Research Direction
Human-centred AI for cognitive load, stress, and adaptive learning.
My research interests are in human-centred AI, physiological machine learning, and adaptive learning systems. I am interested in how AI systems can model cognitive load, stress, and learning-related human states using lightweight, interpretable, and ethically deployable approaches.
Current Research Focus
I am currently exploring how physiological and behavioural signals can be used to understand cognitive load and stress in learning and work environments. I am especially interested in low-cost, practical, and user-facing systems that combine self-reported mental effort, behavioural interaction data, and optional wearable-derived signals.
The challenge is not only whether a model can achieve high accuracy, but whether it can generalise across people, adapt to individual differences, and support users without increasing cognitive burden.
🧠 + ⌚ + 🎓
Research Question
How can adaptive AI systems support learning while respecting human cognitive effort, stress, and individual differences?
Research Areas at a Glance
🧠
Cognitive Load
How mental effort changes across tasks, difficulty, confidence, and learning performance.
⌚
Physiological Signals
Exploring wearable-derived indicators such as sleep, HRV, resting heart rate, and stress-related signals.
🎓
Adaptive Learning
Designing learning systems that adapt pacing, review strategy, and difficulty based on learner state.
Research Themes
Cognitive Load and Stress Modelling
Understanding how physiological, behavioural, and self-reported signals can be used to estimate cognitive load, stress, and mental effort during learning or work tasks.
Personalisation and Individual Differences
Studying how AI systems can adapt to individual baselines, learning patterns, perceived difficulty, confidence, and stress response without requiring large amounts of personal data.
Lightweight Adaptive Learning
Designing adaptive learning tools that can recommend pacing, review strategy, task difficulty, and recovery intervals using simple, transparent, and low-cost algorithms.
Human-Centered AI Evaluation
Evaluating AI systems using metrics beyond accuracy, including cognitive load reduction, trust, usability, calibration, fairness, and practical usefulness.
Publications and Manuscripts
Manuscript / Preprint
Exploring Gender Differences in Cognitive Load and Stress Responses: A Machine Learning Approach using Physiological Data in Learning Environments
Status: Manuscript in preparation / under review
Role: Methodology, implementation, experiments, physiological data analysis, and initial manuscript drafting.
Conference Publication
Improvement on Finger Region Extraction for Hand-Waving Finger Vein Authentication
Venue: JSAP-OSA Joint Symposia, 2019
Research area: Biometrics, computer vision, and hand-waving finger-vein authentication.
Research Roadmap
2026
Research foundation, public notes, lightweight prototypes.
2027
Prototype, benchmark, reproducible experiments.
2028
PhD proposal, supervisor mapping, research statement.
2029
Applications, outreach, writing sample, final portfolio.
2030
Begin doctoral research in human-centred AI.
Open Collaboration
I am open to collaboration with researchers and practitioners working on human-centred AI, physiological computing, learning analytics, adaptive learning, affective computing, and responsible AI systems.
Arkaan Izhraqi 