Emotionally Adaptive UX Interfaces: A Scenario-Based Framework for Real-Time Personalization

Abstract

The increasing complexity of user needs and digital contexts necessitates the development of adaptive user interfaces capable of emotional responsiveness. Traditional emotional personalization methods, reliant on biometric data, often prove costly, intrusive, or impractical for early prototypes and large-scale deployments, raising privacy concerns. This paper addresses these limitations by introducing a novel conceptual scenario-based behavioral framework for emotionally adaptive web UX. The object of the study is to explore how user interfaces can dynamically adjust to a user’s affective state using only observable behavioral indicators, without physiological sensors. The study aims to demonstrate that cues like navigation style, input pacing, or reaction latency can inform UX modifications aligning with emotional states, offering a scalable and ethically sustainable alternative or complement to biometric solutions. The methodology involved developing three synthetic user personas (stressed, bored, focused) based on Plutchik’s Wheel of Emotions and validated behavioral attributes.  Interface mockups were designed in Figma, focusing on adaptive UX fragments. A structured heuristic evaluation, using a 5-point Likert scale and seven key metrics (e.g., perceived emotional fit, cognitive effort, mental model resonance) aligned with ISO 9241–210:2019 and ISO/IEC 25010:2023, assessed the framework. This work integrates insights from key researchers: Zeng et al. and Chen & Li on emotional congruence; Nielsen, Sarodnick & Brau on heuristic evaluation; Huang & Singh on emotional fit; Gentner & Stevens on mental models; and Liu & Wei and Khan & Shukla on emotion-aware computing. Results show that Hypothesis H1, affirming significant emotional alignment from scenario-based adaptation without real-time sensing, was validated. The stressed (4.6 emotional fit) and focused (4.7 mental model resonance) personas showed high alignment. Hypothesis H2, concerning behavioral adaptation’s sufficiency in low-tech contexts and its complementary role in high-fidelity designs, was also supported. Critically, the boredom scenario (low engagement 2.9) highlighted that overstimulation without guidance can disrupt mental models, suggesting the need for refined hybrid adaptation logic.  The findings confirm emotional responsiveness can be approximated via behavioral interface design, with hybrid systems offering dynamic fine-tuning. This framework introduces a vital “hybrid potential” for both substituting biometrics in constrained environments and augmenting them in high-stakes systems.