Learning from practice: How professionals use AI and what it means for higher 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 analyzes international and national experiences in the use of artificial intelligence (AI) tools in education and outlines the legal and regulatory framework for their implementation. The need for developing clear rules and recommendations on the ethical applying of generative AI in academia is emphasized. Current approaches to integrating AI technologies into the educational process of design specialties are examined. The research explores contemporary trends in the application of artificial intelligence technologies in education, particularly within the Design field. The purpose of the study is to analyze the potential of artificial intelligence (AI), especially generative models, in developing a product design concept using the Method of Focal Objects (MFO). Methodology. The study employs a comprehensive approach that combines theoretical analysis of recent scientific publications, a comparative method, and experimental modeling of the ideation process using generative AI tools. The Method of Focal Objects is considered as an algorithmic basis for creating new visual and functional solutions, while AI functions as an analytical assistant in selecting, combining, and visualizing creative ideas. Results. The study revealed that the rapid development and dissemination of AI technologies, particularly generative models, significantly affect the structure, content, and methodology of the educational process. The key stages of integrating AI into the process of developing a design concept are identified: defining characteristics/ properties of auxiliary objects, selecting associative series, generating creative combinations, analyzing results, and constructing a design model. It is demonstrated that the use of AI reduces the time required for idea generation, expands the designer’s associative field, and enables the emergence of unexpected yet potentially functional solutions. A practical scheme of interaction between MFO and AI is proposed. It is concluded that the higher education system have to adapt its approaches to learning, teaching, and assessment, taking into account technological innovations and the principles of academic integrity. Scientific novelty. For the first time, the methodological interaction between the Method of Focal Objects and generative AI in product design has been substantiated. A model of the creative process is proposed, in which AI algorithms act as a catalyst for creative thinking rather than a substitute for the designer. An analogy between the cognitive principles of the Method of Focal Objects and the architecture of generative models has been identified. Practical relevance. The findings can be applied in the professional training of designers to develop creative thinking skills and effective collaboration with AI tools. The proposed approach contributes to optimizing the conceptual design process, fostering an individual creative style, and enhancing the competitiveness of design solutions in the digital era. The results can also be used to develop methodological recommendations for integrating AI tools into higher education curricula. The conclusions of the study support the improvement of teacher training for working with generative technologies and the development of educational programs aimed at fostering ethical, digital, and civic competencies among students.

Marketing scientists as well as practitioners believe that artificial intelligence (AI) holds the promise of productivity gains for organizations. However, there has been little scientific research into these theories. This study investigates the role of AI in enhancing marketing productivity, deriving insights from a case study conducted with the marketing team of an industrial software start-up. Drawing upon Case Study Analysis by Yin (2018) and Participatory Action Research by Kemmis and McTaggart (2007), the study employs a combination of survey interviews, AI tool research and AI tool testings. Key findings indicate that productivity gains are more likely than productivity impairments with the use of marketing AI tools. This effect is even stronger when knowledge workers possess high levels of AI skills and utilize AI tools with suitable capabilities. Having closely analyzed six marketing disciplines, particularly SEO / content and design demonstrated significant productivity gains including generative AI (GAI) tools the team already subscribed to like ChatGPT 4 and Canva, but also new AI solutions. While an AI tool’s level of integration only showed a weak positive productivity impact, future studies are suggested to further investigate this variable by comparing the effects of less advanced but more accessible tools like generative AI versus highly advanced, but less accessible business AI. Having navigated the vast and dynamic landscape of AI tools, insights further emphasize the importance of AI experience sharing and informed decision-making, implying knowledge of own user rights and always staying updated on AI advancements. Zooming out from process level, the work's literature review further highlights the role of environmental and organizational AI enablers, like budget allocation, fostering AI trust and mindset, but also implementing AI routines and responsibilities. Overall, this research underscores the imperative for companies, especially startups and SMEs, to explore AI technology as a means to enhance productivity and gain a competitive edge.

This poster showcases a case study of an Australian higher education institution’s artificial intelligence (AI) literacy staff development program. It offers practical suggestions to ASCILITE attendees on how to empower academic and professional staff to navigate the unknown terrain of generative AI collaboratively and responsibly. Since the release of ChatGPT in November 2022, higher education institutions have been grappling with its impact on assessment, teaching and learning, and the world of work (CRADLE Blog, 2023) -culminating in the Tertiary Education Quality and Standards Agency (TEQSA) Request for Information (RFI) about how institutions will engage with AI and secure course integrity (TEQSA, 2024). Effective institutional responses to TEQSA’s RFI are predicated on staff at all levels rapidly developing their AI literacy in order to conceptualise and implement the curriculum and assessment changes required. AI literacy is generally accepted to include understanding of AI tools and how they work, discussion of ethical and societal implications and critical evaluation of their outputs, and competency in integration of AI ethically and effectively into daily practice (Chan & Colloton, 2024; Hibbert, Melanie et al., 2024; Hillier, 2023). This poses a significant challenge for institutions because of rapidly evolving AI tools and the diverse capabilities and starting points of large staff cohorts, including among third space support staff responsible for implementation. ECU's evolving strategy for building organisational capacity in AI literacy is outlined in this poster. The approach, which aligns with ECU’s Framework and Guidelines for Ethical and Productive Use of AI (Edith Cowan University, 2023), is designed to empower and enable staff. It intentionally incorporates connectivist and constructivist learning theories, informed by Fink's Taxonomy of Significant Learning (Fink, 2013) and Miller's Pyramid (Miller, 1990). This meant (a) providing essential foundational knowledge about AI, (b) developing practical skills through hands-on experience and exploration, and (c) fostering collective capability through sharing and collaboration. These efforts complemented initiatives to support student AI literacy through similar impactful interventions (Sullivan et al., 2024). In 2024, ECU implemented the following activities to support academic and professional staff: “AI 101” Canvas site: Covers how AI works, ethical and societal considerations, and AI in learning and teaching. “Explore AI” workshops: Focused on practical exploration of AI tools that generate both text and images, as well as ethics, research and assessment. “AI Digest” Viva Engage Community: Provides regular updates about AI. Generative AI tools: A series of tools for trials e.g., custom chatbots and image generators. Workshops co-designed with Schools: Explores generative AI in discipline-specific ways (including arts, humanities, business, law and performing arts) Despite currently being voluntary, these initiatives have received strong engagement and positive feedback to date. For example, all respondents to the Explore AI Session feedback forms said they would recommend the sessions to colleagues. 331 academic and professional staff have engaged with the AI 101 Canvas site so far, spending a median of 3 hours and 5 minutes in the course. 74% of the 50 respondents to the AI 101 evaluation form stated that their confidence levels improved after completing the course. ECU continues to iteratively improve its AI literacy offerings and expand staff engagement, collectively making sense of generative AI and its effects as an institution.

Generative artificial intelligence (AI) changes the picture of graduate education by providing personalized learning, automated feedback, intelligent research assistants, and automated content creation (George, 2023). AI tools will support doctoral students in text generation, language translation, responding to academic queries, and data collection and analysis and encourage self-learning and thinking development (Rasul et al., 2023; Zou & Huang, 2023). They also would be helpful for doctoral students working as teaching assistants and aiding in daily problems (Can et al., 2023; Parker et al., 2024). However, the rise of AI tools also leads to considerations of academic integrity, over-reliance on AI, misinformation, and the potential biases embedded in algorithms (George, 2023; Rasul et al., 2023). Echoing the opportunities and challenges of AI applications in research and learning, the ALISE Doctoral Students SIG wants to encourage a discussion on how doctoral students can use AI tools to empower us in the Ph.D. journey. The panel invites a diverse group of doctoral students/candidates to share how AI tools can facilitate data collection and analysis and their critical understanding of AI systems. Manar Alsaid will talk about using AI and machine learning to detect complex misinformation on social media. The talk aims to enhance our understanding of misinformation and reduce its negative impacts. This presentation will provide valuable insights for research on misinformation and information literacy. Adam Eric Berkowitz will introduce the black-box tinkering method that experimentally discerns how AI systems operate. The method enhances the transparency of AI systems, challenging the technocratic paradigm. With three examples, Berkowitz encourages attendees to learn what black-box tinkering is, how to identify cases using it, and potential opportunities to incorporate it in research. Anisah Herdiyanti will share insights from a study comparing transcripts generated by Otter.ai and Zoom Meetings. The presentation will highlight both the benefits and challenges of AI-based notes and transcription software, including technical concerns and the convenience of automated result delivery. The audience will enhance their understanding of AI tools in qualitative data transcribing and the ethical considerations in the process. Rebecca Bryant Penrose will showcase the use of HeyGen, an AI-based video generator and translation tool, in an international interview project between students at California State University Bakersfield and a Ukrainian artist/author. The presentation will increase awareness of the potential use of AI-based video and help researchers overcome language barriers in data collection. The panel will last 90 minutes, including a 5-minute introduction and a 5-minute wrap-up. Each panelist will have 10 minutes to present their topics, followed by 5-minute Q&As. A 25-minute moderated roundtable discussion will follow the panelists’ presentations to explore the potential use of different AI tools in research, including ChatGPT and AI-powered article summarizers. The panel’s learning outcomes include (1) Identifying challenges and opportunities to incorporate AI tools in research and study and (2) Explaining how to interact with AI tools to improve efficiency in research. It also provides a platform for doctoral students to share their knowledge of how AI changes research approaches and networks with each other.

Purpose: This analysis aims to propose guidelines for artificial intelligence (AI) research ethics in scientific publications, intending to inform publishers and academic institutional policies in order to guide them toward a coherent and consistent approach to AI research ethics.Methods: A literature-based thematic analysis was conducted. The study reviewed the publication policies of the top 10 journal publishers addressing the use of AI in scholarly publications as of October 2024. Thematic analysis using Atlas.ti identified themes and subthemes across the documents, which were consolidated into proposed research ethics guidelines for using generative AI and AI-assisted tools in scholarly publications.Results: The analysis revealed inconsistencies among publishers’ policies on AI use in research and publications. AI-assisted tools for grammar and formatting are generally accepted, but positions vary regarding generative AI tools used in pre-writing and research methods. Key themes identified include author accountability, human oversight, recognized and unrecognized uses of AI tools, and the necessity for transparency in disclosing AI usage. All publishers agree that AI tools cannot be listed as authors. Concerns involve biases, quality and reliability issues, compliance with intellectual property rights, and limitations of AI detection tools.Conclusion: The article highlights the significant knowledge gap and inconsistencies in guidelines for AI use in scientific research. There is an urgent need for unified ethical standards, and guidelines are proposed for distinguishing between the accepted use of AI-assisted tools and the cautious use of generative AI tools.

Generative artificial intelligence (AI) tools like ChatGPT hold the capacity to tremendously impact patterns of information seeking behavior in higher education, as students rely on AI tools for a variety of tasks in their daily life. This study examines the current state of how U.S. university students perceive and use AI chatbots versus traditional online search engines and academic library resources for academic information seeking and retrieval tasks. Based on an understanding of information seeking concepts drawn from existing information behavior research and theory, an electronic survey was distributed to 236 students from diverse demographic backgrounds, measuring information source use, preference, perceived relevance, and satisfaction across AI tools, search engines, and library databases. The results of the survey suggest that, while search engines like Google remain dominant for information retrieval in higher education, generative AI tools are an increasingly significant component of students’ information worlds. Younger students and international students are especially likely to use AI for academic tasks. Students who are frequent AI users also report higher satisfaction in the information supplied by AI models. These findings are indicative of a shifting ecology of information behavior where artificial intelligence serves both as a complement and a competitor to traditional information sources like search engines and university libraries, presenting important implications for information literacy instruction, academic library services, and educators navigating AI integration in higher education.

With the increasing use of Generative Artificial Intelligence (AI) tools like ChatGPT and Bard, universities face challenges in maintaining academic integrity. This research investigates the impact of these tools on learning outcomes (factual knowledge, comprehension, critical thinking) in selected universities of Ghana's Upper East Region during the 2023-2024 academic year. The study specifically analyzes changes in student comprehension and academic integrity concerns when using Generative AI for content generation, research assistance, and summarizing complex topics. A mixed-methods approach was employed, combining qualitative data from interviews and open-ended questions with quantitative analysis of survey data and academic records. The research focuses on three institutions: C. K. Tedam University of Technology and Applied Sciences, Bolgatanga Technical University, and Regentropfen University College. A purposive sampling technique recruited 150 participants (50 from each university) who had used Generative AI tools. Key findings show that 72% of students reported improved understanding of course material through Generative AI use, yet 75% cited academic integrity as a primary concern. Quantitative analysis revealed a weak to moderate positive correlation (r = 0.45) between AI tool usage and improved grades, with variations depending on the specific AI tasks performed. Qualitative data highlighted concerns about overreliance on AI and its impact on critical thinking skills. This research contributes to the ongoing debate on AI's role in education by providing valuable insights for educators and policymakers worldwide. The findings suggest that while AI tools can enhance comprehension, ethical considerations and potential drawbacks related to critical thinking require careful attention. The study concludes with recommendations for integrating AI literacy programs, developing ethical guidelines, and implementing advanced plagiarism detection systems to harness the benefits of Generative AI while mitigating risks to academic integrity. Although specific to the Upper East Region of Ghana, these insights may be applicable to other educational systems with similar characteristics.

Human beings are endowed with a natural curiosity and creativity, which motivate them to learn new things from their interactions with the world. Human learning has involved exploration and experimentation, which have allowed humans to discover new facts and principles, and to invent new artifacts and systems. Human learning has also affected human evolution, both genetically and culturally, as humans have adjusted to different situations and demands in their environments. However, in the current world, human learning is largely facilitated by artificial intelligence (AI) tools, which are programs that can perform tasks that usually require human intelligence, such as comprehension, reasoning, problem-solving, and communication. AI tools can support humans in their learning endeavors, by giving them access to enormous amounts of information, and by delivering them customized and interactive assistance and feedback. AI tools can also amplify human creativity and innovation, by generating novel and diverse content, such as code, poems, essays, songs, and more. But what are the effects of this dependence on AI tools for human learning and evolution? Does it boost or diminish human curiosity and creativity? Does it enable or limit human autonomy and agency? Does it foster or hamper human diversity and collaboration? These are some of the questions that this topic will explore, by evaluating the pros and cons of using AI tools for human learning, and the ethical and social issues that arise from this phenomenon. [28] Today when we look around us we observe the advancement in technology has brought a lot of comfort to our lives in terms of traveling, education, or enjoying content virtually. [29] Talking about our basic requirements, technology has become so friendly that we can learn everything through E-Learning. Everyone only wondered about having an AI which will help in making our lives easy. The latest concept in terms of AI which is widely received and accepted by the people everywhere around the Globe is the Open AI that is Chat Gpt, Gemini, Copilot. All of these AI helps us in decision making or cutting our chase short for finding solutions for either lengthy solutions like writing a summary related to something or Questions which are easy to solve but difficult to look for solutions. About a quarter (27%) of Americans say they interact with artificial intelligence almost constantly or several times a day. Artificial intelligence (AI) is used in a variety of ways, including online product recommendations, facial recognition software and chatbots. One in six (17%) adults reported that they can often or always recognise when they are using AI, one in two (50%) adults reported that they can some of the time or occasionally recognise when they are using AI, one in three (33%) adults reported that they can hardly ever or never recognise when they are using AI. [26] In this project we are testing the dependence upon the recently emerged Open AI tools such as ChatGPT, Google Bard, Bing. Our motive is to find out whether people are using these powerful tools to help in their academics or other tasks only or do they take advice from these tools in their financial planning as well.

BackgroundTo analyze medical students’ perceptions, trust, and attitudes toward artificial intelligence (AI) in medical education, and explore their willingness to integrate AI in learning and teaching practices.MethodsThis cross-sectional study was performed with undergraduate and postgraduate medical students from two medical universities in Beijing. Data were collected between October and early November 2024 via a self-designed questionnaire that covered seven main domains: Awareness of AI, Expectations and concerns about AI, Importance of AI in education, Potential challenges and risks of AI in education and learning, The role and potential of AI in education, Perceptions of generative AI, and Behavioral intentions and plans for AI use in medical education.ResultsA total of 586 students participated in the survey, 553 valid responses were collected, giving an effective response rate of 94.4%. The majority of participants reported familiarity with AI concepts, whereas only 43.5% had an understanding of AI applications specific to medical education. Postgraduate students exhibited significantly higher levels of awareness of AI tools in medical contexts compared with undergraduate students (p < 0.001). Gender differences were also observed, with male students showing more enthusiasm and higher engagement with AI technologies than female students (p < 0.001). Female students expressed greater concerns regarding privacy, data security, and potential ethical issues related to AI in medical education than male students (p < 0.05). Male students or postgraduate students showed stronger behavioral intentions to integrate AI tools in their future learning and teaching practices.ConclusionsMedical students exhibit optimistic yet cautious attitudes toward the application of AI in medical education. They acknowledge the potential of AI to enhance educational efficiency, but remain mindful of the associated privacy and ethical risks. Strengthening AI education and training and balancing technological advancements with ethical considerations will be crucial in facilitating the deep integration of AI in medical education.Trial registrationNot clinical trial.

This study investigates the integration of artificial intelligence (AI) tools in Chinese primary mathematics education, focusing on the factors shaping their adoption and application. Guided by the Technological Pedagogical Readiness (TPR) framework and employing an explanatory sequential mixed-methods design, the research quantitatively examines relationships among teachers' Technological Pedagogical Content Knowledge (TPACK), practices, attitudes, and AI utilization in a sample of 1205 primary mathematics teachers across three cities in Southwest China. Qualitative interviews with six teachers provide in-depth, contextual explanations for the quantitative patterns observed, illuminating how and why AI tools, such as generative AI and adaptive learning systems, are incorporated into classroom teaching. The results demonstrate that teachers' TPACK and attitudes toward AI are decisive for successful AI integration, while external factors, including educational challenges, parental and community involvement, and students' technology literacy, have limited direct influence in this well-supported context. Four main approaches to AI integration were identified: contextual visualization to clarify abstract concepts, personalized instruction, interactive and collaborative learning, and data-driven instructional optimization. These mixed-methods findings not only expand understanding of AI's pedagogical applications but also underscore the importance of teacher psychological readiness (e.g., attitudes, confidence) alongside professional knowledge. The study advocates for targeted professional development and strategic institutional support as critical to maximizing the educational potential of AI, and highlights the conditional nature of contextual factors for future research and policy considerations. Further discussion of these findings and their implications is provided in the main text.

In contemporary society, Artificial Intelligence (AI) pervades numerous facets of our lives and is likely to impact many sectors and professions, including education. Tertiary-level students in particular face challenges regarding the use of AI for studies and assessment, including limited understanding of AI tools, as well as a lack of deep critical engagement with AI for learning (Shibani et al., 2024). To respond to emerging developments in generative AI, the recent Australian Tertiary Education Quality and Standards Agency (TEQSA) report suggests tertiary-level learning and assessments be designed to foster responsible and ethical use of AI (Lodge et al., 2023). This involves the development of AI literacy among students to engage with AI in critical, ethical ways that aid their learning and not hinder it. Our project aims to narrow the AI literacy gap among students from diverse study backgrounds by providing foundational knowledge and developing critical skills for practical use of AI tools for learning and professional practice, in collaboration with students and academics as part of a Students as Partners (SAP) initiative. Staff bring expertise in AI critical engagement and students bring practical, first-hand experiences of learning in this collaboration, supported by the university’s SAP program. Building on the current UNESCO recommendations for the use of generative AI in education (UNESCO, 2023) and prior theoretical frameworks on AI literacy (Chiu et al., 2024; Ng, et al., 2021; Southworth et al., 2023) we target key skills that higher education students should develop to meaningfully engage with AI. By creating accessible and engaging resources, such as instructional videos and comprehensive guides on generative AI applications like ChatGPT and ways to prompt for enhancing learning, we introduce existing AI tools and teach students to use them effectively, promoting a hands-on learning environment. Using learning design principles, the developed curriculum will be presented in an AI Literacy module on a Canvas site, with supporting instruction workshopped with student participants for evaluation. Student cohorts recruited from diverse disciplines will pilot and assess the effectiveness of the program, and qualitative methods such as focus groups and interviews will be used for evaluation of our intervention and continuous improvements. Findings will inform tertiary students’ current level of AI literacy and the effectiveness of interventions to improve key skills beyond their disciplinary knowledge, better preparing them for life beyond university. Indeed, the implementation of similar AI literacy courses has demonstrated statistically significant improvements in AI literacy and understanding of AI concepts amongst university students (Kong, Cheung, &amp; Zhang, 2021). Our approach underscores the importance of relational engagement in higher education with students as partners (Matthews, 2018) and participatory design with students in a topic that is significant in the current age of AI (Laupichler et al., 2022). The course's flexibility to be accessed directly or embedded into other curricula ensures scalability and broader impact, solidifying the validity of our multifaceted approach. Through utilising relevant research methodologies and learning design principles, we endeavour to create an AI literacy course that is robust, accessible, educational, and engaging to use by tertiary-level students from diverse study backgrounds.

Artificial Intelligence (AI) is increasingly adopted by educational institutions, particularly as a generative AI (GenAI) tool for e‐learning. This study explores the effectiveness of using GenAI with engineering students at a leading university in Saudi Arabia and the Middle East. It aims to assess GenAI's impact in the College of Engineering and examine gender‐based differences in how students utilize AI as a learning tool. The study also investigates how students from different engineering majors utilize AI in their learning. To achieve this objective, an online survey with 15 questions was distributed to 403 engineering students to analyze their perceptions of AI adoption in education. The study employs two non‐parametric rank‐based statistical tests: the Mann–Whitney test to analyze gender differences, and the Kruskal–Wallis test to examine how various engineering disciplines such as industrial, electrical, mechanical, civil, chemical, nuclear, and mining engineering influence GenAI adoption. The findings reveal significant differences between male and female students in their experiences with GenAI, particularly regarding inaccurate or misleading responses, accurate and reliable responses, and their opinions regarding the users from applied academic field toward GenAI adoption. The results also indicate notable differences among engineering majors in their proficiency with GenAI features, their experiences with hallucinated responses, their views on using GenAI in theoretical disciplines, and their trust in the accuracy of information provided by ChatGPT. These findings support educational decision‐makers in integrating AI as a learning technology for engineering students and in understanding student engagement with AI tools in education.

Performance of an artificial intelligence tool with real-time clinical workflow integration - Detection of intracranial hemorrhage and pulmonary embolism.

Artificial intelligence (AI) is strengthening rapidly, and the release of ChatGPT and similar generative AI (GenAI) tools has brought about a substantial change in the conduct of education. The domain of English Language Teaching and Learning (ELT/L), amenable to technological integration, has also experienced a profound transformation in recent years when language education stakeholders began embracing the use of AI-powered applications in various facets of language pedagogy. Considering the recent advancements in artificial intelligence, this systematic literature review endeavors to explore the integration of generative AI technologies, such as AI Chatbots, AI-powered Speech Recognition Technology (AI-SRT), Machine Translation tools, Automated Evaluation Systems (AWE), and other AI applications in language education. The primary focus is to understand how these technological innovations facilitate language acquisition and enhance language skills, such as listening, speaking, reading, and writing in English as a Second Language (ESL) and English as a Foreign Language (EFL) contexts. Through scrupulously examining the existing literature, the study seeks to identify the most frequently used GenAI tools in language education and their pedagogical implications. Further, it seeks to determine the crucial language skills predominantly enhanced using AI, assess the efficacy of the tools used in their enhancement, and evaluate the impact of incorporating AI in language teaching and learning. The study comprehensively examines 55 research articles sourced from Scopus, Web of Science (WoS), Taylor &amp; Francis, and Springer, published between December 2022 and May 2025, that highlight the significant advancements of AI as applied to language teaching and learning. The findings reveal that advanced generative AI tools can substantially transform and augment learners' language skills by offering a personalized, versatile, and dynamic learning environment. In addition, the study elucidates the limitations and challenges inherent in incorporating generative AI into language education and advocates for undertaking effective measures to optimize its inclusion. Furthermore, the study concludes by discussing avenues for future empirical studies.