Author(s) :

Jay Tamirisa, Jackson Hardin .

Volume/Issue :

Abstract :

Social robots (SRs) interact with humans using speech, motion, and affective behavior, and are used in retail, education, healthcare, and disability services. Advances in big data, machine learning, and generative artificial intelligence have enhanced the responsiveness of SRs. However, real-world adoption remains inconsistent, and key human-robot interaction (HRI) dynamics, such as adaptability, personalization, emotional modeling, and robot ‘personality,’ are underexplored. This narrative review synthesizes cross-sector evidence to identify patterns, limitations, and emerging design principles. SRs are most effective when integrated into existing workflows and tailored to context. Socially expressive SRs enhance engagement in hospitality, whereas emotionally responsive designs are more effective for children with autism. In healthcare, SRs are more readily accepted as complementary team members, rather than human substitutes. Overall, effectiveness depends on alignment between system design and user context. Persistent challenges, including novelty effects, ethical concerns, and limited longterm validation, constrain scalability, highlighting the need for human-centered, interdisciplinary development.