Recent advances in AI-enabled systems and machine learning applications across multiple sectors have highlighted the critical need for robust, tailored frameworks to enhance system usability and trustworthiness. In the healthcare domain, where verifying methods and outcomes is paramount, establishing explainability requires a comprehensive, systemic approach that balances depth with appropriate abstraction levels. The qualities of explainability and interpretability are central to ensuring that AI technologies are both user-friendly and reliable, thereby playing a key role in technology acceptance. In this chapter, we introduce a novel framework for defining explainability requirements in AI-driven systems by integrating the principles of the Technology Acceptance Model (TAM). Our framework leverages targeted machine learning techniques-including hierarchical clustering and k-means, among others-to develop a user model that supports multi-layered, personalized explainability. Furthermore, it incorporates a rule-based mechanism that adjusts the level of trustworthiness based on user perceptions and their proficiency with AI. We demonstrate the application of this framework in the context of AI-powered medical systems, with the objectives to (1) evaluate and quantify physicians’ familiarity with technology and AI, (2) create tailored layers of explainability that correspond to individual user needs and trust levels, and (3) foster an environment that promotes transparency and validation. To assess doctors’ technological proficiency, we employ the Rapid Estimate of Adult Literacy in Medicine (REALM), a tool widely used in healthcare to improve communication between clinicians and patients.