Continual User Intent Learning for Adaptive Human–AI Interaction in Long-Term Conversational Systems
Keywords:
continual learning; user intent; human–AI interaction; conversational systems; adaptive systems; memory architecture; governance; fairness; sustainabilityAbstract
Long-term conversational systems increasingly mediate complex activities in health care, education, finance, customer service, and personal assistance. Static intent classifiers trained on fixed datasets cannot adequately support interactions in which user goals, preferences, constraints, and domain knowledge shift over months or years. This paper presents a system-level analysis of continual user intent learning for adaptive human–AI interaction. It examines architectural strategies that combine foundation-model representations with persistent user memory, episodic retrieval, and controlled model updating. The analysis argues that continual intent learning is not only a machine learning problem but also an infrastructure design and governance problem. Three structural tensions organize the discussion: the need for adaptation against the need for stability, the value of personalization against privacy obligations, and the ambition of automation against the requirement for meaningful human oversight. The paper considers memory lifecycle, intent drift, alignment, fairness, robustness, deployment constraints, evaluation regimes, and policy implications. It further compares conversational systems across domains to illustrate how organizational capacity and contextual harm shape acceptable adaptation. The paper concludes that sustainable adaptive conversational platforms require continual intent learning to be embedded within accountable sociotechnical infrastructure, with feedback loops that connect model behavior to institutional review, user control, and long-term evaluation.
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