An HCI-Centric Survey and Taxonomy of Human-Generative-AI Interactions

Generative AI (GenAI) has shown remarkable capabilities in generating diverse and realistic content across different formats like images, videos, and text. In Generative AI, human involvement is essential, thus HCI literature has investigated how to effectively create collaborations between humans and GenAI systems. However, the current literature lacks a comprehensive framework to better understand Human-GenAI Interactions, as the holistic aspects of human-centered GenAI systems are rarely analyzed systematically. In this paper, we present a survey of 291 papers, providing a novel taxonomy and analysis of Human-GenAI Interactions from both human and Gen-AI perspectives. The dimensions of design space include 1) Purposes of Using Generative AI, 2) Feedback from Models to Users, 3) Control from Users to Models, 4) Levels of Engagement, 5) Application Domains, and 6) Evaluation Strategies. Our work is also timely at the current development stage of GenAI, where the Human-GenAI interaction design is of paramount importance. We also highlight challenges and opportunities to guide the design of Gen-AI systems and interactions towards the future design of human-centered Generative AI applications

Visualizing Causality in Mixed Reality for Manual Task Learning: An Exploratory Study

Mixed Reality (MR) is gaining prominence in manual task skill learning due to its in-situ, embodied, and immersive experience. To teach manual tasks, current methodologies break the task into hierarchies (tasks into subtasks) and visualize the current subtask and future in terms of causality. Existing psychology literature also shows that humans learn tasks by breaking them into hierarchies. In order to understand the design space of information visualized to the learner for better task understanding, we conducted a user study with 48 users. The study was conducted using a complex assembly task, which involves learning of both actions and tool usage. We aim to explore the effect of visualization of causality in the hierarchy for manual task learning in MR by four options: no causality, event level causality, interaction level causality, and gesture level causality. The results show that the user understands and performs best when all the level of causality is shown to the user. Based on the results, we further provide design recommendations and in-depth discussions for future manual task learning systems

Zinc–Phosphorus Complex Working as an Atomic Valve for Colloidal Growth of Monodisperse Indium Phosphide Quantum Dots

Growth of monodisperse indium phosphide (InP) quantum dots (QDs) represents a pressing demand in display applications, as size uniformity is related to color purity in display products. Here, we report the colloidal synthesis of InP QDs in the presence of Zn precursors in which size uniformity is markedly enhanced as compared to the case of InP QDs synthesized without Zn precursors. Nuclear magnetic resonance spectroscopy, X-ray photoelectron spectroscopy, and mass spectrometry analyses on aliquots taken during the synthesis allow us to monitor the appearance of metal–phosphorus complex intermediates in the growth of InP QDs. In the presence of zinc carboxylate, intermediate species containing Zn–P bonding appears. The Zn–P intermediate complex with P­(SiMe₃)₃ exhibits lower reactivity than that of the In–P complex, which is corroborated by our prediction based on density functional theory and electrostatic potential charge analysis. The formation of a stable Zn–P intermediate complex results in lower reactivity, which enables slow growth of QDs and lowers the extreme reactivity of P­(SiMe₃)₃, hence monodisperse QDs. Insights from experimental and theoretical studies advance mechanistic understanding and control of nucleation and growth of InP QDs, which are key to the preparation of monodisperse InP-based QDs in meeting the demand of the display market