In a range of settings, human operators make decisions with the assistance of automation, the reliability of which can vary depending upon context. Currently, the processes by which humans track the level of reliability of automation are unclear. In the current study, we test cognitive models of learning that could potentially explain how humans…

We live in an age of artificial intelligence (AI). Open AI, an American artificial intelligence organization, claims that generative chatbots are here to stay. Although computer technology remains unevenly distributed, the presence of AI continues to be felt in many areas of daily life. Further, AI development happens in few countries whose…

With artificial intelligence (AI) is extensively applied in education, human-centered AI (HCAI) has become an active field. There although has been increasing concern about how to systematically enhance the AI applications effect, AI risk governance in HCAI education has not been discussed yet. This study adopted literature meta-analysis, along…

Background/purpose: ChatGPT, a recent form of AI-based language model, have garnered interest among people from diverse backgrounds with its immersive capabilities. Using ChatGPT to support or generate scientific research has also created an ongoing debate over its advantages versus risks. The present study aimed to conduct an AI-enabled research…

Recently, ChatGPT, a cutting-edge large language model, has emerged as a powerful Generative Artificial Intelligence (GenAI) tool with the capacity to influence education. ChatGPT provides ample opportunities for learners, researchers, educators, and practitioners to achieve the intended learning outcomes in various disciplines. This special issue…

Much of the philosophical debate regarding technological epistemic enhancement concerns interventions on cognitive capacities that are already performing well in order that they perform even better. However, several decades of research in cognitive science demonstrates that humans harbor systematic cognitive biases that can produce ill-grounded,…

The growing use of machine learning for the data-driven study of social issues and the implementation of data-driven decision processes has required researchers to re-examine the often implicit assumption that datadriven models are neutral and free of biases. The careful examination of machine-learned models has identified examples of how existing…