Speakers

Learners are introduced to responsible use frameworks, UNESCO and EU guidelines on AI in research, and practical tools for data analysis, translation, and discovery. The session emphasizes both the potential of AI to accelerate Open Science and the importance of transparency, integrity, and fairness when adopting AI tools in academic and research practice.

Drawing from pioneering projects developed at UPT, Professor Holotescu will demonstrate how decentralized identity and digital diplomas are redefining academic integrity and student mobility across Europe.

The introduction of genAI tools has clearly exposed long term inadequacies of some strongly established educational traditions, in particular approaches to grading student learning. These new tools provide everyone with an unprecedented power to generate increasingly sophisticated outputs in the form of text, images, and music, and more recently video and sound recordings. These outputs are produced in response to “prompts”, and the complexity and nature of the prompts that can be crafted has been the subject of a new area of professionalism “prompt engineering”. This phrase overstates the dignity of the prompting activity, it is not a scientific activity or field of study, rather it is a craft that provides generative AI tools with sufficiently nuanced context so as to improve the usability of the output. Since these tools have proven capable of substantially amplifying skilled human performance levels on qualified tasks, and in some cases automating human work in well constrained domains, their integration into professional work has been rapid. Higher Education Institutions would be very unwise to ignore this trend. This talk provides an overview of how this situation has emerged, an analysis of the capabilities and implications of widespread genAI adoption, an overview of the applications of these tools in educational settings and possible avenues for developing the highly relevant education future learners will demand of us. The intention of the talk is to provide a firm research foundation upon which the participants can engage in a nuanced discussion of how the education sector should respond to generative AI and agentic systems.

This shift creates a fundamental challenge to educational institutions regarding what and how to teach and to evaluate. This rises four questions: what to teach (in order for the graduate to excel in a job that probably will use AI extensively), how to teach (using AI as a tool to support instructors and students in their teaching and learning efforts), how to evaluate (in order to avoid misconduct and fraudulent practices), and what to evaluate (what skills and knowledge should be assessed). No final recipes can be given, especially when the field is evolving at exponential pace. Prompts, guardrails, skills, agents, computer use, …, new affordances emerge daily introducing an instability that makes planning very difficult. Given the current scarcity of established best practices, this presentation tries to identify some basic principles and ideas to navigate this environment, one that is both exciting and chaotic.

Assignments are increasingly unreliable indicators of true understanding and learning. Proper assessment of such assignments becomes increasingly challenging.

This is particularly important, in the context of online education, where there’s no way of monitoring the students during the work on such tasks without severe privacy intrusions, but also in regular on-campus contexts, where students also often finish their projects at home rather than during their on-campus stays.

Drawing from teaching experience, the session will explore effective strategies for assessing authentic projects and collaborative work, with special attention to distributed online classrooms. Key challenges to be addressed are, how to assess individual contributions within team projects, ways to evaluate learning when AI tools are ever-present, and designing assessments that focus on processes rather than products.

In an age where artificial intelligence is reshaping how knowledge is produced, trusted, and used, teaching and learning specialists are being called to reassess their pedagogical foundations.

“Agentic” AI systems can independently browse and take action without direct human input. The rise of synthetic research is straining peer review systems, and “ghost learners” are appearing in classrooms. Amid both excitement and concern, education leaders around the world face pressure to act quickly, often with limited evidence to guide their decisions. 

This keynote examines the frictions AI generates across curriculum, assessment, teaching and learning practices and governance, while highlighting the need to protect the public purpose of education. Drawing on the founding vision of MOOCs and open education, the keynote also considers new frontiers:  from public interest AI and digital sovereignty to community-led and human-centred models of learning. Ultimately, it calls for planning education that is ethical and inclusive, where human agency remains the primary architect of educational transformation.