Conversational interfaces, including text-based chatbots and voice user interfaces, are widely used in healthcare, education, and customer service domains. However, they often rely on static affordances and signifiers that limit adaptability and user engagement. Existing systems struggle to evolve with user expertise, leading to discoverability, trust, and long-term usability issues. This paper proposes a theoretical framework that redefines affordances and signifiers as adaptive constructs, ensuring that interactions dynamically adjust based on user experience and context. The framework structures affordances into three levels—exploratory, learning, and proactive—while categorizing signifiers as explicit, contextual, and minimalist. By integrating these elements with the UX Honeycomb model, the framework provides a structured method for designing conversational interfaces that align with established user experience principles. A heuristic evaluation of Amazon Alexa demonstrates the framework’s applicability, revealing that while Alexa effectively guides initial interactions, its affordance evolution remains primarily user-driven rather than system-driven. The analysis highlights key challenges in discoverability and engagement, emphasizing the need for structured affordance progression and enhanced contextual signifiers. This framework offers researchers a foundation for empirical studies on adaptive interaction models and provides developers with actionable insights for improving conversational system design.