Over the last decade, technological advancements, especially artificial intelligence (AI), have significantly transformed educational practices. Recently, the development and adoption of Generative Pre-trained Transformers (GPT), particularly OpenAI’s ChatGPT, has sparked considerable interest. The unprecedented capabilities of these models, such as generating humanlike text and facilitating automated conversations, have broad implications in various sectors, including education and health. Despite their immense potential, concerns regarding their widespread use and opacity have been raised within the scientific community. ChatGPT, the latest version of the GPT series, has displayed remarkable proficiency, passed the US bar law exam, and amassed over a million subscribers shortly after its launch. However, its impact on the education sector has elicited mixed reactions, with some educators heralding it as a progressive step and others raising alarms over its potential to reduce analytical skills and promote misconduct. This paper aims to delve into these discussions, exploring the potential and problems associated with applying advanced AI models in education. It builds on extant literature and contributes to understanding how these technologies reshape educational norms in the “new AI gold rush” era.

The rapid advancement of artificial intelligence (AI) has generated an increasing demand for tools that can assess public attitudes toward AI. This study proposes the development and the validation of the AI Attitude Scale (AIAS), a concise self-report instrument designed to evaluate public perceptions of AI technology. The first version of the AIAS that the present manuscript proposes comprises five items, including one reverse-scored item, which aims to gauge individuals’ beliefs about AI’s influence on their lives, careers, and humanity overall. The scale is designed to capture attitudes toward AI, focusing on the perceived utility and potential impact of technology on society and humanity. The psychometric properties of the scale were investigated using diverse samples in two separate studies. An exploratory factor analysis was initially conducted on a preliminary 5-item version of the scale. Such exploratory validation study revealed the need to divide the scale into two factors. While the results demonstrated satisfactory internal consistency for the overall scale and its correlation with related psychometric measures, separate analyses for each factor showed robust internal consistency for Factor 1 but insufficient internal consistency for Factor 2. As a result, a second version of the scale is developed and validated, omitting the item that displayed weak correlation with the remaining items in the questionnaire. The refined final 1-factor, 4-item AIAS demonstrated superior overall internal consistency compared to the initial 5-item scale and the proposed factors. Further confirmatory factor analyses, performed on a different sample of participants, confirmed that the 1-factor model (4-items) of the AIAS exhibited an adequate fit to the data, providing additional evidence for the scale’s structural validity and generalizability across diverse populations. In conclusion, the analyses reported in this article suggest that the developed and validated 4-items AIAS can be a valuable instrument for researchers and professionals working on AI development who seek to understand and study users’ general attitudes toward AI.

Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions’ effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior—several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people’s initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors.

Creativity has traditionally been considered an ability exclusive to human beings. However, the rapid development of artificial intelligence (AI) has resulted in generative AI chatbots that can produce high-quality artworks, raising questions about the differences between human and machine creativity. In this study, we compared the creativity of humans (n = 256) with that of three current AI chatbots using the alternate uses task (AUT), which is the most used divergent thinking task. Participants were asked to generate uncommon and creative uses for everyday objects. On average, the AI chatbots outperformed human participants. While human responses included poor-quality ideas, the chatbots generally produced more creative responses. However, the best human ideas still matched or exceed those of the chatbots. While this study highlights the potential of AI as a tool to enhance creativity, it also underscores the unique and complex nature of human creativity that may be difficult to fully replicate or surpass with AI technology. The study provides insights into the relationship between human and machine creativity, which is related to important questions about the future of creative work in the age of AI.

The published literature has produced several definitions for the sense of presence in a simulated environment, as well as various methods for measuring it. The variety of conceptualizations makes it difficult for researchers to interpret, compare, and evaluate the presence ratings obtained from individual studies. Presence has been measured by employing questionnaires, physiological indices, behavioral feedbacks, and interviews. A systematic literature review was conducted to provide insight into the definitions and measurements of presence in studies from 2002 to 2019, with a focus on questionnaires and physiological measures. The review showed that scholars had introduced various definitions of presence that often originate from different theoretical standpoints and that this has produced a multitude of different questionnaires that aim to measure presence. At the same time, physiological studies that investigate the physiological correlates of the sense of presence have often shown ambiguous results or have not been replicated. Most of the scholars have preferred the use of questionnaires, with Witmer and Singer's Presence Questionnaire being the most prevalent. Among the physiological measures, electroencephalography was the most frequently used. The conclusions of the present review aim to stimulate future structured efforts to standardize the use of the construct of presence, as well as inspire the replication of the findings reported in the published literature.

Virtual reality (VR) offers novel ways to develop skills and learning. This technology can be used to enhance the way we educate and train professionals by possibly being more effective, cost-efficient, and reducing training-related risks. However, the potential benefits from virtual training assume that the trained skills can be transferred to the real world. Nevertheless, in the current published scientific literature, there is limited empirical evidence that links VR use to better learning. The present investigation aimed to explore the use of VR as a tool for training procedural skills and compare this modality with traditional instruction methods. To investigate skill development using the two forms of training, participants were randomly divided into two groups. The first group received training through an instructional video, while the second group trained in VR. After the training session, the participants performed the trained task in a real setting, and task performance was measured. Subsequently, the user’s experienced sense of presence and simulator sickness (SS) was measured with self-report questionnaires. There were no significant differences between groups for any of the performance measures. There was no gender effect on performance. Importantly, the results of the present study indicate that a high sense of presence during the VR simulation might contribute to increased skill learning. These findings can be used as a starting point that could be of value when further exploring VR as a tool for skill development.