Navigating the intersection of generative artificial intelligence and democratic pedagogy in music education in Macau

Purpose This study aims to critically examine the socio-technical, economic and governance challenges emerging at the intersection of Generative artificial intelligence (AI) and Urban AI. By foregrounding the metaphor of “the moon and the ghetto” (Nelson, 1977, 2011), the issue invites contributions that interrogate the gap between technological capability and institutional justice. The purpose is to foster a multidisciplinary dialogue–spanning applied economics, public policy, AI ethics and urban governance – that can inform trustworthy, inclusive and democratically grounded AI practices. Contributors are encouraged to explore not just what GenAI can do, but for whom, how and with what consequences. Design/methodology/approach This study draws upon interdisciplinary literature from public policy, innovation studies, digital governance and urban sociology to frame the emerging governance challenges of Generative AI and Urban AI. It builds a conceptual foundation by synthesizing insights from comparative city case studies, innovation systems theory and normative policy frameworks. The approach is interpretive and exploratory, aiming to situate AI technologies within broader institutional, geopolitical and socio-economic contexts. The study invites contributions that adopt empirical, theoretical or practice-based methodologies addressing the governance of GenAI in cities and regions. Findings This study identifies a critical gap between the rapid technological advancements in Generative AI and the institutional readiness of public governance systems – particularly in urban contexts. It finds that current policy frameworks often prioritize efficiency and innovationism over democratic legitimacy, civic trust and inclusive design. Drawing on comparative global city experiences, it highlights the risk of reinforcing power asymmetries without robust accountability mechanisms. The analysis suggests that trustworthy AI is not a purely technical attribute but a political and institutional achievement, requiring participatory governance architectures and innovation systems grounded in public value and civic engagement. Research limitations/implications As an editorial introduction, this study does not present original empirical data but synthesizes key theoretical frameworks, case studies and policy debates to guide future research. Its analytical scope is conceptual and comparative, offering a foundation for submissions that further investigate Generative and Urban AI through empirical, normative and practice-based lenses. The limitations lie in its broad coverage and reliance on secondary sources. Nonetheless, it provides an agenda-setting contribution by highlighting the urgent need for interdisciplinary research into how AI reshapes public governance, institutional legitimacy and urban democratic futures. Practical implications This editorial offers a structured framework for policymakers, urban planners, technologists and public administrators to critically assess the governance of Generative and Urban AI systems. By highlighting international case studies and conceptual tools – such as public algorithmic infrastructures, civic trust frameworks and anticipatory governance – the article underscores the importance of institutional design, regulatory foresight and civic engagement. It invites practitioners to shift from techno-solutionist approaches toward inclusive, democratic and place-based AI governance. The reflections aim to support the development of trustworthy AI policies that are grounded in legitimacy, accountability and societal needs, particularly in urban and regional contexts. Social implications The editorial underscores that Generative and Urban AI systems are not socially neutral but carry significant implications for equity, representation and democratic legitimacy. These technologies risk reinforcing existing social hierarchies and systemic biases if not governed inclusively. This study calls for reimagining trust not as a technical feature but as a relational, contested dynamic between institutions and citizens. It encourages submissions that examine how AI reshapes the urban social contract, affects marginalized communities and challenges existing civic infrastructures. The goal is to promote AI governance frameworks that are pluralistic, just and reflective of diverse societal values and lived experiences. Originality/value This editorial offers a timely and conceptually grounded intervention into the emerging field of Urban AI and Generative AI governance. By framing the challenges through Richard R. Nelson’s metaphor of The Moon and the Ghetto, this study foregrounds the gap between technical capabilities and enduring societal injustices. The contribution lies in its interdisciplinary synthesis – bridging innovation systems, AI ethics, public policy and urban governance. It introduces a critical framework for assessing “trustworthy AI” not as a technical goal but as a democratic achievement and encourages research that is policy-relevant, equity-oriented and attuned to the institutional realities of AI in cities.

Aim/Purpose: This study aimed to investigate the potential of integrating Generative Artificial Intelligence (GenAI) in tertiary education. It examined current practices among teachers and learners regarding GenAI, as well as their perceptions of its benefits and challenges. Background: Higher education worldwide is seeing the increasing use of GenAI. However, its usage patterns and teachers’ and learners’ perceptions of its adoption are yet to be studied. The feasibility and viability of this emerging tool can be assessed by examining early usage patterns as predictors of formal adoption, as supported by the Technology Acceptance Model (TAM) and the Task-Technology Fit (TTF) frameworks. This study aims to fill that gap by examining both teachers’ and students’ practices and perceptions regarding various aspects of AI and its adoption in education. Methodology: A mixed-method approach was employed. Data were collected from 44 teachers and 186 students at Jashore University of Science and Technology through workshops and structured questionnaires based on the TAM and TTF frameworks. Quantitative data were analyzed with SPSS and MS Excel, while qualitative data were thematically analyzed. Contribution: This study contributes empirical evidence on the adoption of GenAI in a South Asian tertiary education context, enriching the body of knowledge on technology acceptance, digital pedagogy, and GenAI in education policy. By revealing the pictures of relevant variables of Generative Artificial Intelligence in Education (GenAIEd) in a unique context, such as Bangladesh, the findings have implications for similar situations. They can inform others about possible challenges and the usefulness of GenAIEd. Findings: Teachers and students are both moderately familiar with GenAI. The teachers primarily use it to prepare courses and materials, while students sporadically engage with GenAI, mainly for academic problem-solving, and they emphasize its role in personalized, learner-centered learning. GenAI familiarity is found to be a strong predictor of usage frequency. However, teachers express concerns about the reliability of GenAI, ethical implications, and the potential for deskilling. While the benefits and usefulness dominate, possible challenges and threats are marginally associated with the future adoption and use of GenAI. This finding is unique because, despite the overpowering ‘ease of use’ of the TAM model, ‘benefits or usefulness’ of the TTF model, challenges, and threats have been found as catalysts for GenAI adoption. Recommendations for Practitioners: Practitioners are to utilize GenAI to support, rather than replace, their teaching expertise. They should also encourage students to strike a balance between GenAI-assisted learning, critical thinking, and independent work. Furthermore, the institutions should introduce guidelines to ensure the ethical use of GenAI and academic integrity. Recommendation for Researchers: Researchers should explore the longitudinal effects of GenAI adoption on learning outcomes and skill development. They can also conduct comparative studies across different universities and disciplines. Investigating the role of GenAI in inclusive education and support for learners from disadvantaged backgrounds also demands research focus. Impact on Society: The findings highlight how GenAI can transform higher education in Bangladesh and similar contexts. It shows the importance of addressing the risks of overreliance and the unethical use of GenAI for effective learning. A balanced adoption could strengthen human–technology collaboration in education. On the other hand, it has revealed the aspects of GenAI, preferred by educators, that AI developers should consider. Future Research: Further studies should examine hybrid learning models that integrate GenAI with human expertise. Cross-cultural perspectives on GenAI in education remain another area of study. Furthermore, studies should be carried out to develop frameworks for maintaining academic authenticity while GenAI is being used in education.

Introduction Author Arthur C. Clarke famously argued that in science fiction literature “any sufficiently advanced technology is indistinguishable from magic” (Clarke). On 30 November 2022, technology company OpenAI publicly released their Large Language Model (LLM)-based chatbot ChatGPT (Chat Generative Pre-Trained Transformer), and instantly it was hailed as world-changing. Initial media stories about ChatGPT highlighted the speed with which it generated new material as evidence that this tool might be both genuinely creative and actually intelligent, in both exciting and disturbing ways. Indeed, ChatGPT is part of a larger pool of Generative Artificial Intelligence (AI) tools that can very quickly generate seemingly novel outputs in a variety of media formats based on text prompts written by users. Yet, claims that AI has become sentient, or has even reached a recognisable level of general intelligence, remain in the realm of science fiction, for now at least (Leaver). That has not stopped technology companies, scientists, and others from suggesting that super-smart AI is just around the corner. Exemplifying this, the same people creating generative AI are also vocal signatories of public letters that ostensibly call for a temporary halt in AI development, but these letters are simultaneously feeding the myth that these tools are so powerful that they are the early form of imminent super-intelligent machines. For many people, the combination of AI technologies and media hype means generative AIs are basically magical insomuch as their workings seem impenetrable, and their existence could ostensibly change the world. This article explores how the hype around ChatGPT and generative AI was deployed across the first six months of 2023, and how these technologies were positioned as either utopian or dystopian, always seemingly magical, but never banal. We look at some initial responses to generative AI, ranging from schools in Australia to picket lines in Hollywood. We offer a critique of the utopian/dystopian binary positioning of generative AI, aligning with critics who rightly argue that focussing on these extremes displaces the more grounded and immediate challenges generative AI bring that need urgent answers. Finally, we loop back to the role of schools and educators in repositioning generative AI as something to be tested, examined, scrutinised, and played with both to ground understandings of generative AI, while also preparing today’s students for a future where these tools will be part of their work and cultural landscapes. Hype, Schools, and Hollywood In December 2022, one month after OpenAI launched ChatGPT, Elon Musk tweeted: “ChatGPT is scary good. We are not far from dangerously strong AI”. Musk’s post was retweeted 9400 times, liked 73 thousand times, and presumably seen by most of his 150 million Twitter followers. This type of engagement typified the early hype and language that surrounded the launch of ChatGPT, with reports that “crypto” had been replaced by generative AI as the “hot tech topic” and hopes that it would be “‘transformative’ for business” (Browne). By March 2023, global economic analysts at Goldman Sachs had released a report on the potentially transformative effects of generative AI, saying that it marked the “brink of a rapid acceleration in task automation that will drive labor cost savings and raise productivity” (Hatzius et al.). Further, they concluded that “its ability to generate content that is indistinguishable from human-created output and to break down communication barriers between humans and machines reflects a major advancement with potentially large macroeconomic effects” (Hatzius et al.). Speculation about the potentially transformative power and reach of generative AI technology was reinforced by warnings that it could also lead to “significant disruption” of the labour market, and the potential automation of up to 300 million jobs, with associated job losses for humans (Hatzius et al.). In addition, there was widespread buzz that ChatGPT’s “rationalization process may evidence human-like cognition” (Browne), claims that were supported by the emergent language of ChatGPT. The technology was explained as being “trained” on a “corpus” of datasets, using a “neural network” capable of producing “natural language“” (Dsouza), positioning the technology as human-like, and more than ‘artificial’ intelligence. Incorrect responses or errors produced by the tech were termed “hallucinations”, akin to magical thinking, which OpenAI founder Sam Altman insisted wasn’t a word that he associated with sentience (Intelligencer staff). Indeed, Altman asserts that he rejects moves to “anthropomorphize” (Intelligencer staff) the technology; however, arguably the language, hype, and Altman’s well-publicised misgivings about ChatGPT have had the combined effect of shaping our understanding of this generative AI as alive, vast, fast-moving, and potentially lethal to humanity. Unsurprisingly, the hype around the transformative effects of ChatGPT and its ability to generate ‘human-like’ answers and sophisticated essay-style responses was matched by a concomitant panic throughout educational institutions. The beginning of the 2023 Australian school year was marked by schools and state education ministers meeting to discuss the emerging problem of ChatGPT in the education system (Hiatt). Every state in Australia, bar South Australia, banned the use of the technology in public schools, with a “national expert task force” formed to “guide” schools on how to navigate ChatGPT in the classroom (Hiatt). Globally, schools banned the technology amid fears that students could use it to generate convincing essay responses whose plagiarism would be undetectable with current software (Clarence-Smith). Some schools banned the technology citing concerns that it would have a “negative impact on student learning”, while others cited its “lack of reliable safeguards preventing these tools exposing students to potentially explicit and harmful content” (Cassidy). ChatGPT investor Musk famously tweeted, “It’s a new world. Goodbye homework!”, further fuelling the growing alarm about the freely available technology that could “churn out convincing essays which can't be detected by their existing anti-plagiarism software” (Clarence-Smith). Universities were reported to be moving towards more “in-person supervision and increased paper assessments” (SBS), rather than essay-style assessments, in a bid to out-manoeuvre ChatGPT’s plagiarism potential. Seven months on, concerns about the technology seem to have been dialled back, with educators more curious about the ways the technology can be integrated into the classroom to good effect (Liu et al.); however, the full implications and impacts of the generative AI are still emerging. In May 2023, the Writer’s Guild of America (WGA), the union representing screenwriters across the US creative industries, went on strike, and one of their core issues were “regulations on the use of artificial intelligence in writing” (Porter). Early in the negotiations, Chris Keyser, co-chair of the WGA’s negotiating committee, lamented that “no one knows exactly what AI’s going to be, but the fact that the companies won’t talk about it is the best indication we’ve had that we have a reason to fear it” (Grobar). At the same time, the Screen Actors’ Guild (SAG) warned that members were being asked to agree to contracts that stipulated that an actor’s voice could be re-used in future scenarios without that actor’s additional consent, potentially reducing actors to a dataset to be animated by generative AI technologies (Scheiber and Koblin). In a statement issued by SAG, they made their position clear that the creation or (re)animation of any digital likeness of any part of an actor must be recognised as labour and properly paid, also warning that any attempt to legislate around these rights should be strongly resisted (Screen Actors Guild). Unlike the more sensationalised hype, the WGA and SAG responses to generative AI are grounded in labour relations. These unions quite rightly fear the immediate future where human labour could be augmented, reclassified, and exploited by, and in the name of, algorithmic systems. Screenwriters, for example, might be hired at much lower pay rates to edit scripts first generated by ChatGPT, even if those editors would really be doing most of the creative work to turn something clichéd and predictable into something more appealing. Rather than a dystopian world where machines do all the work, the WGA and SAG protests railed against a world where workers would be paid less because executives could pretend generative AI was doing most of the work (Bender). The Open Letter and Promotion of AI Panic In an open letter that received enormous press and media uptake, many of the leading figures in AI called for a pause in AI development since “advanced AI could represent a profound change in the history of life on Earth”; they warned early 2023 had already seen “an out-of-control race to develop and deploy ever more powerful digital minds that no one – not even their creators – can understand, predict, or reliably control” (Future of Life Institute). Further, the open letter signatories called on “all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4”, arguing that “labs and independent experts should use this pause to jointly develop and implement a set of shared safety protocols for advanced AI design and development that are rigorously audited and overseen by independent outside experts” (Future of Life Institute). Notably, many of the signatories work for the very companies involved in the “out-of-control race”. Indeed, while this letter could be read as a moment of ethical clarity for the AI industry, a more cynical reading might just be that in warning that their AIs could effectively destroy the w

The article describes the role of the scientific school by E. B. Abdullin in the formation and gradual development of the author’s research positions in the field of pedagogy of higher school music education. The first stage is characterized by considering problem of forming the artistic culture of future music teachers in a pedagogical University.The research embodied in his doctoral thesis, developed under the influence of the vectors of musicological and pedagogical thought. In includes analyses of the interaction of music with other art forms as manifestations of the General laws of artistic activity of man, revealed in the phenomenon of artistic culture. Pedagogical interpretation of artistic culture allowed us to consider this category: a) as an integral component of the content of music and pedagogical education; b) as a professionally significant quality of the music teacher’s personality. The concept changed the status of historical and theoretical training of future teachers-musicians, which received the working title “pedagogical musicology”. The content of pedagogical musicology was determined by a culturological approach to the learning process, designed to solve practical problems of mastering music in synchronic and diachronic interaction with other types of arts.The second stage is to expand the cultural component of professional training of music teacher, becoming a cultural orientation that integrates musicology, cultural studies and the pedagogy of music education on the basis of higher spiritual principles of music as an art form. The cultural direction allows developing new scientific approaches in the content of master’s educational programs in line with the methodological concept by E. B. Abdullin.

ABSTRACTBackgroundWith the rapid advancement of technology, the integration of Generative Artificial Intelligence (GAI) in education has gained considerable attention. Many studies have examined GAI's impact on learning outcomes, yet their conclusions are inconsistent, highlighting the need for a comprehensive review to clarify its overall effects and identify influential factors.ObjectivesThis study aims to conduct a meta‐analysis of the effects of GAI on student learning outcomes across cognitive, competency and affective dimensions. Additionally, it seeks to explore how various moderating factors, including subject discipline, instructional duration, knowledge type, prior knowledge and tool type, influence GAI's effectiveness.MethodsA meta‐analysis was performed on 34 experimental and quasi‐experimental studies published internationally. Effect sizes were calculated for overall learning outcomes and categorised by dimension. Further analysis was conducted to assess the influence of moderating variables on the impact of GAI.ResultsThe meta‐analysis indicates that Generative Artificial Intelligence has a significant positive impact on overall learning outcomes, with a combined effect size of 0.68 (p < 0.001). The impact is particularly pronounced in the cognitive dimension (g = 0.795) and the competency dimension (g = 0.711), while its effect on the affective dimension (g = 0.507) is moderate but still significant. The analysis of moderating variables reveals that the effectiveness of GAI is influenced by discipline type but is not significantly affected by instructional period, knowledge type, prior knowledge level, or tool type. Specifically, GAI exhibits the highest positive effects in mathematics, science and humanities, whereas its impact is relatively lower yet still significant in computer science and medical/nursing education. Additionally, GAI's effectiveness does not significantly differ across various instructional periods, different knowledge types, learners with varying prior knowledge levels, or different AI tool versions.ConclusionsTo optimise GAI's use in education, the study suggests aligning GAI with specific subject needs, adapting tools for different student levels, integrating GAI with traditional teaching and establishing monitoring mechanisms. These strategies aim to maximise GAI's positive impact on learning efficiency and quality across educational settings.

Purpose This study examines how non-traditional undergraduate students in online higher education environments utilize generative artificial intelligence (GenAI) as a tool to enhance academic engagement, personalize learning and improve productivity. Design/methodology/approach Using a quantitative, cross-sectional, correlational design, the study surveyed 491 non-traditional undergraduate students enrolled in online general education courses. Multiple regression analysis was conducted to evaluate how academic performance, skill confidence, technology proficiency and weekly time commitments relate to the frequency of GenAI use. Open-ended survey responses were also thematically analyzed to contextualize quantitative findings and explore student motivations, benefits and concerns. Findings Results indicate GenAI use is significantly associated with stronger academic performance and confidence in technology skills. Time constraints and lower academic confidence did not significantly predict use. High-performing and digitally proficient students were more likely to adopt GenAI to deepen understanding, generate ideas and streamline academic tasks. Qualitative responses described GenAI as a supportive thinking partner, while also highlighting ethical concerns around authorship, accuracy and academic integrity. Research limitations/implications This study examines GenAI use within a single large online university serving primarily adult, non-traditional learners in a flexible, asynchronous model. Findings should therefore be interpreted as contextually situated rather than broadly generalizable. The cross-sectional design captures adoption at an early stage and does not permit causal inference or analysis of long-term learning outcomes. Reliance on self-reported measures may introduce response bias. Future research should employ longitudinal, multi-institutional and mixed-method designs to examine how GenAI use evolves over time and how institutional context, discipline and learner demographics shape patterns of strategic AI integration. Practical implications Findings indicate GenAI adoption in online higher education is most strongly associated with digital confidence and academic motivation rather than remediation. Institutions serving adult and non-traditional learners should therefore embed structured GenAI literacy within curricula, emphasizing ethical use, critical evaluation and academic voice preservation. Faculty should design assignments that promote transparent, reflective engagement with AI tools instead of prohibition-based policies. Institutional leaders must develop coherent, context-sensitive AI frameworks and invest in digital skill development to prevent widening equity gaps as AI becomes normalized within asynchronous, autonomy-driven learning environments. Social implications As GenAI becomes integrated into online higher education, its social impact extends beyond productivity to issues of digital agency, equity and access. In adult-serving, asynchronous environments where learners self-manage academic decisions, disparities in digital fluency may amplify existing inequalities. Without intentional institutional support, students with lower technological confidence risk marginalization as AI-enhanced workflows become normative. Promoting inclusive, transparent and ethically guided GenAI integration can strengthen learner agency and participation, particularly among adult and non-traditional students balancing complex external responsibilities. Equitable AI adoption requires investment in digital empowerment alongside clear institutional standards. Originality/value This study offers contextually grounded empirical insight into how adult, non-traditional undergraduates in online higher education strategically integrate GenAI into their learning practices. By situating adoption within frameworks of technology acceptance, self-directed learning and digital agency, the findings challenge deficit-based narratives and demonstrate that GenAI use is associated with academic confidence and digital fluency rather than remediation. The study contributes student-centered evidence from an autonomy-driven, asynchronous learning environment and provides theoretically informed guidance for ethical AI integration, digital equity initiatives and future longitudinal research on AI-supported learning.

How Can <i>IJDS</i> Authors, Reviewers, and Editors Use (and Misuse) Generative AI?

The integration of generative artificial intelligence (GAI) in education has shown the potential to improve learning outcomes, yet its impact on self‐regulated learning (SRL) in second language (L2) writing remains underexplored. This mixed‐methods study investigated the effects of a GAI chatbot tool on task strategy diversity, metacognitive awareness and writing performance among 40 undergraduate students in Eastern China over an 8‐week intervention. Participants were randomly assigned to an experimental group ( n = 20) using the GAI chatbot platform Tongyi.ai or a control group ( n = 20) relying on traditional resources. Data were collected using the Metacognitive Awareness Inventory (MAI), Strategy Inventory for Language Learning (SILL), writing performance assessments, participant interaction logs, reflective journals and semi‐structured interviews. Quantitative analysis revealed that the experimental group showed greater improvements in task strategy diversity, metacognitive awareness and writing performance than the control group. Qualitative analysis further indicated that GAI tools could facilitate task planning, promote adaptive strategy use and deepen metacognitive reflection. Despite these benefits, participants expressed concerns about the potential of over‐reliance on GAI and the accuracy of its generated content. The present study highlights the potential of GAI to enhance SRL in L2 writing by fostering adaptive task strategies and promoting metacognitive development, offering valuable implications for integrating GAI into L2 writing instruction. Practitioner notes What is already known about this topic Generative AI (GAI) tools have shown the potential to enhance various aspects of education, including personalized learning and feedback provision. Self‐regulated learning (SRL) is crucial for students' academic success, particularly in second language writing. Technology has been found to support the development of writing strategies and metacognitive skills. What this paper adds This study provides novel empirical evidence on how GAI tools influence undergraduate students' task strategies and metacognitive awareness in self‐regulated learning, specifically in L2 writing contexts. The research demonstrates that students using GAI tools developed more diverse and adaptive task strategies compared with those using traditional resources. The study reveals that GAI tool usage led to increased metacognitive awareness among students, enhancing their ability to plan, monitor and evaluate their writing processes. Implications for practice and policy Educators should consider integrating GAI tools into L2 writing instruction to support students' development of diverse task strategies and metacognitive skills. When implementing GAI in education, it is crucial to balance technology assistance with fostering students' independent thinking and creativity. Future research should explore the long‐term effects of GAI on self‐regulated learning and investigate its impact across different student populations and educational contexts. Educational institutions should develop guidelines for the ethical use of GAI tools in academic settings, addressing concerns about academic integrity and data privacy.

The purpose of the study is to predict possible trends in the impact of generative artificial intelligence, in particular ChatGPT technologies, on education. Predictive estimates are formed on the basis of expert discussions of the consequences of using these digital technologies in education, which are currently going on in the public space and in the scientific community. The main groups of expert opinions and scientific approaches are being identified and compared, which makes it possible to present a perspective vision of the processes of integrating generative artificial intelligence into education. Analysis and forecasting are mostly carried out on the example of the practice by using generative artificial intelligence in higher education, however, the main provisions and conclusions can be extrapolated to other levels of education.Materials and methods. In the course of the study, methods of qualitative analysis of expert opinions presented in the public space (in the media, social networks, on the websites of educational organizations and analytical agencies, in public speeches), as well as methods of meaningful analysis of scientific publications, were used. Grouping and classification of expert opinions and scientific approaches were carried out. The analysis also used the results of a sociological study conducted by means of online survey of students from the Plekhanov Russian University of Economics on a sample of more than 3 thousand people. Methods of social forecasting were used to form predictive estimates.Results. The analysis made it possible to conclude that public discourse on employing generative artificial intelligence in education is controversial. Five groups of expert opinions were identified regarding the impact of generative artificial intelligence on education, which differ as to the need for its use in educational organizations and the scope of educational transformations that can occur under its influence. The analysis of scientific discussions showed that scientific community has not finally determined the consequences of the practical impact of generative artificial intelligence on the field of education. At the same time, possible promising areas and problem areas of its use are being identified, as well as its potential to initiate new reforms in education. The following possible trends in the integration of generative artificial intelligence into education are predicted: gradual change in the paradigm of education towards creativity-oriented education; increase of the share and scope of using artificial intelligence technologies in education; formation of new legal and ethical standards governing the use of generative artificial intelligence in education; increasing the importance and changing the role of the lecturer.Conclusions. Generative artificial intelligence has all the potential for solving long-term tasks of developing education. However, rapid technological development is inevitably associated with numerous risks, which require the creation of a methodology for using generative artificial intelligence in education, improvement of regulatory framework and solution of ethical problems. A new qualitative level of integration of a human being and artificial intelligence in the educational sphere is the thing of the future. Such integration will contribute to improving the quality of human capital in line with rapidly developing technologies of 5.0 Industrial Revolution.

In the twenty-first century, many music education scholars seek to reconceptualize music education toward social justice. Critical pedagogy is at the fore-front of this shift. However, as teachers aim toward equity through employing critical pedagogy, some undesired effects of using this teaching approach may arise. In this paper, I consider the problematic side of critical pedagogy and ask two important questions: Are there any restrictions or limits placed on who can enact critical pedagogy in music education? And are there any so-called casualties of critical pedagogy in music education or in education more generally? To consider these questions philosophically, I employ a critical race lens to explore tenets of critical pedagogy and their applications to music education, as illustrated in the ideas and practices of four elementary music teachers who strove to challenge dominant paradigms of music education. By examining critical pedagogy in music education with a critical lens, I seek to illuminate the philosophical complexities and paradoxes of engaging critical pedagogy in the classroom.

Generative artificial intelligence (GenAI) is rapidly reframing academic practice, yet staff confidence to harness its potential remains uneven. Many university staff indicate that a “lack of familiarity with the technology, uncertainty about its use, or a lack of time to engage, contributed to their reluctance to use AI tools for their work” (McDonald, 2024). Building this confidence demands accessible opportunities that honour diverse starting points, and courageous experimentation which normalises trial-and-error—two of the principles guiding the University of Newcastle’s GenAI response. Our GenAI Tinker Workshops operationalise these principles by offering low-stakes, experimentation-based experiences where educators co-explore AI tools, reflect on ethical dimensions and iterate on real teaching artefacts. Playful, informal learning environments may lower cognitive load and heighten curiosity by reducing the focus on serious workplace implications (O’Hara &amp; Lo, 2025). Tinkering studios, for example, cultivate “permission to fail” that supports creativity and innovation (Henriksen, 2021). The informal, low-stakes format of the sessions promoted engagement, echoing Worth’s (2024) assertion that “AI playgrounds” backed by appropriate guidance can support learning. Responding to TEQSA’s call for structured capacity-building around GenAI (TEQSA, 2024), Learning Design &amp; Teaching Innovation designed monthly Tinker Workshops capped at roughly ten participants. Each 60-minute session invited academics to “tinker” with tools like ChatGPT, NapkinAI Copilot, NotebookLM and Gamma to draft real artefacts (course plans, presentations, podcasts, images, etc) for use in their teaching activities. Facilitators guide the participants through a simple introductory activity and focus on identifying opportunities to promote discussion in the group. Evaluation of the workshops, through informal participant feedback and in-session observations, showed an appreciation for this approach. Participants reported increased confidence not only with the tools but with navigating rapid change more broadly, citing reduced anxiety and a stronger sense of community. Participants noted possible operational benefits identified by exposure to various AI tools (example comments – “this will save me so much time” and “That just took about 20 seconds, it would take me three days to do the same.”). They also developed insights beyond the immediate operational focus of the activity (example comments – “I’d never thought of structuring my course that way, it’s really clear” and “This has really helped me think about how I use images and graphics in my lectures.”). Further, the informal nature of the sessions, with facilitator positioned as fellow learner rather than expert instructor supported honest discussion and debate around the implications (both positive and negative) of GenAI (example comments – “it’s just good to be able to talk about this stuff, and know everyone is in the same boat” and “I kind of knew that not all AI is not bad, but have not had the time to learn more.”). End of session discussion is guided towards awareness of the fallibility of GenAI outputs, and the importance of human expertise in co-creating learning experiences with GenAI. By embedding accessible, iterative, low-stakes Tinker Workshops alongside more traditional GenAI initiatives, we aim to directly tackle the challenge of uneven confidence driven by unfamiliarity, uncertainty and limited time. The format normalises trial-and-error, scaffolds ethical reflection and focuses “AI playground” tinkering on real world use cases—delivering immediate efficiency gains while supporting consideration of broader pedagogical impacts. Participants report reduced anxiety, stronger professional community and increased confidence in decision making around the use of GenAI. In short, structured opportunities to experiment safely are building adaptable educators who can collaborate with AI responsibly, meeting TEQSA’s call for capability-building and strengthening resilient, human-centred learning.

Reinventing assessments with ChatGPT and other online tools: Opportunities for GenAI-empowered assessment practices

Generative Artificial Intelligence (GenAI) is re-defining the way higher education design is taught and learned. The explosive growth of GenAI in design practice demands that design educators ensure students are prepared to enter the design profession with the knowledge and experience of using GenAI. To facilitate GenAI’s introduction in a project-based context, it is suggested that design educators use critical engagement as a starting point to assure students understand the strengths and weakness of GenAI in the creative design process. There is little guidance on how to systematically integrate GenAI in design studio practice while maintaining a critical perspective of the ethical issues it has engendered. This research explores student attitudes toward GenAI, frequency of its use, and student perception of its impact on their future design careers. A survey of a representative cohort of graphic design students (n = 17) reveals a pragmatic acceptance that GenAI will change how design is practiced and a concurrent willingness to learn more on how to use it effectively and ethically. The survey validates the need for design educators to engage and guide students critically in their understanding and use of GenAI within studio and professional practice.

This article explores the reflections of Australian law students on the use and integration of Generative Artificial Intelligence (GenAI) in the practical legal training law curriculum. Participants were enrolled as students in the Graduate Diploma in Legal Practice at Queensland University of Technology (QUT) between April and November 2024 and engaged with several GenAI use cases embedded in their law subjects. Surveys were used to assess participants’ perceptions of the incorporation of GenAI into the subjects. The findings indicated that some participants had no prior GenAI experience, but the majority had at least a limited experience. Participants reported that all GenAI use cases improved their GenAI literacy and that they were interested in engaging with different AI tools and applications and wanted to learn how to prompt more effectively. While students’ understanding of GenAI capabilities improved, they remain cautious about using GenAI in their future legal practice, particularly for tasks such as legal research, feedback on a video recordings and written communication. Having engaged with GenAI in their studies, participants reported feeling better prepared for entry into a legal profession that is increasingly incorporating the use of GenAI. Implications from this study include an increased understanding of how best to embed GenAI in legal curriculum and assessment to ensure law students are provided with opportunities to explore the appropriate and responsible use of GenAI and to develop their AI literacy skills.

Advances in generative artificial intelligence (GenAI) have created uncertainties and tensions in higher education, particularly concerning learning, equity and quality. Despite emerging empirical research, much current policy is based on assumptions about how and why students are using GenAI. This Pecha Kucha reports on 20 online focus groups involving 79 students from four Australian universities. Each focus group represents a mix of disciplines and levels of study (including undergraduate and postgraduate). We conducted reflexive thematic analysis, adopting a relational view of AI (Bearman &amp; Ajjawi, 2023) that supports a nuanced examination of how AI uses are enacted, understood, and contested within educational settings. Our study shows that students use GenAI in diverse and complex ways and their beliefs about GenAI contain ambiguity, contradictions, and tensions. In this pecha kucha we focus on five interrelated tensions, identified across participants, and selected as particularly significant and challenging for educators. The salience of these tensions varied across participants but, together, they paint a complex picture of student engagement with GenAI. Tension 1 is between student perceptions of AI in terms of enhanced efficiency and concerns about academic integrity. Students reported that GenAI tools could speed up writing, editing, summarising, and simplifying complex materials. However, many also feared that short-cuts and efficiencies could lead to accusations of cheating. Tension 2 is between widespread adoption of GenAI tools and ambiguous policy around acceptable use. Many students used a diverse range of GenAI tools, yet a number of participants voiced uncertainty about allowable use of GenAI in assessments. A perceived lack of clear and detailed guidance from universities created confusion and anxiety, and the development of personal rules to avoid accusations of academic misconduct. Tension 3 is between empowerment and dependency. AI tools were sometimes seen as reducing inequalities (e.g. for international students or those requiring language support). On the other hand, some students expressed concerns about becoming dependent on GenAI tools where tasks were made too easy, undermining learning and skill development. Tension 4 is between access and equity. Closely related to tension 2, here, the reduction of barriers to academic writing and accessing educational resources is contrasted with concerns around exacerbating inequalities due to variation in access and support. These concerns are amplified through diversity of engagement, beliefs of students and educators around acceptability, and contextual pressures (e.g. fear of being left behind, time pressures, the perceived stakes of assessment). Tension 5 is between beliefs about deepened engagement with learning materials and reduced quality or accuracy of GenAI output. Some students reported that GenAI tools could provide useful perspectives on resources or simplify complex texts. However, many voiced frustration that GenAI tools sometimes provided incorrect information, required verification or “missed the point”, which could lead to significant additional work. These tensions highlight areas where students need additional support and guidance. The overlaps and entanglements of these tensions make their navigation in higher education particularly complex. These findings suggest practical implications for educators, policymakers, and institutions. For instance, to better support students, institutions should continue to develop clear, context-sensitive guidelines that resolve ambiguities around acceptable use (Tensions 1 and 2) and provide concrete strategies to balance the benefits of efficiency with concerns over academic integrity and dependency (Tensions 1 and 3). Additionally, efforts should be made to ensure equitable access to GenAI tools and support (Tension 4) while helping students critically assess the quality of AI-generated content (Tension 5).