This paper introduces PONTE, a human-in-the-loop framework for generating personalized and trustworthy natural language explanations from AI systems. It employs a closed-loop validation and adaptation process to ensure faithfulness, completeness, and stylistic alignment with user preferences, mitigating common issues associated with large language models in Explainable AI.