Large language models (LLMs) are getting impressively good at generating human-like text, but can they truly understand the nuances of emotional responses in conversation? New research delves into this question, exploring whether we can teach AI to plan socio-emotional strategies, like expressing happiness or offering sympathy, before responding in a dialogue. Researchers built a system with two key modules: a 'planner' that predicts the appropriate socio-emotional strategy for a given conversation turn, and a 'generator' that uses this plan to craft its response. They then meticulously evaluated these AI-generated responses using both automated metrics and, critically, detailed human evaluations focused on consistency, fluency, and emotional adequacy. The results? While there's still a gap between AI and human performance, conditioning LLMs on socio-emotional strategies demonstrably improves the quality and appropriateness of their responses. This work reveals that explicitly teaching AI about social and emotional cues holds significant promise for future chatbot development, moving us closer to more human-like and helpful AI conversational partners. However, the study also highlighted the limitations of existing automated metrics. Traditional metrics often struggle to evaluate the subtleties of emotional expression, emphasizing the need for robust, human-centered evaluation methods for this complex aspect of AI communication. This opens up an exciting avenue of research, focused on bridging the gap between AI's growing linguistic capabilities and its understanding of human emotions.