Research team
Expertise
My research expertise broadly focuses on educational technology and AI in education. While general-purpose AI tools are increasingly being used in educational contexts, I am particularly interested in the promise of AI-supported systems that are specifically designed for education, developed for clearly defined pedagogical purposes, and informed by learner and contextual data. My research examines how such systems can be designed and integrated in ways that are meaningful, responsible, and accepted by multiple stakeholders in education, including teachers, learners, researchers, and policy actors. Much of this work is situated within the broader spectrum of hybrid intelligence, where AI systems are designed to complement rather than replace human expertise. In this respect, my research builds on principles from human-centred AI and human-centred design, with attention to teacher autonomy, transparency, trust, pedagogical relevance, and responsible implementation. A central question guiding my work is how educational AI systems can support decision-making while keeping educators meaningfully involved in interpretation and action. This focus is closely connected to my work on learning analytics and multimodal data. Educational AI systems increasingly rely on rich data about learning processes, including verbal and non-verbal data, video data, log-data, eye-tracking data, and other indicators. I use these data sources to better understand how learners engage with content, interact with others, collaborate in teams, and respond to technology-supported learning environments. One current use case I am working on is an AI-enhanced teacher tool that uses eye-tracking data to support teachers in understanding and responding to students’ reading comprehension processes. A second important line of research concerns the use of educational technology and learning analytics in collaborative learning environments. In this area, I focus on computer-supported collaborative learning, including computer-supported collaborative problem solving and computer-supported collaborative writing. My work examines how team processes can be captured, analysed, and made visible through collaborative learning analytics, with the aim of supporting both research and educational practice. In addition, I study flow and team flow as important variables in learning, work, collaboration, and technology use. I examine how optimal experiences emerge, how they relate to individual characteristics and team dynamics, and how they may be supported in educational and technology-mediated contexts. I am also a member of the European Flow Researchers Network, which connects researchers studying flow across different domains and contexts. Methodologically, I combine quantitative, qualitative, and mixed-methods approaches, including observational research, content analysis, design-based research, validation studies, systematic literature reviews, and meta-analyses. This methodological combination allows me to study educational technology, AI-supported learning environments, and learning processes from both data-driven and theory-informed perspectives.