work.

Graduates are prepared for technical and interdisciplinary roles where AI systems must work well for people: positions such as machine learning engineer, NLP specialist, data scientist, UX/AI researcher, or product designer for AI-driven systems in industry and research labs. The programme’s blend of technical, empirical and ethical training also provides a solid basis for PhD studies or research positions that address human‑centred aspects of intelligent systems.

Employers include tech companies building conversational agents, recommender systems or search engines; research institutions focusing on HCI and AI; policy and ethics teams assessing AI impact; and organisations integrating explainability and user research into AI products.