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Plenary lecture
Plenary lecture
Igor Perko
Faculty of Economics and Business, University of Maribor, Slovenia
Faculty of Economics and Business, University of Maribor, Slovenia
Feeding collective intelligence: interaction observations
Feeding collective intelligence: interaction observations
Generative intelligence creates new opportunities for genuine collective intelligence, where information from diverse sources can be combined to support human reasoning. A central obstacle remains: most research data is not produced in formats suitable for AI systems. Given that we are still operating in largely uncharted territory, we can experiment. This keynote proposes formatting research outputs to make them directly useful for collective intelligence and introduces interaction observations as an AI-oriented reporting technique. Drawing on CyberSystemic foundations, interaction observations offer a semi-structured method for documenting second-order, subjective reflections on interactions. When conducted systemically, it helps connect multiple perspectives on the same interaction or reveal parallels between interactions that initially appear unrelated, potentially shifting how we understand the world around and within us. Simple solutions work best with intelligent support.
Marija Boban
University of Split, Faculty of Law, Croatia
University of Split, Faculty of Law, Croatia
Artificial Intelligence and Personal Data Protection: Key GDPR Considerations
Artificial Intelligence and Personal Data Protection: Key GDPR Considerations
The rapid development and widespread adoption of Artificial Intelligence (AI) technologies have created significant opportunities for innovation across various sectors. However, the extensive use of personal data in AI systems raises important concerns regarding privacy, transparency, and compliance with data protection regulations. This lecture will provide an overview of the key requirements of the General Data Protection Regulation (GDPR) as they apply to AI applications. The topics to be covered include lawful data processing, transparency, accountability, automated decision-making, profiling, and data subjects' rights. Particular attention will be given to the challenges that organisations will face in ensuring GDPR compliance while implementing AI systems. The lecture will also highlight best practices for responsible and ethical AI development and use, emphasizing the importance of balancing technological innovation with the protection of fundamental privacy rights.
Irena Kondratenko
Irena Kondratenko
Independent Expert, Horizon Europe strategist and evaluator, Belgium
Independent Expert, Horizon Europe strategist and evaluator, Belgium
Beyond the Perfect Proposal: Lessons from Using Generative AI in Horizon Europe Applications
Beyond the Perfect Proposal: Lessons from Using Generative AI in Horizon Europe Applications
Generative AI is rapidly transforming the way Horizon Europe proposals are developed, written, and evaluated. From analysing call topics and identifying consortium partners to drafting proposal sections, refining narratives, and supporting project coordination, AI tools offer unprecedented speed, linguistic precision, and efficiency. The presentation will share lessons learned from practice and highlight why AI should be treated as a collaborator rather than an author. It will discuss the growing importance of prompt design, human oversight, domain expertise, and implementation credibility in an environment where many proposals benefit from similar AI-assisted advantages. Ultimately, the session argues that successful proposals will continue to depend on the combination of human judgement, scientific excellence, strong partnerships, and strategic use of AI tools.
Ana-Marija Stjepić
Ana-Marija Stjepić
University of Zagreb, Faculty of Economics and Business, Croatia
University of Zagreb, Faculty of Economics and Business, Croatia
From Data to Meaningful Ideas: Finding Signal in the Age of Noise
From Data to Meaningful Ideas: Finding Signal in the Age of Noise
In an environment characterised by information abundance, the central challenge is no longer access to data, but the ability to distinguish relevant signals from noise and transform them into meaningful ideas. Analytical outputs do not automatically produce useful insights. Their value depends on data quality, methodological transparency, contextual understanding, and the ability to communicate results clearly and responsibly. The lecture will illustrate the process through which raw data are converted into information, interpreted as knowledge, and ultimately developed into ideas that can guide research, business decisions, and societal innovation. It will also address the risks of algorithmic bias, information overload, spurious patterns, and uncritical reliance on automated tools. The key message is that meaningful insight emerges from the interaction between data, analytical technology, human judgement, and creative thinking.
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