BIP – AI-Driven Service Innovation Summer School 2025

The BIP Summer School 2025, focusing on AI-Driven Service Innovation, offers an engaging exploration of how artificial intelligence shapes modern service systems. Through a combination of online lectures, practical workshops, and inspiring excursions, participants will delve into topics such as responsible AI, recommendation systems, smart service design, and service complexity. This program is a blend of theory and real-world application, providing valuable insights for students keen to embrace the transformative potential of AI in service innovation.
(text generated by AI, of course 😊)

Main organiser: Masaryk University

Participating: University of Salerno, University Polytechnica Bucharest, Deggendorf Institute of Technology

Location of physical mobility: University centre Telč (Univerzitní centrum Telč)

Key Dates

Online lectures: June 16–20
Physical mobility: June 29 – July 5
In-person teaching: June 30 – July 2
Excursion day: July 3 (Potential options: Kyndryl, KYPO, Žďár nad Sázavou – to be confirmed)
Project presentations: July 4

Number of students we need: 20 from non-MUNI universities is the optimal

Conditions for students:

Available for master's students and senior bachelor's students (from the 5th semester and more)
English on B-level
If the maximum number of students is reached, a motivation letter may be required.

Process of nomination

Via the link provided by the Erasmus office

Topics & Lecturers

Online (18 hours in total)

Prof. Monica Dragoicea: Responsible AI in Service Design (4 hours)
Today we want to see AI adopted more widely, as it can bring significant benefits to society, companies, and individuals. One key advantage is the increased automation of tasks. However, for AI to be successfully adopted, we must build trust in its reasoning processes. Creating this trust is essential. According to the latest EU regulations, AI systems must meet three key requirements: Compliance with the law; Robustness and reliability; and Trustworthiness. The third element, trustworthiness, is more abstract and difficult to define. It's a somewhat fuzzy concept, and we need to identify the factors that help us evaluate how trustworthy an AI system truly is. This lecture addresses specific principles of responsible innovation and trustworthy AI in smart, data intensive service design. Specifically, it will explain how trustworthy AI integrates with the AI and analytics life cycle and the data supply chain, how to identify unwanted biases throughout the AI and analytics life cycle, and how to understand the principles of responsible innovation in smart service design.
Prof. Mouzhi Ge: Recommender Systems and AI in Services (2 hours)
This session provides a conceptual and foundational overview of recommender systems and the role of AI in modern service industries. We'll begin with the evolution of recommender systems, covering key paradigms such as collaborative filtering, content-based methods, and hybrid approaches. Alongside technical foundations, we'll work on the strategic role of AI in service personalization, decision support, and customer engagement. We also touch base some use cases like e-commerce, and healthcare. This lecture is designed for participants who want to build a solid theoretical ground in AI-driven recommendation and service systems. You would expect a mix of technical insights and real-world context, which is to help you understand why these systems matter, how they work and the broader implications of embedding AI in service environments. No deep coding is expected. This lecture is about principles, models, and mindset.
Prof. Luca Carrubbo: Smart Service Systems (2 hours)
An overview on Smart Service Systems’ design and management will be taken, with a specific focus on practical evidence in real life about how (and how much) versatile, functional, scalable they can be. A comparison on different strategies and operations performed by competitive firms nowadays helps in highlighting special features of their typical reactive, proactive, adaptive and dynamic approaches. Finally, an analysis in depth of ongoing interactions among entities in a (smart) service eco-system, as consequence of all of that, completes the scientific framework.
Prof. Leonard Walletzký: Service Complexity, Smart Service Design and AI (4 hours)
This session delves into the intricacies of service complexity and the principles of smart service design. Participants will explore how AI can be leveraged to enhance service design, focusing on practical applications and theoretical foundations. Topics include the role of AI in optimizing service processes, improving customer experiences, and driving innovation in service industries. The session aims to provide a comprehensive understanding of how AI can be integrated into service design to create resilient and smart service systems.

In-Person Workshops (18 hours)

Prof. Monica Dragoicea: Responsible AI in Service Design (4 hours)
Building responsible AI systems requires a well structured approach from principles to actionable insights and practices. A few of the factors that contribute to the model’s trustworthiness include fairness, robustness, interpretability, and explainability/ interpretation. A trustworthy AI model will be more easily accepted by end-users and industries. This lecture will demonstrate how to apply the principles of human-centricity, inclusivity, accountability, privacy & security, robustness, and transparency to real-world scenarios in smart service design. It will help participants to identify how modern technologies may address unwanted bias and innovate responsibly in data management, model development, and model deployment. This session will bridge theory with practice, giving participants the possibility to engage with hands-on exercises, interactive demos, using Fair AI Tools, to evaluate and mitigate data and model bias in smart service design. Basic Python programming background is a plus, and a visual low code/no code perspective will be explored as well.
Prof. Mouzhi Ge: Recommender Systems and AI in Services (4 hours)
In this hands-on session, we’ll explore how to build, evaluate, and fine-tune AI-driven recommender systems using real-world datasets and modern tools. The first part will walk through basic implementation steps using Python libraries to build a movie recommender system. Participants will learn to prepare data, choose recommendation strategies, and evaluate results using metrics such as precision, recall, and F1. We'll then explore service applications, simulating real-world scenarios like dynamic pricing, personalized interfaces, and AI-driven chatbots. This lecture focuses on bridging theory with practice, giving participants the skills to prototype and experiment with AI-powered service solutions. You’ll engage in guided exercises, short coding sprints, and interactive demos. This lecture is ideal for those who are with a basic Python programming background. Whether you're aiming to build better user experiences, optimize customer journeys, or simply get a feel for recommenders of Netflix or Amazon's recommendations, this session is where theory meets reality.
Prof. Luca Carrubbo: Smart Service Systems (4 hours)
Multiple case studies, R&D projects, will be explained and shared with students, in order to make evidence on how Smart Service Systems work, how technologies, organizations, information sharing and people act together, how they co-create value. In this sense, a systems approach helps in understanding how to analyse weak signals surrounding, to manage uncertainty, to make decisions under pressure, to power interactions with partners, to gain such advantage from smartness in complex situations (in terms of specificity, measurability, concordance, realistic and timely responsiveness of proposed solution), with a number of implications in innovative processes and innovation management.
Prof. Leonard Walletzký: Service Complexity, Smart Service Design and AI (4 hours)
This session focuses on the practical applications of AI in smart services, highlighting real-world examples and case studies from cities, municipalities, and similar environments. Participants will explore how AI technologies are being utilized to enhance urban mobility, improve public services, and create smarter, more efficient cities. The session will cover various AI-driven solutions, such as intelligent transportation systems, smart traffic management, and predictive maintenance for public infrastructure. Through detailed case studies, participants will gain insights into the challenges and successes of implementing AI in different urban settings, providing a comprehensive understanding of the potential and impact of AI in smart services.