During this research process, I was invited to give a talk at UX Graz. The invitation came at an interesting point in my work. I had spent months reading, I had a lot of material, many connections and a growing sense about the topic. At the same time, I was still exploring rather than defending one narrow thesis topic.
The talk became an opportunity to test whether the research could make sense outside my own notes and Zotero library.
I titled it Designing for the Interrupted: Notes on Attention and Adaptive Interfaces. I did not want to present it as a finished argument or a final solution. The aim was to share the direction of the research with a UX audience and to ask what we lose when digital systems treat every moment as equally interruptible.
The central thought behind the talk was simple: interruptions are not small interface events. They affect task continuity, emotional state, workload and the ability to return to something afterwards. A notification may only take a few seconds to read, but the cognitive work of resuming a task can last much longer. This is why the design question cannot only be whether an alert is visually clear or technically delivered. It also has to be whether it arrived at a moment when the user could reasonably deal with it.
To build this argument, I began with attention and flow. Flow is often used loosely in design conversations, sometimes as another word for engagement. What interested me in Csikszentmihalyi’s work was the structure behind it: clear goals, feedback, a relationship between challenge and skill and a sense of control over the activity. These conditions are easy to disrupt. An interface that repeatedly changes the user’s context can make concentration feel fragile even when every individual interruption appears minor.
From there, the talk moved through a series of research areas that had shaped my work. Interruption studies show that timing changes the cost of disruption. Cognitive-load research shows that people are not equally interruptible in every situation. Emotional-design research shows that interruptions can communicate pressure, urgency and social obligation rather than merely information. Memory and resumption research show that the real problem often begins after the interruption, when people have to reconstruct what they were doing.
I also wanted to avoid the common idea that this is a “short attention span” problem. Attention is not a fixed amount of time that every person has before they stop listening or working. It is contextual. It changes with task structure, motivation, environment, cognitive control and the behaviour of the system itself. The problem is not that users have become incapable of focusing. The problem is that many interfaces are built to claim attention whenever they can.
Notifications were the most recognisable example in the talk, but I tried not to make them the whole subject. They are only one visible form of interruption. A system can interrupt through sound, vibration, visual prompts, a change in automation, a request for approval or a recommendation that appears at the wrong time. This becomes even more important as interaction moves beyond a laptop screen into wearables, mixed reality and AI-agent systems.
The talk also gave me a way to discuss neuroadaptive interfaces without treating them as magic. Physiological computing and workload-aware interfaces offer an interesting interaction logic: a system can use signals from the body, behaviour or task performance to adjust its own behaviour. But this only makes sense if the system acknowledges uncertainty. It should not pretend that gaze data or EEG can reveal a person’s complete mental state. It should use partial signals carefully, explain what it has changed and let the user override it.
The final part of the talk connected this to current AI systems. AI agents create a new version of an old design problem. If an agent constantly asks the user for confirmation, it can become another source of interruption. If it acts independently for too long, it can create errors that are difficult to discover and correct. The human role cannot be reduced to pressing approve or reject. It has to include understanding what the system is doing and deciding when to step in.
Preparing the talk was valuable because it forced me to see the research as one larger landscape. It also made the limits of that landscape visible. The live presentation was research-rich, but it covered too much for a twenty-minute setting. The feedback afterwards was fair and consistent. People found the topic interesting, but they needed one concrete example or object to hold onto. The talk felt like several connected research directions compressed into one argument.
I do not see that feedback as a failure. It was a scope diagnosis. The research was not empty, but it was still too broad to communicate through one presentation alone. That distinction matters. A broad research map can be useful in the early stages of a project, yet it eventually has to lead to a specific situation where its ideas can be tested.
The strongest phrase I used near the end of the talk was “from capturing attention to caring for it.” I still believe that this is the right design direction. It does not mean that interfaces should never interrupt, automate or ask for something. It means they should recognise that attention has a cost and that the user should remain part of the decision about when that cost is worth paying.
The UX Graz talk gave me a public moment to articulate that position. More importantly, it gave me a reason to move forward differently. Instead of trying to make the next step another broad explanation of the field, I want to experiment with one concrete adaptive interaction loop. That is where the prototype begins.
Also linking the presentation here in case someone is interested: https://www.figma.com/deck/knB8wTii1xOntnhMrQMYgN
References
Adamczyk, P. D., & Bailey, B. P. (2004). If not now, when?: The effects of interruption at different moments within task execution. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 271-278). ACM. https://doi.org/10.1145/985692.985727
Altmann, E. M., & Trafton, J. G. (2002). Memory for goals: An activation-based model. Cognitive Science, 26(1), 39-83. https://doi.org/10.1207/S15516709COG2601_2
Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. Harper & Row.
Fairclough, S. H. (2009). Fundamentals of physiological computing. Interacting with Computers, 21(1-2), 133-145. https://doi.org/10.1016/j.intcom.2008.10.011
Iqbal, S. T., & Horvitz, E. (2007). Disruption and recovery of computing tasks: Field study, analysis, and directions. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 677-686). ACM. https://doi.org/10.1145/1240624.1240730
Lavie, N. (2010). Attention, distraction, and cognitive control under load. Current Directions in Psychological Science, 19(3), 143-148. https://doi.org/10.1177/0963721410370295
