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.

Stanford HAI previews its spring conference on intelligence augmentation, emphasizing AI's role in enhancing human work rather than replacing it. Associate directors Russ Altman and James Landay discuss how AI can empower teachers with real-time student assessment tools, help doctors manage unfamiliar diagnoses, and enable artists to push creative boundaries — all while keeping humans firmly in control. The conversation highlights specific examples such as Dennis Wall's Google Glass tool for autistic children to recognize emotions, AI-powered tutoring systems that personalize learning to boost student motivation, and wearable health technologies championed by Deborah Estrin of Cornell Tech.

A central theme is that practitioners must have a vote in how augmentation is deployed — when decision-makers who aren't the actual teachers, nurses, or artists dictate AI adoption, the technology risks going too far. The speakers also stress choosing problems where services are currently inaccessible, which reduces the likelihood of job displacement. The conference brings together researchers, industry professionals, government regulators, and students to explore how AI can serve people solving global challenges in education, health care, and the arts, rather than acting as a standalone replacement.

This paper introduces Human-Centered Human-AI Collaboration (HCHAC), a framework that redefines the relationship between humans and AI systems in the intelligent era. The authors argue that as AI agents evolve from passive tools to active teammates, the collaboration paradigm must shift accordingly, with humans maintaining critical leadership roles throughout. The chapter, part of an upcoming Handbook of Human-Centered Artificial Intelligence, reviews current research methodologies and agendas within the HAC field through the lens of Human-Centered AI (HCAI).

The proposed HCHAC framework integrates existing research to address the unique challenges of human-AI collaboration, including effectiveness, reliability, and ethical integration. A case study on autonomous vehicles demonstrates practical applications of the framework, illustrating how synergistic interactions between humans and AI agents can function in real-world contexts. The paper concludes by identifying future research directions aimed at improving human-centered HAC systems across diverse domains, emphasizing that the design of collaborative AI must prioritize human agency and oversight rather than full automation.