Structural model of pre-service teacher training based on artificial intelligence technologies

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

The modern stage of technological advancement is characterized by the dynamic development of artificial intelligence (AI) technologies and their integration into education. Of the several dozen artificial intelligence technologies used in various spheres of human activity, the most widely used in education are: a) machine learning, b) natural language processing, c) data science and d) intelligent tutoring system. On their basis, artificial intelligence tools are created, which have significant language teaching potential and in many ways change the traditional roles of the teacher and learners in the educational process. However, it should be noted that the integration of artificial intelligence technologies into education in general and foreign language teaching in particular is currently at the initial stage. Educators and learning designers conduct pilot studies investigating the abilities of specific artificial intelligence tools in the formation of foreign language aspects or the development of learners' foreign language communication skills. At the same time, the limited number of empirical research studies does not allow us to talk about the systematicity and comprehensiveness of foreign language teaching based on artificial intelligence technologies. One of the key differences between artificial intelligence technologies and modern information and communication technologies is their AI’s ability to provide a much wider range of feedback. It is owing to this advantage of artificial intelligence that innovative methods of teaching a foreign language will be based, creating new additional conditions for students to master a foreign language and raising the learning process to a new level in terms of the quality of solving learning tasks. However, the consideration of the types of feedback provided by AI tools has not been the subject of separate research, which determined the importance of this study. The aim of the study is to identify the types of feedback provided to learners by artificial intelligence technologies for the subsequent development of teaching methods (teaching technologies and/or typologies of tasks and assignments) based on them. The definition of the types of feedback provided to users by artificial intelligence tools was based on a review and analysis of research in the field of pedagogy and foreign language teaching methods. The sample of sources included research articles and reviews published in academic journals indexed in Scopus and Web of Science (Q1 and Q2), as well as Russian academic journals, included in the list of the Higher Attestation Commission of the Russian Federation (Categories 1 and 2) (pedagogical sciences). The following aspects of teaching methods were the subject of study in the review and analysis of academic papers: a) the artificial intelligence tool used for receiving feedback; b) the target audience of learners; c) the purpose of interaction with artificial intelligence; d) the form of activities; e) the type of feedback used. As a result, the following six types of feedback provided by artificial intelligence tools were identified in this study: a) educational and social; b) information and reference; c) methodological; d) analytical; e) evaluative; f) conditionally creative feedback.

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

This article examines in detail the growing role of artificial intelligence (AI) in teacher education in Kazakhstan. Through extensive research and interviews with local teachers and technology experts, we are exploring the implications and possibilities of using artificial intelligence tools. Our analysis shows how these artificial intelligence-based systems are changing pedagogical practice, contributing to the personalization of learning and improving the dynamics of relationships between teachers and students. The article also highlights the current problems faced by the Kazakh education sector in the implementation of artificial intelligence technologies, such as data privacy, ethical dilemmas and the critical need for digital literacy of teachers.The article also emphasizes that the integration of artificial intelligence requires a paradigm shift in teacher training models: from traditional pedagogical methods to technologically advanced ones. We consider the situation in Kazakhstan, documenting its unique path to digital transformation of education and exploring how artificial intelligence can solve the specific educational challenges of this country and the diverse contribution to the development of artificial intelligence, we must provide students and teachers with the opportunity to learn about artificial intelligence through training programs and advanced training focused on artificial intelligence and ethics. Revealing the multifaceted potential and subtleties of the use of artificial intelligence in teacher education in Kazakhstan, this article helps stakeholders to make informed decisions on the integration of artificial intelligence into teacher training programs. At the same time, the need to align educational aspirations with digital achievements is emphasized, which is a fundamental step towards a technologically integrated educational future of Kazakhstan.

Artificial intelligence (AI) technologies have spread throughout the world and changed the way that many social functions are conducted, including health care. Future large-scale combat missions will likely require health care professionals to utilize AI tools among other tools in providing care for the Warfighter. Despite the need for an AI-capable health care force, medical education lacks an integration of medical AI knowledge. The purpose of this manuscript was to review ways that military health care education can be improved with an understanding of and using AI technologies. This article is a review of the literature regarding the integration of AI technologies in medicine and medical education. We do provide examples of quotes and images from a larger USU study on a Faculty Development program centered on learning about AI technologies in health care education. The study is not complete and is not the focus of this article, but was approved by the USU IRB. Effective integration of AI technologies in military health care education requires military health care educators that are willing to learn how to safely, effectively, and ethically use AI technologies in their own administrative, educational, research, and clinical roles. Together with health care trainees, these faculties can help to build and co-create AI-integrated curricula that will accelerate and enhance the military health care curriculum of tomorrow. Trainees can begin to use generative AI tools, like large language models, to begin to develop their skills and practice the art of generating high-quality AI tools that will improve their studies and prepare them to improve military health care. Integration of AI technologies in the military health care environment requires close military-industry collaborations with AI and security experts to ensure personal and health care information security. Through secure cloud computing, blockchain technologies, and Application Programming Interfaces, among other technologies, military health care facilities and systems can safely integrate AI technologies to enhance patient care, clinical research, and health care education. AI technologies are not a dream of the future, they are here, and they are being integrated and implemented in military health care systems. To best prepare the military health care professionals of the future for the reality of medical AI, we must reform military health care education through a combined effort of faculty, students, and industry partners.

Importance. The modern stage of information and technological development of civilization is characterised by the dynamic emergence of artificial intelligence (AI) technologies and the development of tools based on them, which are being more and more introduced into various spheres of life. The education system in general, and foreign language education in particular, is no exception. Currently, there are several dozen AI tools that are actively used by students and teachers in the development of foreign language communicative skills and the development of language skills. There is a rather voluminous body of research in the academic literature devoted to the disclosure of the language teaching potential of modern AI tools. However, most of the studies are of a pilot nature. The focus of scholars’ attention is on particular methods for the development of students’ communicative skills or the development of certain language skills based on individual AI tools. The systematic consideration of the integration of AI technologies into the process of teaching foreign language majoring students – future foreign language teachers – linguistic and teaching methods training has not been the subject of a special study. The purpose of this study is to develop a matrix of AI tools used in the linguistic and teaching methods training of future foreign language teachers.Materials and Methods. The study is conducted on the basis of the expert assessment method, which allows to: a) identify the language teaching potential, as well as the limitations of the most common AI tools; b) summarise and classify the available knowledge in the form of a matrix of AI tools used in the linguistic and teaching methods training of future foreign language teachers. The materials of the study were research articles, published in Russian and foreign academic journals, indexed in Web of Science and Scopus.Results and Discussion. A matrix of AI tools in the linguistic and teaching methods training of future foreign language teachers has been developed. The matrix is presented according to six types of feedback from generative AI used in foreign language teaching and teaching methods. The following are the main and most accessible AI tools for teachers and students providing feedback of each type: a) Replika, LingvoBot, Multitran_bot, Slavaribot, WorldContextBot, ChatGPT, Google Assistant, EGEEnglish.ru (educational and social feedback); b) ChatGPT, YandexGPT and GigaChat (information and reference feedback); c) ChatGPT 4.0, YandexGPT, GigaChat, Twee (methodological feedback); d) ChatGPT, YandexGPT, GigaChat, Turnitin, software “Antiplagiat” (analytical feedback); e) Grammarly, PaperRater, Pigai, ChatGPT 4.0, YandexGPT, GigaChat, Criterion (assessment and evaluative feedback); f) ChatGPT, YandexGPT, GigaChat, AI Poem Generator, Midjourney, Suno, Sora, Runway (conditionally creative feedback).Conclusion. The novelty of the research consists in the development of a matrix of AI tools in the linguistic and teaching methods training of future foreign language teachers. The prospects for further research lie in the development of teaching methods for aspects of language, types of speech activity, as well as specialised disciplines based on specific AI tools. In their entirety, these particular methods will enable the creation of an integrated system of linguistic and teaching methods training of future foreign language teachers based on AI tools.

Every day Artificial Intelligence (AI) technologies are more deeply inte-grated into education in general and into the process of a foreign language teaching in particular. Methodologists are developing language-specific methods of teaching lin-guistic university students foreign language aspects, types of speech activities, culture and translation based on specific AI tools. Most of these studies have a narrow focus and are aimed at solving individual and sometimes discrete pedagogical tasks. Such integration of AI into the educational process allows us to create additional conditions for extracurricular language practice for students and contributes to the further devel-opment of their foreign language communicative competence in all the diversity of its components. At the same time, the majority of methodological studies have a narrow focus and are oriented towards solving individual and sometimes discrete educational problems related to the development of specific speech skills or the formation of lexical, grammatical or phonetic speech skills of students. At the present stage, the language teaching potential of AI technologies allows to systematically consider the issue of in-tegrating AI into the process of language and methodological pre-service teachers’ training, which has not yet been the subject of a separate study. The aim of the paper is to develop a structural model for the integration of AI technologies in language and methodological pre-service teachers’ training. The model proposed by the authors for the integration of AI technologies in lan-guage and methodological pre-service teachers’ training consists of five blocks: lin-guistic disciplines, narrow professional disciplines, methodological disciplines, peda-gogical practice and research work. Within each block there are sub-blocks aimed at solving specific pedagogical/research tasks. The authors show which AI tools and tasks can be used in the context of teaching the disciplines from each block. The methodo-logical content of the blocks is based on the typology of feedback from generative AI as applied to teaching a foreign language, including educational-social, information-reference, methodological, analytical, evaluative and conditionally creative types. The methodological content of the blocks is based on the typology of feedback from generative AI in relation to foreign language teaching. The research is based on systematic and expert approaches using theoretical (comparative, component and complex analysis of AI tools, classification, synthesis, generalisation, modelling) and empirical (observa-tions and interviews with teachers and students) research methods. The research materials included scientific articles (Article and Review) published in academic journals indexed in the Scopus and Web of Science databases (Q1 and Q2), as well as Russian academic journals from the list of the Higher Attestation Commission of the Russian Federation on pedagogical sciences (С1 and С2). The subject of the research in the works used was the development of practical methods for teaching a foreign language (types of speech activity and aspects of language) using specific artificial intelligence tools.

This systematic review examines the role of artificial intelligence (AI) in English language teaching (ELT), analyzing AI tools, applications, and their pedagogical outcomes. AI technologies, such as chatbots, intelligent tutoring systems, and speech recognition software, are increasingly used to enhance language learning experiences. The review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model, a standardized approach that ensures transparency and rigor in identifying, screening, and analyzing relevant literature. PRISMA emphasizes clear documentation of the selection process, including inclusion and exclusion criteria, to provide a systematic and replicable methodology for comprehensive reviews. Through thematic qualitative analysis of recent literature indexed in Scopus and Web of Science, key themes emerged regarding AI types, applications, teacher and learner perspectives, and ethical considerations. Findings reveal that AI tools enhance learner engagement, provide personalized learning experiences, and improve language proficiency, particularly in speaking and writing. However, challenges remain, such as accessibility barriers, teacher preparedness, and ethical concerns around data privacy and bias. This review proposes a framework for AI integration in ELT, focusing on access, teacher training, ethical standards, and blended learning models to optimize AI’s benefits. The study underscores the need for targeted teacher training and ethical standards to maximize AI’s effectiveness and sustainability in ELT. This framework and the review findings aim to support educators, developers, and policymakers in fostering an AI-enriched learning environment that aligns with educational goals while addressing existing limitations.

Artificial intelligence (AI) is rapidly transforming society and industries, presenting both opportunities and ethical challenges. AI enables machines to perform tasks traditionally done by humans, such as natural language processing, pattern recognition, decision-making, and problem-solving (Brookings, 2023). In education, AI enhances teaching methodologies, student assessment, and administrative tasks through tools like intelligent tutoring systems, adaptive learning platforms, and educational chatbots. These tools offer customised learning experiences, immediate feedback, and data-driven insights. This research aims to investigate how AI can be leveraged within education to promote social good by identifying how familiar educators and students are with AI tools, identify how educators and students perceive the role of AI in education and what are the current applications of AI technologies in educational settings and how widely are they used. Finally, discuss the opportunities and ethical considerations of integrating AI in education. AI technologies can address critical social challenges such as inequality, accessibility, and personalised learning. According to Luckin et al. (2016), "AI can provide tailored educational experiences that adapt to individual learning needs, thus promoting equity in education." This exploratory research begins with an overview of AI's role and tools in education, followed by a discussion of the challenges, opportunities, and ethical considerations associated with AI integration. Understandings are drawn from educator’s response to a questionnaire and a focus group with first year and final-year third level students. This qualitative data, analysed using NVivo software, reveals key themes and significant findings on effectively utilising AI in education.

Background/Purpose: The agriculture sector is the backbone of every nation which contributes to the global economy. The implementation of technology in agriculture has brought revolutionary development in its outcome. Due to this, a drastic improvement in the global economy from the agricultural sector is expected. Moreover, the implementation of artificial intelligence (AI) improves the productivity of farmers giving solutions to various challenges faced by the farmers. The various AI tools that are developed for the agriculture sector include precision farming, predictive analytics, automated machinery, smart irrigation systems, crop and soil monitoring, supply chain optimization, weather forecasting, and livestock management. Adopting AI in agriculture faces several challenges despite its long-term benefits. The high upfront costs to be invested in implementing AI technology make it difficult for small-scale and developing farmers to invest in AI. Implementing the above technology needs technical skills, fast internet connectivity, and costlier equipment. Due to the lack of the above-mentioned requirements, the AI technologies that are meant for agriculture do not reach the farmers. This results in the wastage of resources for AI without the outcome. Considering the above issues an appropriate simplified model is proposed that facilitates the adaptation of the AI technology by small and medium-scale farmers in their agriculture to improve the performance. Objective: The objective of this paper is to review the various journals related to the implementation of AI in Agriculture and to study the various issues related to its implementation. It also aims at identifying the research gap which will help to develop a model suitable for the end like small-scale and medium-scale farmers. Design/Methodology/Approach: A systematic literature review was conducted by gathering and examining relevant literature from international and national journals, conferences, databases, and other resources accessed via Google Scholar and various search engines. Findings/Result: The agriculture sector, crucial to every nation's economy, has seen revolutionary advancements through technology, especially AI. AI tools like precision farming, predictive analytics, and smart irrigation promise to enhance productivity and address various agricultural challenges. However, high implementation costs, resistance to new technologies, and lack of necessary infrastructure hinder widespread adoption among small-scale and developing farmers. To overcome these obstacles, a model is proposed to effectively support farmers in adopting AI technologies to boost agricultural performance. Originality/Value: The implementation of AI and ML tools in agriculture from diverse sources is done. This area needs study due to recent challenges faced by small and medium-scale farmers in the implementation of AI and ML tools in agriculture. The information acquired will help to create a new model by improving the outcomes of the existing scenario. Paper Type: Literature Review.

Focus issue: Artificial intelligence in medical physics.

BackgroundNew artificial intelligence (AI) tools are being developed at a high speed. However, strategies and practical experiences surrounding the adoption and implementation of AI in health care are lacking. This is likely because of the high implementation complexity of AI, legacy IT infrastructure, and unclear business cases, thus complicating AI adoption. Research has recently started to identify the factors influencing AI readiness of organizations.ObjectiveThis study aimed to investigate the factors influencing AI readiness as well as possible barriers to AI adoption and implementation in German hospitals. We also assessed the status quo regarding the dissemination of AI tools in hospitals. We focused on IT decision makers, a seldom studied but highly relevant group.MethodsWe created a web-based survey based on recent AI readiness and implementation literature. Participants were identified through a publicly accessible database and contacted via email or invitational leaflets sent by mail, in some cases accompanied by a telephonic prenotification. The survey responses were analyzed using descriptive statistics.ResultsWe contacted 609 possible participants, and our database recorded 40 completed surveys. Most participants agreed or rather agreed with the statement that AI would be relevant in the future, both in Germany (37/40, 93%) and in their own hospital (36/40, 90%). Participants were asked whether their hospitals used or planned to use AI technologies. Of the 40 participants, 26 (65%) answered “yes.” Most AI technologies were used or planned for patient care, followed by biomedical research, administration, and logistics and central purchasing. The most important barriers to AI were lack of resources (staff, knowledge, and financial). Relevant possible opportunities for using AI were increase in efficiency owing to time-saving effects, competitive advantages, and increase in quality of care. Most AI tools in use or in planning have been developed with external partners.ConclusionsFew tools have been implemented in routine care, and many hospitals do not use or plan to use AI in the future. This can likely be explained by missing or unclear business cases or the need for a modern IT infrastructure to integrate AI tools in a usable manner. These shortcomings complicate decision-making and resource attribution. As most AI technologies already in use were developed in cooperation with external partners, these relationships should be fostered. IT decision makers should assess their hospitals’ readiness for AI individually with a focus on resources. Further research should continue to monitor the dissemination of AI tools and readiness factors to determine whether improvements can be made over time. This monitoring is especially important with regard to government-supported investments in AI technologies that could alleviate financial burdens. Qualitative studies with hospital IT decision makers should be conducted to further explore the reasons for slow AI.

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.

The acceptance of technology at the higher educational level has been a significant discussion, with little attention on the gender dynamics on the acceptance of artificial intelligence (AI ) tools by senior lecturers. This study delved into a detailed analysis of the gender dynamics in the discussion of technology acceptance mainly AI tools, in foreign language (FL) education. Quantitative study approach was adopted in the process, and survey design was implemented. Data was collected using structured digital questionnaire, based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model. A total of ninety-five (95) male senior lecturers and one hundred and three (103) female senior lecturers participated in the study. Analysis was conducted using relevant statistical measures. The results showed disparities in the attitudes and views of senior lecturers towards artificial intelligence (AI) technologies in the context of FL education, greatly influenced by gender. In relation to usage, male senior lecturers have higher positive reactions (61.06%) in comparison to their female counterparts (46.6%). However, in relation to the assumption that AI technologies improve the performance of learners, 69% of male senior lecturers agree with this notion, but a substantially greater percentage of 72.81% of female senior lecturers hold the same perspective. Moreover, there exists a little disparity in the level of proficiency in using AI technologies across genders. Specifically, 56.84% of male senior lecturers see it as uncomplicated, while 61.16% of their female counterparts share the same sentiment. The gender discrepancy that is most notable pertains to the perceived level of ease in using artificial intelligence (AI) technologies during foreign language (FL) lessons. The data reveals that a majority of male senior lecturers, calculated as 69.48%, see the use of these tools very easy. In contrast, a much higher proportion of female senior lecturers, 86.41%, share the same perception. This discrepancy highlights a notable disparity in confidence levels between the two genders. These results together emphasise the changing gender dynamics in the acceptance of technology, interrogating conventional assumptions and underscoring the need for customised support systems to guarantee fair and efficient integration of artificial intelligence (AI) technologies in foreign language instruction among senior lecturers.

The impacts of Artificial Intelligence (AI) tools on various aspects of second language (L2) education have been widely reported in the literature. However, the socio-emotional dangers of using AI technologies from the perspective of English as a foreign language (EFL) teachers have remained uncharted. To address the gap, this study adopted a qualitative design and drew on control value theory (CVT) and social constructivism to unveil the socio-emotional risks of AI-mediated L2 education. A sample of 33 Chinese EFL teachers participated in online semi-structured interviews. The results of thematic analysis showed six dangers in using AI. Specifically, 'social isolation and competition', 'bias and academic dishonesty', and 'reduced teacher-student interaction and rapport' were the common social dangers of using AI by L2 educators. Concerning emotional dangers, it was found that AI tools may lead to 'classroom anxiety and stress' and 'feeling of passiveness and lack of autonomy', and 'creativity and criticality reduction'. The findings are discussed, and theoretical and practical implications are listed for EFL teachers, students, and trainers, as well as AI tools' developers, to inform them of the socio-emotional consequences of using AI.