This literature review surveys the evolution of Human-AI interaction research, arguing that the field has been overly focused on Explainable AI (XAI) — making AI systems understandable to users — while neglecting more impactful forms of interaction that give users genuine agency. The authors systematically analyze studies across Human-Centered AI, Contestable AI, Interactive AI, and Interactive Machine Learning (IML), identifying a critical gap: most existing work treats humans as passive recipients of AI explanations rather than active participants in AI system design and evolution.

The review reveals several key findings. A wide range of studies fail to include end-users in co-designing AI systems, and recent research on active interaction tends to focus on low-risk domains like education and leisure while neglecting high-stakes areas such as healthcare and security. Most AI systems prioritize enhancing user experience for acceptance rather than enabling meaningful user control over AI mechanics. Only a small number of studies, primarily in the Interactive Machine Learning domain, allow users to actively modify AI internals — representing an underexplored opportunity for greater user empowerment.

The authors propose a framework centered on three dimensions — AI Users, AI Implementations, and AI Goals — and advocate for interactions that are explicit (obvious to the user), intentional (user-initiated), and informed (the user understands the system's functionality). They call for a shift from theoretical proposals to practical experimentation, offering design guidelines that promote participatory human-AI interaction and shared responsibility between users and AI systems. The work highlights that achieving truly interactive AI requires moving well beyond explainability toward systems where humans are co-creators, not just consumers.