3 research outputs found
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 PÂ(SiMe<sub>3</sub>)<sub>3</sub> 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 PÂ(SiMe<sub>3</sub>)<sub>3</sub>, 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