Programme

• Start at 13.45 PM with an overarching lecture on human–AI collaboration [Introductory Lecture: Do we need AI system? Dr Simone Borsci]
• 14.45 – 16.30 L1 - AI and Public Service - How intelligent is actually Artificial Intelligence? Ethics of AI and its use in public sector services [Dr Elifcan Karacan]
• Drinks and networking

• 10.45 – 12.30  L2 - Quantum Technologies and quantum cybersecurity [Dr Luca Possati]  

Lunch Break

• 13.45 – 15.30  L3 - Sustainable AI [Dr Alejandro Dominguez Rodriguez].
• 15.45 – 17.00  L4 – Trusting intelligent systems to enhance our transport experience [Dr Sarah Kustumastuti].

Social Dinner

• 9.00 – 10.30  L5 - AI Walking Among Us [Dr Cesco Willemse]
• 10.45 – 12.30  L6 - How AI is reshaping how we learn [Dr Farrokhnia, Mohammadreza].

Lunch Break

• 13.45 – 15.30  L7 - AI Agents: Oracles or False Prophets? The role of AI-driven agents for digital mediated reality from gaming to daily life [Dr Maximilian A. Friehs]. 

[Dr. Simone Borsci].
This introduction talk will provide an overview of activities of the course and an overall reflection on Human and AI collaboration.

[Dr Elifcan Karacan].

Contents: This part of the course focuses on the capabilities and limitations of AI, challenging common perceptions of its "intelligence." We will begin by examining early models of AI and addressing key ethical concerns. Particular attention will be given to the risks posed by using AI tools in public sector services. We will discuss causes of algorithmic bias and how it can affect the trust between citizens and authorities in governance and what can be done to mitigate the risks of using AI tools in public sector services. 

Activity: In the second part of the session, we will explore the use of AI in public sector services with cases from the Dutch municipalities. The emphasis will be put on the role of transparency, accountability, efficiency, and fairness in automated decision-making. The session will conclude with an open discussion on how AI can be responsibly integrated into systems that significantly impact society. Participants are invited to bring their own case.

Learning objective: After this class learners will be able to reflect on the responsible use of AI for service provision, considering key elements such as transparent usage, accountability, efficiency and fairness in the design of AI utilisation and communication with stakeholders on how data is utilised.

[Dr Luca Possati].

Contents

This module provides professionals with a strategic framework to manage high-impact technological transitions, using quantum technologies and cybersecurity as a concrete case. It focuses on how to make robust decisions under uncertainty, how to avoid both hype-driven adoption and defensive paralysis, and how to assess risks when systems become too complex or opaque to be fully understood or independently verified.

Activity (Solarium exercise)

Split the team into 2–3 groups, each crafting a different strategy for mitigating the same threat; compare pros and cons, then select the best plan. Quantum scenario: an adversary can “harvest now, decrypt later,” simulating the risk of a global quantum attack on the internet.

Learning objective

By the end of this module, participants will be able to make strategic and technical decisions under conditions of deep technological uncertainty, avoiding both hype-driven adoption and defensive paralysis. They will learn to identify and assess long-term and delayed-impact risks, and to apply the same reasoning to AI- and automation-driven infrastructures. The course will enable them to recognise how opacity, vendor dependence, and verification asymmetries generate hidden security and governance vulnerabilities in complex systems, and to translate these technical issues into organisational and strategic risk terms.

[Dr Alejandro Dominguez Rodriguez].

Contents

The effects of AI on the environment are an ongoing topic of discussion across many fields, including threats such as climate change. Innovation is needed, and working with AI can help to find faster solutions. Ironically, AI consumes large amounts of resources, such as water and minerals, and produces diverse residuals that are dangerous to human health. A balanced use of AI is urgently needed, along with further policy changes and the creation of algorithms that consume fewer resources. In this part of the course, the impact of AI on the environment will be presented and discussed, along with alternatives for a sustainable future where AI plays a key role.

Activity

Participants will assume the roles of policymakers, CEOs, university rectors, and others to propose a balanced approach to AI with reduced environmental impact. Ethical considerations should be part of the plan.

Learning objective

After this class learners will be aware of the key aspects to consider when composing or reflecting upon policy for sustainable and ethical usage of AI utilization. Get familiar with standards such as ISO/EIC 42001, the first international standard for a responsible use of AI. And finally making decisions for a responsible use of AI, to mitigate energy use and emissions.

Dr Sarah Kustumastuti].

Contents

This part of the course utilize the context of public transport systems to provide an overview of how autonomous and intelligent systems have been utilized to enhance control over complex systems, optimize such systems and our transportation experience. We will examine the development of autonomous and agentic systems in various transport domains such as road, rail, aviation, maritime, etc. We will also take a critical look at how public acceptance and trust towards these systems evolve as they become more embedded in everyday transport. The talk will close out by prospecting on what it means for the future to incorporate automated systems in transport in a way that works for everyone.

Activity

The class will be split into 2-3 teams, each given a description of scenarios of the use of autonomous technology in transportation systems and will be asked to discuss and analyse potential problems and issues that may arise from the design and application of the system from a socio-technical point of view, and brainstorm ideas on to improve it.

Learning objective

After this class learners will be aware of key human factors elements to consider when designing integration of AI driven interfaces and automation for complex services.

[Dr Cesco Willemse]

Contents

This class will explore embodied AI in the context of social robotics. Through a lens of cognitive psychology, we will examine what interaction with robots can tell us about human behaviour and the brain, and how we can use this to inform and improve robotic design. People naturally and readily perceive robots as beings with intentions, rather than mechanical products. We will delve into questions such as what human-likeness means, how we attribute mental states, what the role of robotic eyes is herein, and whether these things matter. 

Activity

The second part will be performed as an open discussion on robots and their socialised application in the context of service delivery. Potentially with demonstration of robotics applications.

Learning objective

After this class learners will be able to reflect on potential application of embodied AI and social robots for future services.

[Dr Farrokhnia, Mohammadreza].

Contents

This session explores how Artificial Intelligence can complement human intelligence to create new forms of hybrid intelligence in education. We will discuss how AI can take on different roles in the learning process, automating routine cognitive tasks (externalization), modelling expert reasoning for learners to refine their understanding (internalization), and collaborating with humans to co-construct knowledge and understanding (extension).  

Activity

In the second part of the lecture, we will discuss together, the emerging forms of human–AI partnership, and how these invite us to rethink how learning happens, and how training or operators or hand-over of accumulated knowledge regarding a project  can be facilitated by AI but remain both intelligent and human-centered in an AI-driven world.

Learning objective

After this class learners will be able to reflect on usage of AI for training or handing over information within a company

[Dr Maximilian A. Friehs]. 

Contents

AI-driven agents (e.g., Chatbots) are presented as human-like replacements despite the technical problems (e.g., hallucinations, voice-interaction problems) and an industry has formed around marketing these aggressively aiming to replace human-human interactions (e.g., AI-based helpdesk agents, AI partners). However, AI-agents be helpful too when used conscientiously. We will discuss if and how Non-Player Characters as collaborators in games are now drifting into reality. We will explore if AI enhanced gaming can improve people skills and performance in the real world. Outside the gaming sphere, we will focus on AI-driven agents and their potential role in the digitally-mediated reality - what are positive and negative potential consequences? For example, we could reflect on how such systems might be used to assist the training process of first responders when negotiating with people in need. This discussion will examine the ethical considerations and design principles necessary to foster positive and engaging interactions. 

Activity 

In the second part of the lesson, we will reflect on and explore by a discussion into the future of player-NPC collaborators in real world. This may involve a text-based prototyping and hands-on experience.

Learning objective

After this class learners will be able to reflect on AI conversational systems and on the potential usage of such agents for gaming pleasure as well as guiding and training operators.

Participants are also invited to bring their own cases for discussion. The teaching format emphasises group work, peer interaction, and expert-guided discussion, ensuring an engaging and participatory learning experience. The programme is delivered entirely on campus and in English, potentially with live AI-generated subtitles in Dutch. There is no final exam; the learning experience is oriented around reflection, discussion, and applied reasoning.