Keynotes

Cognitive ergonomics, human-computer interactions and related design-oriented disciplines counterbalance the dominance of technology-driven approaches by focusing on users and their needs. In my talk, I will use the concept of design space to discuss existing views of users and designer-user relationships.

Design spaces typically support a product-centered perspective. However, we currently experience a massive introduction of new digital artifacts in ever shorter cycles and it is difficult to cope with unintended or negative effects if they are already massively used by a majority of people. I will argue that design spaces need to be complemented by experience spaces containing descriptions of experience-related aspects of artifact use that promote critical co-reflection.

The integration of Extended Reality (XR) and Generative AI introduces new demands on cognitive processing, attention, and emotional regulation. This keynote examines how AI-enhanced XR environments affect user behavior and cognition, focusing on both spontaneous practices in social virtual spaces and findings from controlled affective computing studies.
Examples from recent fieldwork in Social VR—such as self-imposed muteness, moderation behavior, and phantom touch—illustrate how users adapt to ambiguous, stimulus-rich environments. These are contrasted with experimental data on avatar expressivity, haptic feedback, and the cognitive effects of emotionally responsive AI agents.

The analysis addresses emerging risks related to social influence, affective overload, and identity confusion, with implications for user autonomy and mental well-being. Design strategies are discussed to mitigate these risks and promote sustainable cognitive engagement.

The presentation bridges ecological observation and experimental validation to support cognitive ergonomics research on AI-mediated interaction, emphasizing the need for human-centered design in increasingly immersive and intelligent systems.

The intersection of multimodal data and advanced computational analyses has the capacity to improve our understanding of how humans learn and provide novel affordances that enhance our learning, such as affective and embodied learning. Multimodal data coming from learners’ mobile devices, wearables, and other ubiquitous devices not only offer new ways to detect humans’ learning experience, but also enable powerful learning technologies and interfaces (via AI and ML algorithms). In this talk, I will present methods and studies, and initial results on multimodal learning analytics (MMLA). Moreover, I will discuss the inherent connection of MMLA with AI, the potential implications of putting MMLA technologies into practice, and the challenges connected with ethical and methodological aspects. Lastly, I will discuss the emerging paradigm of hybrid intelligence (HI), its overarching objective to bridge the gap between human intelligence and what new sociotechnical ensembles can be developed in HI contexts.