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The advent of generative artificial intelligence (AI) offers transformative potential in the field of education. The study explores three main areas: (1) How did ChatGPT answer questions related to science education? (2) What are some ways educators could utilise ChatGPT in their science pedagogy? and (3) How has ChatGPT been utilised in this study, and what are my reflections about its use as a research tool? This exploratory research applies a self-study methodology to investigate the technology. Impressively, ChatGPT’s output often aligned with key themes in the research. However, as it currently stands, ChatGPT runs the risk of positioning itself as the ultimate epistemic authority, where a single truth is assumed without a proper grounding in evidence or presented with sufficient qualifications. Key ethical concerns associated with AI include its potential environmental impact, issues related to content moderation, and the risk of copyright infringement. It is important for educators to model responsible use of ChatGPT, prioritise critical thinking, and be clear about expectations. ChatGPT is likely to be a useful tool for educators designing science units, rubrics, and quizzes. Educators should critically evaluate any AI-generated resource and adapt it to their specific teaching contexts. ChatGPT was used as a research tool for assistance with editing and to experiment with making the research narrative clearer. The intention of the paper is to act as a catalyst for a broader conversation about the use of generative AI in science education.

Educational researchers and practitioners assert that supportive school and classroom climates can positively influence the academic outcomes of students, thus potentially reducing academic achievement gaps between students and schools of different socioeconomic status (SES) backgrounds. Nonetheless, scientific evidence establishing directional links and mechanisms between SES, school climate, and academic performance is inconclusive. This comprehensive review of studies dating back to the year 2000 examined whether a positive climate can successfully disrupt the associations between low SES and poor academic achievement. Positive climate was found to mitigate the negative contribution of weak SES background on academic achievement; however, most studies do not provide a basis for deducing a directional influence and causal relations. Additional research is encouraged to establish the nature of impact positive climate has on academic achievement and a multifaceted body of knowledge regarding the multilevel climate dimensions related to academic achievement.

In this article, I argue that academic language is a raciolinguistic ideology that frames racialized students as linguistically deficient and in need of remediation. I propose language architecture as an alternative framing of language that can serve as a point of entry for resisting these raciolinguistic ideologies in both research and practice. I use this framework as a lens for analyzing the literacy demands of the Common Core State Standard (CCSS). Using data collected as part of a larger ethnographic study, I illustrate how Latinx children from bilingual communities have unique opportunities for engaging in the language architecture called for in the standards. I then describe a unit plan that I developed from this perspective. I end with a call for situating language architecture within broader political struggles seeking to dismantle the political and economic inequities that are the root causes of deficit perspectives of Latinxs and other racialized students.

This qualitative study examines how pre-service teachers incorporate asset-based, multilingual and equity-focused pedagogies, prioritizing students’ funds of knowledge and advocating for culturally relevant and sustaining approaches. Through an analysis of 92 social studies unit plans developed by pre-service teachers in the final semester of their teacher credential program, the study highlights students’ efforts in integrating locally relevant curriculum, translanguaging, and inclusive pedagogies in their teaching. The study emphasizes the transformative potential of integrating linguistic and cultural resources into content area teaching to cultivate inclusive and equitable learning environments for emergent bilinguals.

Virtual and augmented (VAR) technology is in the early stages of being adopted as a teaching platform in higher education. The technology can facilitate immersive learning in environments that are not usually physically accessible to students via 3D models and interactive 360° videos. To date, adoption rates of VAR technology for teaching have not been well described across a higher education institution. Further, there is an absence of information on the optimal VAR laboratory designs and cost per student. In this study, a purpose designed virtual reality laboratory was formed in 2017 at The University of Sydney, housing 26 Oculus Rift headset units. An evaluation was conducted on the design, costs, rates of teaching adoption and student experiences over five teaching periods (2.5 years). Over this period, 4833 students were taught in the laboratory across 7952 student visits. The laboratory was used most by the Faculty of Engineering (53%), followed by the Faculty of Arts & Social Science (23.8%) and Faculty of Science (23.2%). For engineering, the units of study using the laboratory represented only 1.4% of annual faculty subjects offered. This confirms that adoption was in the initial stage of innovation diffusion. The laboratory saw a 250% increase in student numbers over the period of evaluation and 71.5% of students surveyed (n = 295) reported enhanced learning outcomes. The cost per visit was only AU$ 19.50. These findings give confidence to higher education institutions that the right VAR technology infrastructure is a sound educational investment for the future.

The use of generative artificial intelligence (GenAI) in academia is a subjective and hotly debated topic. Currently, there are no agreed guidelines towards the usage of GenAI systems in higher education (HE) and, thus, it is still unclear how to make effective use of the technology for teaching and learning practice. This paper provides an overview of the current state of research on GenAI for teaching and learning in HE. To this end, this study conducted a systematic review of relevant studies indexed by Scopus, using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. The search criteria revealed a total of 625 research papers, of which 355 met the final inclusion criteria. The findings from the review showed the current state and the future trends in documents, citations, document sources/authors, keywords, and co-authorship. The research gaps identified suggest that while some authors have looked at understanding the detection of AI-generated text, it may be beneficial to understand how GenAI can be incorporated into supporting the educational curriculum for assessments, teaching, and learning delivery. Furthermore, there is a need for additional interdisciplinary, multidimensional studies in HE through collaboration. This will strengthen the awareness and understanding of students, tutors, and other stakeholders, which will be instrumental in formulating guidelines, frameworks, and policies for GenAI usage.

BackgroundFew tools or rubrics exist to assess the quality of integrated STEM curricula, and existing tools focus on checklists of characteristics of integrated STEM. While such instruments provide important information about the presence and quality of certain curricular components, they do not assess the level and nature of integration of the curriculum as a whole. Thus, this study explores the development of a process focused to understand the nature of integration within a STEM curriculum unit.FindingsA conceptual flow graphic (CFG) was constructed for 50 integrated STEM curriculum units. Patterns in the nature of the interdisciplinary connections were used to categorize and understand the nature of integration and curricular coherence within each unit. The units formed four broad types of integrated STEM curricula: (i) coherent science unit with loosely connected engineering design challenge (EDC), (ii) engineering design-focused unit with limited connections to science content, (iii) engineering design unit with science content as context, and (iv) integrated and coherent STEM units. All physical science units were in the integrated and coherent category with strong conceptual integration between the main science concepts and the EDC. Curricula based in the Earth and life sciences generally lacked conceptual integration between the science content and the EDC and relied on the engineering design process to provide a coherent storyline for the unit.ConclusionsOur study shows that engineering practices can serve as a contextual integrator within a STEM unit. The utilization of an EDC also provides the potential for conceptual integration because engineering is grounded in the application of science and mathematics. Integrated STEM curricula that purposefully include science and mathematics concepts necessary to develop solutions to the EDC engage students in authentic engineering experiences and provide conceptual integration between the disciplines. However, the alignment of grade-level science standards with the EDC can be problematic, particularly in life science and Earth science. The CFG process provides a tool for determining the nature of integration between science and mathematics content and an EDC. These connections can be conceptual and/or contextual, as both forms of integration are appropriate depending on the instructional goals.

Machine Learning (ML) is becoming increasingly present in our lives. Thus, it is important to introduce ML already in High School, enabling young people to become conscious users and creators of intelligent solutions. Yet, as typically ML is taught only in higher education, there is still a lack of knowledge on how to properly teach younger students. Therefore, in this systematic literature review, we analyze findings on teaching ML in High School with regard to content, pedagogical strategy, and technology. Results show that High School students were able to understand and apply basic ML concepts, algorithms and tasks. Pedagogical strategies focusing on active problem/project-based hands-on approaches were successful in engaging students and demonstrated positive learning effects. Visual as well as text-based programming environments supported students to build ML models in an effective way. Yet, the review also identified the need for more rigorous evaluations on how to teach ML.

Critical thinking is a metacognitive process that, through purposeful, self-regulatory reflective judgment; skills of analysis, evaluation and inference; and a disposition towards thinking, increases the chances of producing a logical conclusion to an argument or solution to a problem. Critical thinking is vital for not only educational achievement, but also continuous professional development, as it is necessary in social and interpersonal contexts, where adequate decision-making and problem-solving are necessary on a daily basis—which is of particular relevance to adult distance learners, many of whom return to learning to further their professional development. Though a large body of extant research focuses on traditional BA student’s critical thinking performance, there is a dearth of research conducted on more mature adults’ critical thinking. ADL provides a unique opportunity to explore these abilities and the effects of critical thinking instruction through distance learning on such performance. Given the potential benefits linked with critical thinking and associated with adult distance learning, the aim of the current, retrospective, exploratory case study was to examine the effects of an adult distance learning critical thinking module, taught through a BA Training and Education programme, on critical thinking performance. A series of six paired samples t-tests were conducted in order to assess the performance of 95 ADLs from pre-to-post-testing on overall critical thinking performance and the critical thinking sub-scales of hypothesis-testing; verbal reasoning; argument analysis; judging likelihood an uncertainty; and problem-solving. Correlational analysis was also conducted. Results revealed a significant increase from pre-to-post-intervention on overall critical thinking performance, as well as all critical thinking sub-scale performances. However, there were no effects of active engagement with the module, disposition towards thinking or motivation towards learning on critical thinking performance. Results are discussed in light of theory and research on critical thinking.