UXR PoV for Neuroinclusive Emotion Regulation
- URL: http://arxiv.org/abs/2605.31131v1
- Date: Fri, 29 May 2026 10:40:48 GMT
- Title: UXR PoV for Neuroinclusive Emotion Regulation
- Abstract summary: This paper introduces a Generative AI-augmented UXR methodology to support the design of emotionally intelligent digital ER interventions for adults with ADHD.<n>The approach integrates empirical evidence with established psychological frameworks Dialectical Behaviour Therapy (DBT), Self-Determination Theory (SDT), and the COM-B behavioural model.<n>The methodology is operationalized through a four-stage UXR process encompassing AI-supported hypothesis generation, foundational planning, insight generation via Building Blocks, and the construction of stakeholder-specific PoV narratives.
- Score: 3.6279953233728084
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Attention-deficit/hyperactivity disorder (ADHD) is a psychiatric disorder which presents itself in individuals through patterns of developmentally inappropriate levels of inattentiveness, hyperactivity, and impulsivity, with difficulties in decision making and emotional regulation (ER). Although digital and AI-based interventions have expanded access to ER support, many existing systems remain limited by weak theoretical integration, insufficient accommodation of neurodiversity, and a lack of structured user experience research (UXR) methodologies, that bridge psychological insight with design practice. This paper introduces a Generative AI-augmented UXR methodology, grounded in the UXR Point of View (PoV) Playbook, to support the design of emotionally intelligent and Neuroinclusive digital ER interventions for adults with ADHD. The approach integrates empirical evidence with established psychological frameworks Dialectical Behaviour Therapy (DBT), Self-Determination Theory (SDT), and the COM-B behavioural model and leverages Generative AI as a co-analytic tool to support synthesis, hypothesis formation, and design articulation. The methodology is operationalized through a four-stage UXR process encompassing AI-supported hypothesis generation, foundational planning, insight generation via Building Blocks, and the construction of stakeholder-specific PoV narratives. This process results in a set of ten theory informed UXR Play Cards that translate psychological mechanisms and empirical findings into actionable design guidance. The primary contribution of this work is a replicable, bias-aware framework for integrating Generative AI into UXR practice, advancing human-centred and Neuroinclusive approaches to digital mental health design.
Related papers
- From Evidence to Design: Developing an AI-Augmented UX Research Point of View for Digital Wellbeing in Emergency and Public Safety Contexts [5.241443752301431]
This paper investigates how User Experience Research (UXR) methods can be combined with AI-supported analysis to develop clearer design direction for digital wellbeing interventions.<n>This study applied an AI-supported literature analysis process to identify recurring psychological, behavioural, and design patterns.<n>Findings show that effective wellbeing systems for Emergency and Public Safety Personnel must minimise cognitive effort, adapt to operational context, and prioritise psychological safety.
arXiv Detail & Related papers (2026-05-29T10:53:57Z) - Developing an AI-Powered UX Research Point of View for Digital Health in A Regulatory Context: An Exemplar Case from MSM and Transgender HIV Care in Nigeria [5.025015307096222]
This paper introduces a Generative AI-augmented UXR methodology to guide the design of psychologically safe, low-cognitive-load digital health interventions.<n>The methodology is operationalised through a four-stage UXR process encompassing AI-supported hypothesis generation, foundational planning, and insight generation.<n>The output is a set of ten theory-informed UXR Play Cards translating psychological insight and empirical evidence into actionable design guidance.
arXiv Detail & Related papers (2026-05-29T10:47:44Z) - Predicting Neuromodulation Outcome for Parkinson's Disease with Generative Virtual Brain Model [95.41752463487008]
Parkinson's disease affects over ten million people worldwide.<n>Inter-individual variability limits empirical treatment selection, increasing non-negligible surgical risk and cost.<n>We bridge this gap with a pretraining-finetuning framework to predict outcomes directly from resting-state fMRI.
arXiv Detail & Related papers (2026-03-31T02:36:10Z) - Enhancing Psychologists' Understanding through Explainable Deep Learning Framework for ADHD Diagnosis [19.50016953929723]
ADHD is a neurodevelopmental disorder that is challenging to diagnose and requires advanced approaches for reliable and transparent identification and classification.<n>In this paper, an explainable framework based on a fine-tuned hybrid Deep Neural Network (DNN) and Recurrent Neural Network (RNN) is proposed for ADHD detection, multi-class categorization, and decision interpretation.
arXiv Detail & Related papers (2026-01-28T09:19:31Z) - Interpretable Neuropsychiatric Diagnosis via Concept-Guided Graph Neural Networks [56.75602443936853]
One in five adolescents currently live with a diagnosed mental or behavioral health condition, such as anxiety, depression, or conduct disorder.<n>While prior works use graph neural network (GNN) approaches for disorder prediction, they remain black-boxes, limiting their reliability and clinical translation.<n>In this work, we propose a concept-based diagnosis framework that that encodes interpretable functional connectivity concepts.<n>Our design ensures predictions through clinically meaningful connectivity patterns, enabling both interpretability and strong predictive performance.
arXiv Detail & Related papers (2025-10-02T19:38:46Z) - AI Models for Depressive Disorder Detection and Diagnosis: A Review [0.9012198585960441]
Major Depressive Disorder is one of the leading causes of disability worldwide, yet its diagnosis still depends largely on subjective clinical assessments.<n>In this paper, we present a comprehensive survey of state-of-the-art AI methods for detection and diagnosis, based on a systematic review of 55 key studies.
arXiv Detail & Related papers (2025-08-16T11:46:48Z) - Generative Cognitive Diagnosis [13.877294923915118]
We introduce a novel generative diagnosis paradigm that fundamentally shifts cognitive diagnosis from predictive to generative modeling.<n>We propose two simple yet effective instantiations of this paradigm: Generative Item Response Theory (G-IRT) and Generative Neural Cognitive Diagnosis Model (G-NCDM)<n>Our framework opens new avenues for cognitive diagnosis applications in artificial intelligence.
arXiv Detail & Related papers (2025-07-13T23:55:05Z) - CSBrain: A Cross-scale Spatiotemporal Brain Foundation Model for EEG Decoding [57.90382885533593]
We propose a Cross-scale Spatiotemporal Brain foundation model for generalized decoding EEG signals.<n>We show that CSBrain consistently outperforms task-specific and foundation model baselines.<n>These results establish cross-scale modeling as a key inductive bias and position CSBrain as a robust backbone for future brain-AI research.
arXiv Detail & Related papers (2025-06-29T03:29:34Z) - Neural Brain: A Neuroscience-inspired Framework for Embodied Agents [78.61382193420914]
Current AI systems, such as large language models, remain disembodied, unable to physically engage with the world.<n>At the core of this challenge lies the concept of Neural Brain, a central intelligence system designed to drive embodied agents with human-like adaptability.<n>This paper introduces a unified framework for the Neural Brain of embodied agents, addressing two fundamental challenges.
arXiv Detail & Related papers (2025-05-12T15:05:34Z) - A Knowledge-Driven Cross-view Contrastive Learning for EEG
Representation [48.85731427874065]
This paper proposes a knowledge-driven cross-view contrastive learning framework (KDC2) to extract effective representations from EEG with limited labels.
The KDC2 method creates scalp and neural views of EEG signals, simulating the internal and external representation of brain activity.
By modeling prior neural knowledge based on neural information consistency theory, the proposed method extracts invariant and complementary neural knowledge to generate combined representations.
arXiv Detail & Related papers (2023-09-21T08:53:51Z) - fMRI from EEG is only Deep Learning away: the use of interpretable DL to
unravel EEG-fMRI relationships [68.8204255655161]
We present an interpretable domain grounded solution to recover the activity of several subcortical regions from multichannel EEG data.
We recover individual spatial and time-frequency patterns of scalp EEG predictive of the hemodynamic signal in the subcortical nuclei.
arXiv Detail & Related papers (2022-10-23T15:11:37Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.