Human–AI Interaction and User Satisfaction: Empirical Evidence from Online Reviews of AI Products

Human–AI Interaction (HAI) guidelines have become increasingly important to guide the development of AI systems that align with user needs. However, large-scale empirical evidence on how HAI principles shape user satisfaction remains limited. This study addresses that gap by analyzing over 100,000 user reviews of AI-related products from G2.com. Based on widely adopted industry guidelines, we identify seven core HAI dimensions and examine their coverage and sentiment. We find that the sentiment on four HAI dimensions – adaptability, customization, error recovery, and privacy – is positively associated with user satisfaction. However, engagement with HAI dimensions varies by professional background: Users with technical job roles are more likely to discuss system-focused aspects, such as reliability, while non-technical users emphasize interaction-focused features like customization and feedback. Interestingly, job role does not moderate the link between HAI sentiment and satisfaction, indicating that once a dimension is identified, its effect is consistent across roles.