Generative AI as a lab partner: A case study

[This paper is part of the Focused Collection in Artificial Intelligence Tools in Physics Teaching and Physics Education Research.] This study investigates physics instructors’ acceptance and implementation of generative AI (GenAI) in physics education, guided by Rogers’ diffusion of innovation (DOI) theory, focusing on its five-stage innovation-decision process: knowledge, persuasion, decision, implementation, and confirmation. Two survey versions were developed—one for GenAI users and another for nonusers. Data were collected through an online survey featuring five-point Likert scale items and open-ended questions, yielding 320 responses from high school and university physics instructors. The persuasion stage was explored in depth using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). A mixed-method approach combining quantitative structural equation modeling (SEM) and qualitative content analysis was employed. We found instructors distributed across different adoption stages, with key barriers including insufficient technical knowledge (51.75% of nonusers) and language processing limitations (50.5% of users). Hedonic motivation (β=0.498) was found to have a substantially stronger influence on adoption than performance expectancy (β=0.121), with an effect size approximately 4 times greater. We identified significant differences between paid and free GenAI users, with paid users more likely to request prompt-writing guidance (30.56% vs 6.47%) despite using more advanced models. Support needs also varied substantially, as half of paid users requested budget support for GenAI services compared to only 17.65% of free users. Most physics instructors used GenAI primarily for assessment tasks, particularly for generatingphysics problems with solutions. Their primary concerns included incorrect physics information in GenAI responses, potential negative impacts on students’ analytical thinking, language barriers, and challenges with prompt writing. These findings suggest that successful GenAI integration in physics education requires physics-specific training and differentiated support based on whether instructors use free or paid versions of GenAI tools. Published by the American Physical Society 2025

Aim/Purpose: This paper investigates how academics in Egyptian higher education adopt and engage with generative AI tools, addressing the limited understanding of faculty perceptions and the role of technophobia in influencing adoption. Background: Existing research on generative AI adoption primarily focuses on a single tool (e.g., ChatGPT) and overlooks broader organizational and psychological factors. This study extends the Technology Acceptance Model (TAM) to include technophobia and organizational innovative culture, providing a comprehensive explanation of adoption behaviors in the Egyptian higher education context. Methodology: A mixed-methods design was employed. Quantitative data were collected from 195 academics via a structured survey measuring TAM constructs, technophobia, and organizational culture. Qualitative data were obtained through semi-structured interviews to capture experiences, perceived benefits, and concerns regarding generative AI tools. Contribution: The study refines TAM by demonstrating that technophobia indirectly affects adoption through perceived usefulness and perceived ease of use, while organizational innovative culture does not moderate adoption relationships. It offers both theoretical insights and practical guidance for the responsible use of generative AI in higher education. Findings: Perceived usefulness was the strongest predictor of adoption intention, whereas perceived ease of use was not significant. Technophobia reduced perceived usefulness and ease of use but did not directly affect adoption intention. Organizational innovative culture did not moderate relationships. Interviews highlighted efficiency benefits of generative AI alongside concerns about ethics, originality, and policy gaps. Recommendations for Practitioners: Universities should establish clear policies for the use of generative AI in teaching, assessment, and research, and provide regular training and awareness programs to support responsible adoption. Institutions should encourage critical and purposeful use rather than dependence on generative AI. Recommendation for Researchers: Future studies may expand TAM by including constructs such as trust, perceived risk, and institutional policy support, explore discipline-specific adoption patterns, and examine long-term impacts on teaching and learning. Impact on Society: Generative AI has the potential to enhance academic productivity while raising ethical and integrity concerns. Balanced and responsible implementation can maintain the educational and social mission of universities. Future Research: Further research should involve a wider range of institutions, consider moderators such as digital literacy and organizational readiness, and develop ethical and pedagogical frameworks for the constructive use of AI in higher education.

Blockchain-based auditing of legal decisions supported by explainable AI and generative AI tools

Objectives This study investigates the adoption and integration of Generative AI (GAI) technologies in daily life, focusing on factors that influence user behavior and attitudes. Method Using a mixed‐methods approach, we combined quantitative, qualitative, and semi‐experimental methodologies to capture the complexities of GAI tool usage. A total of 897 individuals completed an online survey with both closed and open‐ended questions; additionally, 38 students participated in a semi‐experimental stage to assess the impact of an online course, “GAI: From Theory to Practice.” Results Key findings underscore the role of personality traits such as openness and self‐efficacy in promoting GAI usage, mediated by behavioural perceptions and digital engagement patterns. In line with the UTAUT2 model, Performance and Effort Expectancy were the strongest predictors of GAI usage. Social media engagement and information overload management also emerged as predictors, illustrating the interplay between digital behaviours and GAI adoption. Qualitative findings revealed a nuanced understanding of user perceptions, emotions, and societal needs, uncovering themes of GAI's revolutionary potential, emotional ambivalence, and the demand for regulatory and educational frameworks. The experimental results demonstrated significant improvements in students' perceptions of GAI utility, self‐reported skill levels, and GAI usage across work, study, and personal contexts following the course. Conclusion This research advances understanding of GAI's societal integration and highlights the need for targeted training and regulatory policies to support responsible adoption and enhance digital literacy. It offers practical recommendations for educators, policymakers, and designers of generative AI tools.

The TrollLabs Open dataset includes comprehensive information that offers a comparison of design practices and outcomes between human participants and Generative AI during a hackathon event. The dataset was curated through the running of a prototyping hackathon designed to assess the abilities and performance of generative AI, specifically ChatGPT, in the early stages of engineering design. This assessment involved comparing ChatGPT's performance to that of experienced engineering students in a hackathon setting, where participants competed by making a prototype that fires a NERF dart as far as possible. In this setup, all ideas, concepts, strategies, and actions undertaken by the AI-controlled team were autonomously generated by the ChatGPT, without human intervention or guidance, but implemented by two participants. Five self-directed baseline teams competed against the AI team. The dataset comprises 116 prototype entries and 433 edges (connection) that enable comparative analysis of design practices and performance between the team instructed solely by generative AI and baseline teams of experienced engineering design students. Prototypes and their attribute data were captured using Pro2booth, an online prototype capture platform running on participants' phones and computers. The dataset includes a transcript of the conversation between ChatGPT and the team responsible for implementing its recommendations, featuring 97 exchanges of prompts and responses. It contains the initial prompt used to instruct the AI, the objective and rules of the hackathon and the objective performance of teams, showing the ChatGPT team finishing 2nd among six teams. To the authors' knowledge, the TrollLabs Open dataset is the first and only open resource that directly compares the performance of generative AI with human teams in an engineering design context. Thus, it is intended to be a valuable resource to design researchers, engineering and design students, educators, and industry professionals seeking to find strategies for implementing generative AI tools in their design processes. By offering a comprehensive data collection, the dataset enables external researchers to conduct in-depth analyses that could highlight the practical implications of integrating generative AI in design practices, possibly providing an overview of its limitations and presenting recommendations for improved integration in the design process.

Aim/Purpose: This study investigated how undergraduate and graduate students in a teacher education program perceived, used, and ethically reflected upon generative AI tools. The problem is the unclear state of preservice teachers’ understanding and use of generative AI in educational settings, which this study aimed to explain. Background: Given generative AI’s growing presence in education, this study addressed a gap in understanding by examining preservice teachers’ perceptions, experiences, and ethical concerns, particularly in relation to self-regulated learning. Methodology: A convergent parallel mixed-methods design was used to gather quantitative and qualitative data from 73 students enrolled in a teacher education program at the University of Nevada, Las Vegas. Descriptive statistics, correlations, and thematic analysis were used for data analysis. Contribution: This study contributes to the emerging literature by providing insights into preservice teachers’ usage patterns of generative AI, the relationship between AI use and self-regulatory skills, and a detailed examination of ethical concerns, informing effective AI integration into teacher education programs. Findings: Text-based generative AI was the most familiar tool among participants. The primary reasons for the use included editing papers, searching for new ideas, and understanding course material. A significant positive correlation was identified between information search with the help of generative AI and resource management skill. Key themes identified by thematic analysis included perceived benefits for student engagement and creativity, AI’s potential as a writing assistant, and ethical concerns about plagiarism and misuse. Overall, preservice teachers demonstrated surface-level use and a general lack of in-depth knowledge about AI integration. Recommendations for Practitioners: Teacher educators should provide explicit training in generative AI, including practical guidelines and clear ethical frameworks to foster effective and responsible integration into classroom practice. Recommendation for Researchers: Future researchers should develop validated measurement instruments specifically designed for studying generative AI perceptions and usage and explore longitudinal impacts as preservice teachers transition into professional teaching roles. Impact on Society: By guiding preservice teachers toward informed and ethical use of generative AI, this study supports broader societal goals of promoting digital literacy, ethical technology integration, and enhanced learning experiences for future generations. Future Research: Subsequent research should further explore reasons behind the limited adoption of generative AI among preservice teachers, examine the long-term impacts of AI use on pedagogical practices, and investigate structured frameworks for promoting deeper AI integration within the teaching and learning domain.

The Elementary School Journal Volume 84, Number 4 S1984 by The University of Chicago. All rights reserved. 001 3-5984/84/8404-0004$01.00 Assumptions, even assumptions that seem logical and reasonable, can make it difficult or impossible for people to understand scientific concepts. Everyone makes assumptions about the way the world works, assumptions like "When the sky is cloudy and dark, it will probably rain" or "Bits of wood float in water." People often use those assumptions to explain how things work: "I can see myself in a mirror because light bounces off me to the mirror and off the mirror to my eyes." Such explanations or conceptions are often based on experience and common sense; however, experience and common sense can sometimes

The main goal of this article is to introduce physics education research (PER) to researchers in other fields. Topics include discussion of differences between science education research (SER) and physics education research (PER), physics educators, research design and methodology in physics education research and current research traditions and trends (e.g. current research ideas) within PER.

This study investigates how emerging digital technologies, particularly generative AI tools, are transforming public sector communication in Europe, highlighting the profound intersection between public organizations, AI, and human interactions. In particular, it explores the opportunities and risks that public sector communicators face as they deal with and integrate digital platforms and AI‐driven tools into their strategies and practices in a contemporary scenario characterized by the spread of disinformation and a growing distrust toward institutions. The article gathers insights from in‐depth interviews with leading public sector communicators working for European governments and EU institutions. Findings reveal that generative AI is seen as a transformative tool for governments and public institutions, with communicators emphasizing both benefits and risks, as well as the importance of adopting ethical practices and new responsibilities toward citizens, institutions, and mass media. From the interviews, generative AI tools emerged as game‐changers in message delivery and content production, demanding greater professionalism and new competencies and skills to integrate these technologies into public sector communication strategies and to counteract the threats posed by disinformation campaigns and platformization. The study provides valuable insights into the evolving role of generative AI in public‐sector communication, addressing the scarcity of research in this field. As the adoption of generative AI becomes inevitable, and policy frameworks like the EU AI Act develop, communicators must ensure transparency and trust to align public sector communication with democratic values and foster meaningful dialogue in new digital‐media arenas. Implications for theory and practice are discussed.

Tourism is one of the most important sectors of the global economy, generating billions of euros in revenue and supporting millions of jobs. However, the tourism industry also faces many challenges, such as changing consumer preferences, environmental impacts and competitive pressures. In this paper, we explore how specialized and generative AI, a branch of artificial intelligence that can create novel content, such as text, images, or audio, can support hotels and other tourist accommodation companies in enhancing their products and services, improving their marketing and communication strategies and optimizing their operations and management. We review the existing literature on AI and its applications in tourism and propose potential use cases of generative AI for tourist companies. We also present some examples of generative AI tools that can be used for these purposes and discuss their benefits and limitations. Finally, we offer some conclusions and future implications for research and practice in this emerging field. Theoretical implications of the present paper suggest that the incorporation of generative AI in the tourism sector can significantly advance the current understanding of AI applications in service industries. By leveraging machine learning techniques, such as deep neural networks and generative adversarial networks, the tourism industry can create novel content and optimize various operations. This study enriches existing literature by identifying new use cases for generative AI, thus providing a foundation for further academic inquiry. Practically, our findings reveal several actionable insights for tourism companies. Generative AI tools can be harnessed to personalize customer experiences, enhance marketing efforts, and streamline operational management.

The study examines the design implications of leveraging generative AI tools such as ChatGPT, Stable Diffusion, Midjourney for literacy development and creative expression for children [6, 8, 18]. We sought to elicit insights on the applicability of generative AI for educational purposes from various stakeholders (i.e., parents, teachers, and AI researchers). We recruited nine participants to elicit their perspectives on designing a visual narrative app with generative AI. We examined the opportunities and limitations of the current generative AI tools. Using the implications from our evaluation, we propose AIStory, an AI-powered visual storytelling application prototype that can be used for children’s creative expression, storytelling, and literacy development.

This study examines the educational effects of a generative AI music creation workshop conducted as part of STEAM education that integrates the arts (in this case, music creation) into science, technology, engineering, and mathematics (STEM). The workshop, held at Osaka Metropolitan University College of Technology, employed state-of-the-art generative AI tools such as ChatGPT and Suno, a music generation AI. It provided students with the opportunity to write their lyrics and collaborate with AI to compose music. Additionally, AI literacy education was incorporated to deepen understanding of the risks and ethical challenges associated with AI, aiming to balance creative activities with the responsible use of technology. Results from a post-workshop survey indicated that students experienced heightened creativity and self-expression through collaboration with generative AI, and many reported feeling a sense of excitement and anticipation (“wakuwaku”) during the process. Notably, creating music with AI was found to enhance students’ creative self-efficacy and promote sustained motivation for long-term AI use. However, some participants also expressed concerns about over-reliance on AI and reported psychological barriers, highlighting areas for improvement in future implementations. This study not only demonstrates that STEAM education incorporating generative AI is an effective approach to fostering students’ creativity and motivation to learn, but also addresses practical challenges such as ethical considerations and the need for supportive educational environments. Furthermore, as a successful case supported by The Asia Foundation and Microsoft, the program suggests the potential for collaboration among industry, academia, and government in advancing STEAM education.

The digital determinants of health have recently attracted attention in the medical and public health literature, but the environmental aspects of these determinants have rarely been considered. This Perspectives article calls for applying a planetary health lens to the digital determinants of health, with a focus on the impacts of novel generative AI technologies on the natural world. The argument is founded on the principle that human and planetary health are intertwined and that digital technologies such as generative AI affect the health of both humans and the other living things in ecosystems. The article discusses proposals of how generative AI tools can contribute to action on the climate, loss of biodiversity and pollution crises but also highlights how the expansion of generative AI infrastructure and operation has exacerbated the problems of e-waste, greenhouse gas emissions and major burdens on energy and water resources. It is argued that for progress to be made in improving human and planetary health, those working in health promotion and other public health domains must acknowledge that human and planetary health are interdependent, so that we can refer to the ‘digital determinants of human and planetary health’. The article highlights this interdependence and the importance of fighting for multispecies justice and the rights of nature to promote the mutual flourishing of humans and the natural world. Actions are proposed that can be taken at the organization and community level to make visible and counter the impacts of generative AI on human and planetary health.

Objectives This study aimed to analyze the perceptual changes experienced by pre-service teachers as they derived the final products of lesson plans using generative AI, and to explore educational implications for enhancing their Technological Pedagogical Content Knowledge (TPACK). Methods For this purpose, a preliminary training on the educational use of generative AI was conducted for 20 students in a teacher education course at A Education Graduate School in Seoul. Subsequently, the students explored applicable lesson design strategies for various subjects and reflected on these processes and experiences through mid-term assignments, which were then qualitatively analyzed based on the TPACK framework. Results Firstly, pre-service teachers recognized the educational utility of selecting and using generative AI tools aligned with educational objectives and confirmed their potential as tools for delivering subject content. Secondly, through experiencing the limitations of generative AI, they recognized the importance of carefully integrating technology and cultivating digital and AI literacy among teachers. It was found that their repeated and experimental approaches to lesson design contributed to the enhancement of problem-solving and critical thinking skills. Thirdly, pre-service teachers who were initially hesitant to use generative AI developed a positive perception and showed a willingness to use it more extensively in lesson design. Lastly, they were able to effectively integrate elements of generative AI and TPACK, developing new and innovative customized teaching methods and strategies suitable for each subject, and expanded their educational thinking and perspectives by experiencing lesson design cases from fellow pre-service teachers. Conclusions Teacher training institutions should develop and operate more practical curricula that include excellent examples of technology-integrated classes and practical exercises. Additionally, the curriculum should include content that simulates real issues encountered while using generative AI, and develop customized professional courses that facilitate the systematic integration of generative AI according to each major.

<div class="section abstract"><div class="htmlview paragraph">Evaluation of integrated human-machine systems depends on having accurate human performance models. However, such models often provide only instantaneous snapshots of cognitive state and fail to account for ongoing dynamics. We argue that generative AI solutions can be used to alleviate this problem. Generative AI tools have been successful when applied to problems that have repeatable structure captured by a low-dimensional lexicon and associated with large amounts of training data. These properties apply to human performance modeling as well. Here, we introduce our Generative Cognitive Modeling Tool, a prototype human performance model developed using strategies from the generative AI community. We demonstrate the utility of our approach using simulated driving data. Our results show that cognitive states associated with driving errors are not randomized events but rather the outcome of continuous dynamics and are predictable up to 25 secs prior to the error event. We also find that the voluntary utilization of autonomous driving aids can be predicted, in part, by the disruption of ongoing dynamics. Overall, this underscores the importance of ongoing dynamics for human performance modeling and establish that generative AI approaches can provide one way to account for such factors.</div></div>