Keynotes - JTELSS 2026

Scientists share a common drive for knowledge and innovation, fuelled by curiosity, logic and objectivity, using the scientific method to study the world empirically, critically evaluating results through peer review, and striving for systematic knowledge acquisition. Even though they may hold differing personal views on, for example, the social role of their work or the positioning of technology-enhanced learning. In short, it is methodological discipline, societal relevance and the fundamental drive to gather and verify knowledge that unite scientists worldwide. Research always forms the basis of innovation, whereby innovative behaviour can be described as the conscious development, introduction and application of value-adding innovations (such as new products, services, processes or working methods), involving curiosity, a willingness to take risks and a search for smarter, better ways of doing things, rather than clinging to the familiar. It is about purposeful innovation, focused on improvement and optimisation based on research and empirical evidence; it encompasses more than just the passive application of technology, and is more than just being creative; it is the actual implementation of new initiatives.

In this keynote, we take a light-hearted look at various scientific, technology driven perspectives that can lead to different forms of innovation. Central through this is the distinction between well-defined problems and ill-defined problems, which can lead to new insights by means of technology-enhanced learning. And perhaps, in the process, a childhood dream will be shattered!

The talk has two main parts: Generative AI for Learning Experience Design and Generative AI to Support Research. The first part is divided into four sections: (a) explicit prompting for LXD, which shows evidence-based prompting strategies and review tools for creating effective prompts; (b) implicit prompting affordances in specialized AI-powered educational platforms; (c) customized AI-course mentors; and (d) the pedagogical layer in general-purpose LLM systems prompts. The second part discusses how Generative AI (GAI) can enhance the research process. Topics include: accessing and synthesizing information from academic sources and professional networks; engaging in AI-mediated conversations to improve research questions and ideas; analyzing scientific papers to find areas for further investigations; helping with data analysis; and supporting the entire research workflow.

Serious Games and their application in education raise high hopes: we would like to take the motivation and engagement experienced in entertainment games and bring them to life in education. Yet, the amount of successful serious games is limited. The keynote goes through experiences from various serious game design and development projects and reports on lessons learned from their failing aspects (and also some successes). It shows some general aspects of serious game design and its different phases. In particular, we look into the complexities involved with designing serious games when it comes to bringing the creativity of game design together with the systematic aspects of learning design.

Artificial Intelligence is increasingly being integrated into educational contexts, including learning analytics, intelligent tutoring systems, and automated assessment. These technologies have the potential to support personalized learning, improve student outcomes, and assist educators in decision-making processes.

Despite these opportunities, many AI-driven educational systems operate as black boxes, making it difficult for students and teachers to understand how decisions are made. This lack of transparency can limit trust, hinder adoption, and reduce the effectiveness of AI-based support in real learning environments.

In this talk, I will provide an overview of Explainable Artificial Intelligence (XAI) in education, focusing on approaches that aim to make AI systems more transparent, interpretable, and user-centered. I will discuss how explanations can support different stakeholders, helping students better understand their learning processes and enabling educators to make more informed decisions.