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.

Elicit is an AI-powered research tool designed to assist with scientific research workflows. The platform positions itself as a tool that helps researchers and academics leverage artificial intelligence to analyze and synthesize scientific literature more efficiently. Elicit aims to streamline the process of finding, reading, and extracting insights from academic papers.

While the specific page content is minimal (likely a landing page with dynamic content), Elicit is widely known as a research assistant that uses language models to help users search for relevant papers, summarize findings, and extract key data points from the scientific literature. It serves as a productivity tool for anyone engaged in evidence-based research, from graduate students to professional scientists.

Alex Imas shares a new post reviewing all available studies and data on the impact of AI on productivity. He attempts to provide a comprehensive synthesis of the existing research, noting that there is significant disagreement about what we actually know regarding AI's productivity effects.

A key insight highlighted is the disconnect between micro-level findings and broader conclusions, which Imas identifies as a major reason for the conflicting narratives around AI and productivity. The post suggests a thorough, evidence-based examination aimed at bridging this gap in understanding.