Human–Computer Interaction Optimization through LLM-Based Cognitive Modeling and Personalized Dialogue Memory
Keywords:
Human-computer interaction, large language models, cognitive modeling, personalized memory, dialogue systems, system architecture, fairness, governanceAbstract
The rapid evolution of large language models has transformed human–computer interaction, yet persistent challenges remain in aligning system behavior with user intent, context, and long-term preferences. This paper presents a system-level examination of how cognitive modeling and personalized dialogue memory can be integrated into LLM-based interactive systems to optimize human–computer interaction. We argue that treating interaction as a continuous cognitive process, rather than a series of stateless exchanges, requires architectures that embed working memory, episodic recall, and user-specific semantic representations directly into the inference loop. By analyzing structural trade-offs across centralized and federated memory designs, latency-sensitive retrieval mechanisms, and adaptive personalization engines, the paper maps the design space for scalable, robust, and fair cognitive dialogue systems. We explore the interplay between memory decay, forgetting policies, and the right to be forgotten under governance frameworks such as the GDPR, revealing tensions between personalization depth and data minimization. Through case illustrations in healthcare and enterprise deployment, we show how architectural decisions around memory persistence, knowledge conflict resolution, and model retraining frequency directly impact fairness, robustness, and sustainability. The discussion extends to infrastructure implications, including on-device versus cloud-based processing, the carbon footprint of lifelong memory stores, and the long-term socio-technical risks of feedback loops that amplify cognitive biases. The paper concludes by identifying actionable policy pathways and design principles that guide the responsible deployment of LLM-based cognitive interaction systems, highlighting future research directions at the intersection of human-centered AI and systems engineering.
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