Competences are understood here as a combination of knowledge, skills, and attitudes that cut across contributing scientific disciplines. We have structured the substantive learning outcomes along the lines of the three pillars of the HSI program.
Design thinking, systems thinking, and business innovation
- Describe, explain, and apply a selected set of systems thinking and systems engineering concepts and relate them in a holistic view, along a design process of definition, concepting, design, and realization.
- Understanding the opportunities of data, computing and AI for the creative and aesthetic design, the aesthetic modalities of learning systems and the new forms of design with different degrees of intelligence.
- Realizing visionary scenarios towards the future ecosystem design of learning systems, based on the understanding of the future technical feasibilities.
- Creating experiential prototypes, locating, selecting, and integrating prototyping techniques and approaches to fit a particular context.
- Evaluating systems and prototypes with regards to users, economic, technical, and social feasibility.
- Shaping and managing design processes suitable for industrial and business contexts.
- Generating ideas and developing innovative design concepts and business models.
- Being comfortable in the current economic reality of complexity and uncertainty in order to be able to explore, understand and act in this hyper-complex world, disrupted by exponential technologies.
- Being a confident creative leader who can lead a project to success, with a multidisciplinary perspective, based on innovative and systemic methodologies.
Scientifically founded and methodologically sound user-centered and data-driven approaches.
- Solid training in the fields of human-computer interaction with a focus on design and evaluation of usability and user experience as key software qualities.
- Identifying, formalizing, and validating system requirements of users and other stakeholders relating to User-System Interaction and related user experiences.
- Understand the theories of how people perceive and relate to AI as well as the current debates on the (in)explainability and fairness/biases in AI algorithms.
- Gain an overview of methods for designing AI systems that model or represent users and adapt to their behaviors.
- Design interfaces for human-AI interaction based on the current best practice.
- Locating and applying scientific methods and results in the design and evaluation of and user experiences.
- Working with quantitative and qualitative data, translating data into insights, using data as creative material in the design and co-creation process with users and stakeholders.
Understanding of artificial intelligence and machine learning
- Understanding of the fundamental principles underlying artificial intelligence, and their strengths, limitations and technical feasibility.
- Developing a critical perspective on the limitations and implications of the advanced AI technology for creation of everyday products, systems, and services.
- Appreciate the opportunities and challenges of different application domains of human-AI interaction.
- Appreciate the main ethical issues and considerations relating to human use of AI.
- Machine learning and data mining to understand the user and their needs and identify relationships as the basis for value propositions within a design concept or a business model.
In addition to the three substantive pillars, the learning outcomes include professional skills: Communicative skills, both orally and in writing:
- being able to present a project proposal, progress or the final results of a project coherently and in a manner which is well-tuned to the audience
- being able to answer subsequent questions constructively
- being able to write a scientific text in a well-structured and understandable way.
- Cooperative skills: being able to participate actively and constructively in a multidisciplinary team.
- Project management:
- having an understanding of the principles of project management
- being able to apply these principles through the setting of goals, intermediate milestones and prioritization of activities.
- Ethics and scientific integrity:
- awareness of issues of scientific integrity, based on the Netherlands Code of Conduct for Scientific Practice
- being able to identify ethical issues relevant to AI and to contribute constructively in a discussion relating to ethical issues.