The Evolution of Calm Technology: Measuring and Adapting AI UX/UI for Neurodiverse Users.
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How do we measure “calm” in AI-driven interfaces, and can we use real-time biometric data to tailor these solutions more effectively rather than relying solely on broad design principles?
This two-part question, raised by a reader in response to my earlier post, The Impact of Artificial Intelligence on the Neurodiverse Community: A Double-Edged Sword, prompted me to ask two more questions, which I’ll answer here:
1. If it’s possible to accurately assess a user’s cognitive state using biometric data and software algorithms, what are the implications of doing so?
2. Is “calm” really what should be measured?
How are people using biometrics in Calm Technology
AI-driven systems use real-time biometric feedback, such as EEG (electroencephalogram) readings, eye tracking, or heart rate variability, to dynamically adjust UI/UX elements and maintain a user’s optimal cognitive and emotional state.
How It Works:
Monitoring Attention & Cognitive Load
EEG sensors or other biometric tools detect stress, overload, or lapses in focus.AI Interprets Data
The system analyzes the user’s response and determines the best way to adjust the interface.Real-Time Adaptation
UI elements (such as brightness, contrast, text density, or interaction style) dynamically shift to maintain an ideal user experience.
These adaptive interfaces are already being utilized in various industries, particularly healthcare and financial services.
Healthcare
In healthcare, wearable devices like the Emotiv Insight EEG headset monitor neurological responses, helping AI-driven mental health platforms dynamically adjust their recommendations and therapeutic strategies in real time. Biometrics are being integrated into mental health and accessibility tools. Some examples are below.
EEG-based Mental Health Support
Wearables like Muse detect brain activity and adapt real-time meditation guides to enhance relaxation. AI-driven Mental Health Apps like BioBase and Neurum Health utilize biometric data to assess stress levels and adjust their UI or communication tone accordingly. BioBase integrates heart rate variability (HRV) monitoring to offer personalized stress management interventions, while Neurum Health uses AI-driven behavioral insights to tailor mental health support. Another company that was making progress was Mindstrong, which closed its doors in 2023.Biofeedback for Autism & ADHD
Neuroadaptive learning tools adjust teaching methods, screen brightness, or sensory input to maintain engagement and reduce cognitive overload.
Finance
Similarly, In financial services, companies like Nasdaq have started exploring the use of biometric monitoring to track traders’ stress levels, allowing for adjustments in workload distribution or interface simplifications to prevent cognitive overload. The concept areas I have learned about so far are.
Trader and Analyst Environments: EEG or eye-tracking software could identify cognitive fatigue or overload, prompting the system to simplify data visualizations, minimize distractions, or recommend breaks.
Algorithmic Trading & Risk Assessment
EEG-based insights can assist traders in managing emotional decision-making, thereby preventing impulsive or overconfident financial choices.Client-Facing AI Assistants
AI-driven financial planning tools could adjust their interaction style based on a user’s stress indicators, transitioning from data-heavy responses to simpler explanations when cognitive overload is detected.
Balancing Passive Measurement with Ethical UX Design
While the potential for biometric-driven AI is compelling, it also raises critical ethical concerns:
Privacy & Consent — Continuous EEG tracking could feel intrusive. Users must have control over data collection and clear opt-in mechanisms.
Accuracy & Individual Differences — Not all stress responses are the same. AI should learn from user preferences rather than applying generic assumptions.
Avoiding Over-Automation — Users should always be able to manually adjust settings, ensuring AI remains a supportive tool rather than an overbearing force.
A successful implementation of biometric-driven AI would require transparent data use, strong ethical safeguards, and user control over AI-driven adjustments.
The Future of Adaptive AI for Neurodiverse Users
Integrating real-time biometrics into AI-driven interfaces could redefine what “calm technology” means for neurodiverse individuals. By measuring attention levels and cognitive load, AI could create truly personalized experiences that enhance accessibility while respecting individual needs.
However, to realize this vision, developers must prioritize ethical considerations, privacy protections, and adaptive controls that allow users to fine-tune AI interactions. By taking a thoughtful approach, we can ensure that AI remains a powerful ally, one that promotes cognitive ease, enhances accessibility, and respects the diversity of neurodivergent experiences.
As for measuring calm…
The Challenge of measuring “Calm” in AI UX/UI
If our goal is to support a person’s ability to complete a desired task, how does stress or remaining calm play into accomplishing that goal? If calm is merely the absence of stress, we must be careful because stress is not inherently negative — it depends on the amount, duration, type, and situation.
Before assuming that reducing stress is beneficial, we must evaluate whether the AI is adapting to genuinely support the user’s performance rather than making overly generalized assumptions about well-being.
It could be that “Calm” is a suitcase word, and I have brought it along, so to speak, as I have been fascinated by the design concepts within the calm technology principles. We need to better define what we’re trying to measure to support all the neurotypes so that, by leveraging AI, everyone can access technology.
Final Thoughts
The conversation around AI and neurodiversity is evolving. What do you think about real-time adaptive interfaces? Would you feel comfortable using biometric-driven AI, or do you see potential risks? Let’s continue this discussion — leave your thoughts in the comments!
Reference: Calm technology is based on the idea that technology should serve us rather than demand our constant attention. The term “calm technology” was coined by Mark Weiser and John Seely Brown in 1995 when they published a paper called The Coming Age of Calm Technology. Since then, Amber Case has taken it further, developing the concept to fit the current day and age and creating a set of calm technology principles.
The principles of calm technology are straightforward yet powerful:
Technology should require the smallest possible amount of attention.
It should inform and empower people.
It should work even when it fails.
Technology should amplify the best of technology and the best of humanity.
In essence, calm technology is a design language. It’s about designing technology that respects our attention and mental space, creating contextual experiences that allow us to interact with the technology when necessary and forget about it when we don’t need it. This is a sharp contrast to Interruptive Technology, which constantly seeks to be the center of attention and disrupts our daily lives with unnecessary notifications and distractions.