Large language models are moving beyond transactional question answering to act as companions, coaches, mediators, and curators that scaffold human growth, decisionmaking, and well-being. This paper proposes a role-based framework for human-centered LLM support systems, compares real deployments across domains, and identifies crosscutting design principles: transparency, personalization, guardrails, memory with privacy, and a balance of empathy and reliability. It outlines evaluation metrics that extend beyond accuracy to trust, engagement, and longitudinal outcomes. It also analyzes risks including over-reliance, hallucination, bias, privacy exposure, and unequal access, and proposes future directions spanning unified evaluation, hybrid human–AI models, memory architectures, cross-domain benchmarking, and governance. The goal is to support responsible integration of LLMs in sensitive settings where people need accompaniment and guidance, not only answers.
Zhiyin Zhou (Sat,) studied this question.