Abstract

Human‑Centered Artificial Intelligence (HCAI) focuses on designing AI systems that augment human capabilities, support meaningful collaboration, and respect user needs, values, and context. In collaborative and assistive intelligent systems, human‑centered design principles ensure that technology aligns with human goals, fosters trust, enables transparency, and enhances overall user experience while mitigating risks associated with automation bias, loss of control, or unintended harm. This paper presents an extensive exploration of design principles for human‑centered AI in collaborative and assistive contexts, highlighting theoretical foundations, practical frameworks, and evaluation strategies. It synthesizes existing research on user‑centric interaction, interpretability, adaptability, and socio‑ethical considerations, and proposes a structured methodology for embedding human‑centered principles throughout the AI system lifecycle. We discuss advantages such as improved usability, trustworthiness, and task effectiveness, alongside disadvantages and challenges including design complexity and resource constraints. Through qualitative and empirical evaluation, the impact of human‑centered design on system adoption and performance is examined. The results underscore the necessity of integrating human values, accessibility, and participatory methods in AI design. The paper concludes with future research directions emphasizing interdisciplinary collaboration, real‑world validation, and regulatory frameworks to ensure responsible, inclusive, and effective human‑AI partnerships