From Robots to Books: An Introduction to Smart Applications of AI in Education (AIEd) arxiv.org The world around us has undergone a radical transformation due to rapid technological advancement in recent decades. The industry of the future generation is evolving, … Continued
But as important as these effects are, the furor over ChatGPT points to something even more significant. In recent years, expertise with data collection and manipulation has all too often, in almost every area of human endeavour, been equated with … Continued
https://psyarxiv.com/jv6rb/ Abstract Algorithms are increasingly employed to provide highly accurate advice and recommendations across domains, yet in many cases people tend to prefer human advisors. Studies to date have focused mainly on the advisor’s perceived competence and the outcome of … Continued
We propose that individuals incorrectly interpret their accuracy in predicting an event as an indication that they understand the event’s underlying mechanism or process. We term this illusion prediction-comprehension bias (PCB) and conceptually locate it within the frameworks of attribute … Continued
Rapid technological advancements and global workforce aging shape the future of work. Drawing on the technology acceptance model, our study aims to connect the literature on aging with the research on technology use in organizations. At its heart, the technology … Continued
arxiv.org/pdf/2205.05682.pdf Human collaboration with systems within the Computational Creativity (CC) field is often restricted to shallow interactions, where the creative processes, of systems and humans alike, are carried out in isolation, without any (or little) intervention from the user, and … Continued
After-effects on cognition—where a prior activity either benefits or hinders subsequent cognitive performance—are empirically inconsistent. Do people have insight into when their subjective energy and cognition will be helped or hurt by engaging in prior activities? Studies 1a and 1b … Continued
Trust calibration is essential in AI-assisted decision-making tasks. If human users understand the reasons for a prediction of an AI model, they can assess whether or not the prediction is reasonable. Especially for high-risk tasks like mushroom hunting (where a … Continued
Much literature has shown that prompt-based learning is an efficient method to make use of the large pre-trained language model. Recent works also exhibit the possibility of steering a chatbot’s output by plugging in an appropriate prompt. Gradient-based methods are … Continued
This work proposes a framework that incorporates trust in an ad hoc teamwork scenario with human-agent teams, where an agent must collaborate with a human to perform a task. During the task, the agent must infer, through interactions and observations, … Continued