Despite increasing interest in using Generative Artificial Intelligence (GenAI) in education, little is known about how students with disability engage with GenAI to support their own learning. This study investigates the potential of ChatGPT to support the learning agency of adolescents with disability in a secondary science classroom in Australia. Guided by sociocultural and socio-material conceptualisations of agency, the study explored the mediated choices and capabilities of three students with disability to use ChatGPT to facilitate their learning. The study was conducted in a class comprising students of varying ages clinically diagnosed with diverse learning needs. Data sources included student interviews, the students' conversations with ChatGPT, teachers' lesson worksheets and video recordings of the lesson. Thematic analyses reveal that while students expressed clear and meaningful choices to use ChatGPT to support their learning, they faced metacognitive challenges and cognitive constraints, resulting in a misalignment between their choices and actual capability. The findings identify key theoretical perspectives and practical considerations for supporting students with disability in using GenAI to develop their learning agency. The study recommends customising GenAI for specific learning needs in line with its function as a cognitive prosthesis for students with disability and for better alignment with Universal Design for Learning, thereby supporting students' learning agency. What is already known about this topic What this paper adds Implications for practice and/or policy

This article examines the potential of generative artificial intelligence (GenAI) to reshape feedback practices in higher education. It reports on a proof-of-concept study in which teacher educators recorded oral feedback. These recordings were uploaded to a customised ChatGPT model and restructured in alignment with the assessment rubric. Educators then edited the text to ensure accuracy, appropriate tone, and alignment with the rubric. Using collaborative autoethnography, we analysed our experiences to explore how GenAI reshaped the feedback process. Initial implementation increased workload due to technical challenges and substantial editing demands. Over time, efficiencies improved as prompts and workflows were refined. Some educators reported reduced cognitive demands when speaking rather than typing, although labour shifted towards verification, tone calibration, and rubric alignment. While GenAI output imposed a coherent structure on spoken commentary, human oversight remained essential. Ethical considerations relating to authorship, transparency, and professional responsibility were central throughout. We argue that meaningful integration of GenAI into feedback practices requires careful design, sustained human oversight, and explicit ethical reflection. This study raises important questions about assessment authorship, professional identity, and evolving assessment processes in higher education.

With the introduction of ChatGPT in 2022, Large Language Models (LLMs) have been increasingly used in classrooms to support teaching and learning. However, the nature of communication between students and LLMs remains under-examined. The current study explored this communication through the lens of Paul Grice’s cooperative principle, examining the extent to which students and ChatGPT adhered to Gricean maxims of quantity, quality, relation and manner in their one-on-one communication during class. The study was implemented in 2023 in a Year 10 English class in an all-boys independent high school in Western Australia. 10 students participated in a poetry-focused lesson, where they interacted with ChatGPT to explore poetic texts. The lesson was designed not only to support understanding of the poetry but also to foster students’ capacity for critical questioning and engagement with AI-generated output by positioning the GenAI as a dialogic partner. The findings identified specific user violations of Gricean maxims in students’ communication with ChatGPT and the impact such violations had on ChatGPT’s output. These findings affirm the relevance of Grice’s cooperative principle and maxims for analysing conversations with LLMs and the potential for identifying interaction patterns with GenAI that differ from human-to-human conversations, underscoring the importance of examining human input as well as LLMs’ output. In place of a recent trend towards technocentric approaches to researching human-GenAI communication, the paper advocates a sociotechnical approach as a means to examine such interactions holistically.

The rapid integration of Generative AI (GenAI) in education presents both opportunities and challenges in fostering critical questioning - a skill essential for critical thinking and AI literacy. In the context of GenAI, critical questioning refers to the ability to question, probe, and critically assess information generated by GenAI that will equip students with the discernment necessary in a digital world. However, there is limited research on how students develop and apply critical questioning when interacting with GenAI. This study addresses the research gap by investigating the pedagogical and contextual conditions that support high school students in critical questioning with GenAI. Through an action research study situated in a Grade 10 English classroom, the study examines the key conditions that facilitated students' critical questioning with GenAI. Ethnographic methods were used to generate data from classroom observations, interviews, and student chatlogs that captured how students engaged with GenAI in situ within the classroom environment. A prior critical questioning framework was modified and used to identify instances of critical questioning with GenAI in the data, which were coded along the dimensions of context, delivery, and competency. Findings highlight how the instructional design of AI-mediated interactions, role of the teacher, students' knowledge and disposition, and the delivery of GenAI platform were crucial in shaping the quality and depth of students' questioning. These findings indicate that the success of critical engagement with GenAI does not rest on its technological capabilities alone, but on the specific pedagogical and classroom conditions that enable students to use it purposefully and reflectively. By extending our understanding of critical questioning in AI-mediated learning environments, this study provides insights into the conditions that foster AI literacy, which can lead to students actively and critically engaging with AI-generated content rather than passively consuming it.

Despite increasing interest in using Generative Artificial Intelligence (GenAI) in education, little is known about how students with disability engage with GenAI to support their own learning. This study investigates the potential of ChatGPT to support the learning agency of adolescents with disability in a secondary science classroom in Australia. Guided by sociocultural and socio-material conceptualisations of agency, the study explored the mediated choices and capabilities of three students with disability to use ChatGPT to facilitate their learning. The study was conducted in a class comprising students of varying ages clinically diagnosed with diverse learning needs. Data sources included student interviews, the students' conversations with ChatGPT, teachers' lesson worksheets and video recordings of the lesson. Thematic analyses reveal that while students expressed clear and meaningful choices to use ChatGPT to support their learning, they faced metacognitive challenges and cognitive constraints, resulting in a misalignment between their choices and actual capability. The findings identify key theoretical perspectives and practical considerations for supporting students with disability in using GenAI to develop their learning agency. The study recommends customising GenAI for specific learning needs in line with its function as a cognitive prosthesis for students with disability and for better alignment with Universal Design for Learning, thereby supporting students' learning agency.Practitioner notes What is already known about this topic The development of students' learning agency has been widely explored in the secondary context, but not for students with disability. There is increasing interest in using Generative AI (GenAI) to support inclusive education. Current theoretical frameworks can inform the exploration of the learning agency of students with disability using GenAI. What this paper adds Foregrounds the learning choices of students with disability as indicators of their emergent learning agency. Identifies learning challenges facing students with disability that misalign their learning choices and capabilities. Examines GenAI's potential as a socio-material mediator facilitating the material and relational agency of students with disability. Implications for practice and/or policy Consulting students with disability on how GenAI can support their learning, providing opportunities for them to express their learning preferences. Customising GenAI tools in line with the Universal Design for Learning guidelines to address the specific learning challenges of students with disability. Clarifying the roles of teachers and education assistants in facilitating the learning agency of students with disability in light of customising GenAI to mediate their learning agency.

Since generative artificial intelligence (GenAI) has emerged as a transformative force in science teaching, learning, and evaluation, countries worldwide have launched initiatives and professional development programs to equip science teachers with essential AI competencies. This paper provides a comparative review of science teacher education on GenAI across eight countries: Australia, Canada, China, Germany, Ghana, Singapore, South Korea, and the United States. For each country, briefs on science teacher education, the application of AI and GenAI in science teacher education, and stakeholders' (science teachers' and science teacher educators/researchers') perceptions of GenAI in science education are reported. Synthesising reports across countries, it was found that GenAI was globally accepted in science teacher education systems, regardless of a country's economic development, and that while centralised teacher education systems were more efficient, decentralised systems were more deliberate in incorporating GenAI into science teacher education. Lessons from each country's case could bridge remaining disparities among them, and help address ethical concerns in adopting GenAI for science education through science teacher education. This study provides a greater understanding of the current status of global science teacher education in the era of GenAI, identifies opportunities for future research, and highlights policy implications.

The rapid advancement of generative artificial intelligence (GenAI) is reshaping various sectors, including education. This editorial explores the intersections of AI, science education, and the preparation of pre-service teachers (PSTs), questioning whether GenAI represents a truly transformative technology or merely the latest iteration of an educational hype cycle. While previous technological innovations-such as radio, television, and computers-were initially heralded as revolutionary, their impact on education has varied significantly. The emergence of GenAI, particularly large language models, introduces new possibilities for personalised learning, digital tutoring, and adaptive content generation, yet its integration into educational practice remains uneven. Despite growing societal reliance on GenAI, a substantial proportion of in-service teachers report limited use, citing a lack of training and institutional policies as key barriers. In contrast, research suggests that PSTs are already engaging with GenAI tools for lesson planning, content summarisation, and assessment preparation. This editorial underscores the need for initial teacher education (ITE) programmes to provide structured support, ensuring that PSTs develop both technical proficiency and critical AI literacy. Ethical concerns-such as data privacy, algorithmic bias, and epistemic authority-must also be central to teacher training. Focusing on science education, this special issue examines the opportunities and challenges of GenAI integration. The contributions explore PSTs' perceptions, competencies, and preparedness to implement AI-driven tools in their teaching, addressing themes such as inquiry-based learning, pedagogical content knowledge, and the evolving role of educators in AI-powered classrooms. The discussion highlights the necessity of balancing technological innovation with critical reflection, equipping future teachers to navigate the complexities of AI in education responsibly. This editorial aims to advance the dialogue on GenAI's role in teacher preparation, urging educational institutions to proactively support PSTs in harnessing its potential while fostering a critical, ethical, and pedagogically sound approach to AI integration in science education.

Encouraging females to engage in and pursue Science, Technology, Engineering and Mathematics (STEM) education and vocations are key priorities for stakeholders and primary aims of the Australian Government-funded STEM in Situ (WISE 2016-18) project. Using a researcher-designed student survey by two of the authors, this article reports on the STEM-related attitudes, engagement and vocational intentions of female students involved in the project. The research survey developed for the project collected data in 2017-8 from 221 female students in Years 5-9 (11 to 16 years of age) from various public schools in Australia. Factor analytic and repeated measures t-tests data analysis techniques were used to explore the factor structure of the survey items and to examine students’ STEM-related attitudes, engagement and future work intentions both before and after their participation in the STEM in Situ project. The findings highlight the outcomes of the STEM in Situ workshops upon female students attitudes and engagements with STEM careers. The findings have the potential to inform future policies related to STEM interventions for young women.

Generative artificial intelligence (GenAI) has revolutionized many aspects of our daily lives, including how we interact with and perceive media. GenAI's capability to generate images, especially AI models like DALL-E, has opened new avenues for visual representation. However, these technological advancements are deeply intertwined with societal norms and narratives, particularly concerning gendered roles and professions. Content analysis was utilized to identify patterns in AI-generated images of early childhood educators and leaders, including the representation of gender and other characteristics. Through a feminist post-structuralist theoretical approach, this paper seeks to understand whether AI perpetuates existing stereotypes or offers a medium to challenge and redefine them. Findings suggest that generative AI reinforces existing stereotypes associated with gender in professional roles, portraying early childhood educators and leaders in biased ways that conform to traditional stereotypes. This study adds to the broader literature about the impact of GenAI in reinforcing or dismantling stereotypes in early childhood education.

Climate change is intensifying the risks faced by children’s lives, as well as impacting their learning and education. How school communities in Bhutan are dealing with issues related to climate change is largely uncharted. This study examined how Bhutanese schools have been affected by the changing climate and how they were responding to it. The data sources included interviews with school principals, teachers, and district education officers, as well as observations, and document analysis. The global comprehensive school safety framework, along with a whole school approach to climate action framed the results of this study. Findings included that schools experienced multiple climate impacts and shocks affecting their children’s health, education, and the school system itself. Adaptation and mitigation measures including climate change education have yet to gain traction at the sector and school’s level. Schools, however, have attempted some actions such as through disaster management, and participatory environmental conservation and green school initiatives. Social capital was critical in filling the resource gap for initiating environmental and climate change actions, carried out through collective school, community, and student-based activities. Human capacities, policy and institutional structures, technical capacities, and capital (cultural and economic) constraints as well as slow mainstreaming into local development processes impeded efforts to build climate-resilient schools, including practices to contribute to greenhouse gases reduction. This study not only shed light on how climate change was affecting schools and children but also provided strong evidence for policymakers and relevant agencies to scale up interventions to enhance adaptation and mitigation practices.

This paper explores the pedagogical potential of GenerativeArtificial Intelligence (GenAI) in secondary education througha dialogic approach to teaching, learning and assessment. It pre-sents an ongoing action research project in collaboration witha high school in Western Australia, involving four teachers to inte-grate GenAI in their classrooms. The study aims to develop andevaluate innovative pedagogies for leveraging GenAI to enhanceeducational practices and student learning outcomes across threeaction research teams focusing on critical questioning, assessmentand differentiation. Drawing on Bakhtin’s concept of heteroglossia,the study conceptualizes GenAI not as a definitive knowledgeprovider but as a dialogic agent that facilitates collaborative dialo-gue and co-construction of knowledge among students. This per-spective aims to encourage students to critically engage with AI-generated content and integrate multiple viewpoints into theirlearning, thus fostering key epistemic skills. Initial findings demon-strate active student engagement in dialogues with GenAI, high-lighting the use of follow-up questions that indicate critical thinkingand creativity. These findings underscore the significance of inte-grating multiple perspectives and fostering epistemic skills amongstudents, promoting a comprehensive and ethical approach to AIuse in education. The research calls for further exploration ofGenAI’s pedagogic potential and its broader implications for edu-cational practices, suggesting a promising avenue for pedagogicalinnovation and the development of critical thinking skills in thedigital age.

This article offers a critical examination of First Nations1 perspectives in the newly revised Australian Science Curriculum. Despite recent revisions, our analysis indicates that the curriculum continues to marginalise and overlook the rich scientific contributions of First Nations communities in Australia. We employ a science capital lens to probe the design of the curriculum. While the curriculum incorporates elaborations related to First Nations contexts, they are offered to educators as optional, or only intended to be embedded through content descriptions as a cross-curriculum priority. Our research proposes the possibility of a transformative curriculum, one that better acknowledges and embeds First Nations science capital. Emphasising the need for local relevance, this approach advocates for co-constructing learning experiences with First Nations communities and repositioning First Nations perspectives in the curriculum. The study explores the dynamics of collaborating with First Nations stakeholders in curriculum design, highlighting how such partnerships can enrich the exchange of science capital and contribute to a more holistic science education. This integration is crucial for preparing all students to navigate and contribute to the increasingly diverse and multicultural dimensions of society-which include different perspectives of science and science capital, and ultimately promotes a more inclusive science education.

The introduction of generative artificial intelligence (GenAI) tools like ChatGPT has raisedmany challenging questions about the nature of teaching, learning, and assessment in everysubject area, including science. Unlike other disciplines, natural science is unique because theontological and epistemological understanding of nature is fundamentally rooted in our interactionwith material objects in the physical world. GenAI, powered by statistical probabilityarising from a massive corpus of text, is devoid of any connection to the physical world. Theuse of GenAI thus raises concerns about our connection to reality and its effect on scienceeducation. This paper emphasizes the importance of materiality (or material reality) in shapingscientific knowledge and argues for its recognition in the era of GenAI. Drawing on theperspectives of new materialism and science studies, the paper highlights how materialityforms an indispensable aspect of human knowledge and meaning-making, particularly in thediscipline of science. It further explains how materiality is central to the epistemic authorityof science and cautions the outputs generated by GenAI that lack contextualization to a materialreality. The paper concludes by providing recommendations for research and teachingthat recognize the role of materiality in the context of GenAI, specifically in practical work,scientific argumentation, and learning with GenAI. As we navigate a future dominated byGenAI, understanding how the epistemic authority of science arises from our connection tothe physical world will become a crucial consideration in science education.

Climate change is accelerating, and it is becoming clearer that the education sector in countries around the world will bear the brunt of the effects. Research into the impacts of climate change on schools and children as well as their engagement in responses is still sparse, albeit growing. In this paper, Bourdieu’s concepts of field, habitus, and capital are applied to better understand the practice of school climate response and the role schools may have in climate action. The analysis is based on interviews with Bhutanese school leaders, teachers, and district education officials. The results indicated that the school system and learners experienced multiple impacts and shocks. Opportunities to engage in practices for climate response were constrained by access to capital (cultural economic) and policy and institutional structures. Socio-cultural dispositions showed a noteworthy influence on school stakeholders’ engagement in environmental and climate response interventions. Social capital acted as a catalyst for initiating environmental and climate change actions, carried out through collective school, community, and student-based activities. This research adds to the literature by exploring opportunities for social transformation that may lead to more effective practices in school climate action and more broadly, the scope of Bhutanese schools to respond to socio-scientific issues in science education.

Immersive virtual reality (IVR) offers significant transformative potential for science education by supporting learning experiences that deeply engage students and improve their understanding of scientific concepts. Despite considerable interest, research on the use of IVR in science education is still in its formative stage. Currently, there is a substantial gap in a tool that can help stakeholders evaluate key elements of immersive software for science education contexts. This research addresses this gap by conceptualising and applying a framework designed to assist educators, researchers, and designers in assessing essential components of an immersive science application. The framework highlights three key components: IVR technological affordances, the exploration of science within IVR, and scientific representations. These components are synthesised into the Immersive Representations Model (IRM). Employing screen capture methodology, we evaluated the application and significance of the IRM. This study pioneers a structured approach to evaluating immersive technologies in science education.

The proliferation of generative artificial intelligence (GenAI) means we are witnessing transformative change in education. While GenAI offers exciting possibilities for personalised learning and innovative teaching methodologies, its potential for reinforcing biases and perpetuating stereotypes poses ethical and pedagogical concerns. This article aims to critically examine the images produced by the integration of DALL-E 3 and ChatGPT, focusing on representations of science classrooms and educators. Applying a capital lens, we analyse how these images portray forms of culture (embodied, objectified and institutionalised) and explore if these depictions align with, or contest, stereotypical representations of science education. The science classroom imagery showcased a variety of settings, from what the GenAI described as vintage to contemporary. Our findings reveal the presence of stereotypical elements associated with science educators, including white-lab coats, goggles and beakers. While the images often align with stereotypical views, they also introduce elements of diversity. This article highlights the importance for ongoing vigilance about issues of equity, representation, bias and transparency in GenAI artefacts. This study contributes to broader discourses about the impact of GenAI in reinforcing or dismantling stereotypes associated with science education.

EDITORIAL article Front. Educ., 17 July 2023Sec. Digital Learning Innovations Volume 8 - 2023 | https://doi.org/10.3389/feduc.2023.1239797