The emergence of Large Language Models (LLMs) has disrupted traditional automated programming assessment methods, as students can now generate correct code without truly comprehending the underlying concepts. Researchers have responded by exploring alternative evaluation approaches, including chatbot-based assessments that gauge code understanding through conversational interactions. A recent study conducted a thorough review of existing conversational assessment methods in programming education, identifying three primary architectural frameworks1. This investigation highlights the need for innovative assessment strategies that can accurately measure programming skills and knowledge in the presence of advanced AI tools. The findings of this study have significant implications for educators, policymakers, and industry leaders, as they underscore the importance of developing effective evaluation methods that can distinguish between human understanding and AI-generated code, so what matters most to practitioners is the ability to design assessments that can reliably validate programming proficiency in an AI-driven landscape.