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This research adopts a human-centered perspective in which artificial intelligence is conceptualized not as a replacement for human cognition, but as an adaptive partner that supports decision-making while minimizing unnecessary cognitive burden. Generation X knowledge workers, typically defined as individuals born between 1965 and 1980, represent a critical yet underexplored demographic in Human–Computer Interaction and artificial intelligence research. Although this cohort occupies a large share of managerial and decision-making roles, they increasingly interact with AI-driven environments not designed with their cognitive or ergonomic profiles in mind, contributing to elevated technostress, cognitive overload, and perceived loss of control. This paper proposes a Human-Centered AI framework that integrates Explainable Artificial Intelligence (XAI) and Adaptive User Experience (AUX) design to mitigate technostress and improve digital wellbeing. The system combines machine learning-based cognitive state inference with real-time interface adaptation and multi-layered explanation mechanisms, including SHAP, LIME, counterfactual reasoning, and natural language generation. A mixed-methods design was adopted, combining structural equation modeling (PLS-SEM) with a controlled within-subject experiment, complemented by a pilot validation with 28 Generation X participants assessing feasibility, usability, cognitive load reduction, and perceived transparency. Despite rapid advances in AI, little attention has been devoted to designing systems that actively support users' cognitive and psychological wellbeing rather than simply optimizing efficiency. This study shifts the focus from technology-centered innovation toward human-centered AI, proposing that intelligent interfaces should function not only as decision-support tools but also as mechanisms for reducing technostress, preserving human cognitive resources, and promoting sustainable digital work environments.