Research on LLM s-Empowered Conversational AI for Sustainable Behaviour Change (2025)

Abstract: This is a position paper for my research, including my research interests, my views about Spoken dialogue system (SDS) research and suggested topics for discussion.

## spoken dialogue system (sds) research over the next 5 to 10 years, spoken dialogue systems (sds) are expected to evolve from task-oriented systems to more context-aware, emotionally intelligent, and personalized agents @xcite . with rapid advances in llms, multimodal learning, and memoryaugmented systems, conversational systems will become more fluent, more adaptable, and capable of long-term, relationship-oriented interactions in a range of applications, from healthcare and education to mental health and productivity together with sustainability. this generation of young researchers is well-positioned to tackle some of the challenges in the field: enhancing dialogue coherence over extended turns, improving commonsense and emotional reasoning, and developing systems that are not only efficient but also ethically responsible and transparent. the integration of behavioral science -particularly in behavior change support systems -is a promising direction because it opens up opportunities for conversational agents to facilitate real-world impact. key questions to enable this research are: for users, reliability, trustworthiness, and contextual relevance remain top priorities. systems must not only understand the user's intent but also be able to adapt to the user's preferences and emotional state over time. ## suggested topics for discussion there are a few critical considerations regarding the limitations and potential risks of using off-the-shelf llms for conversational ai to support behavior change. firstly, current llms will not retain memory of past interactions across sessions. as a result, they cannot track a user's behavior change journey over time or personalize support based on long-term context. secondly, although emotion detection techniques have advanced, the emotional intelligence of off-the-shelf llms remains shallow. these models often respond based on surface-level sentiment cues rather than deep contextual understanding, which can lead to generic or even inappropriate responses in emotionally sensitive situations. moreover, coherence between turns also has some limitations. offthe-shelf models may generate contextually disconnected responses, which may reduce trust, disrupt engagement and impede behavior change process. importantly, transparency and trust in behavior change counseling delivered by llms should be addressed, given the inherent opacity of their decision-making processes. hence, in order to address these limitations, the following topics are proposed for further discussion.