Practice Research on Generative Artificial Intelligence Assisted Junior High School English Reading Teaching

English reading, as one of the five major skills including listening, speaking, reading, viewing, and writing, has always been emphasized by English teachers. With the deepening of the new curriculum reform, high school English reading teaching faces new requirements and goals. However, there are still some issues in the classroom practice of English reading teaching. This paper aims to delve into the implications of high school English reading teaching under the new curriculum standards, aiming to shift students from mere test-takers to genuine readers. Firstly, the paper reviews the requirements of the new curriculum standards for high school English teaching, emphasizing the focus on core literacy, adhering to a people-oriented educational philosophy, and giving special attention to the importance of reading. Secondly, by analyzing the perspectives of both teachers and students, the current situation of high school English teaching is comprehensively examined, revealing the challenges faced by educators and students in adapting to the changes brought by the new curriculum standards. By comprehensively applying these insights, we hope to promote the reform of high school English reading teaching, cultivating students with more profound thinking, innovative capabilities, and interdisciplinary literacy. Teaching students how to read is an integral part of this process.

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

Critical thinking is an important part of thinking ability. Teachers’ questions in English reading classrooms affect the quality of reading teaching and play a key role in cultivating and developing students’ critical thinking. There is a certain positive impact on students' critical thinking. However, in the current high school English reading teaching in China, most teachers lack attention to the cognitive level of questioning, and the number of questions with high cognitive level is relatively small. Teachers lack the awareness of using classroom questions to stimulate students' thinking, which is not conducive to cultivating and developing students' critical thinking. (1) How does teacher questioning affect students' critical thinking tendency in high school English reading teaching? (2) How does teacher questioning affect students' critical thinking skills in high school English reading teaching? (3) What is the impact of teachers' questions on students' English reading performance in high school English reading teaching? In response to these problems, this study classifies and designs teacher questions based on Bloom's cognitive target classification, and applies teacher questions to English reading teaching based on constructivism theory and zone of proximal development theory. Using empirical research methods to explore the impact of teacher questioning on students' critical thinking and the effectiveness of cultivating students' critical thinking in high school English reading teaching, with a view to the design of questions in front-line English teachers' reading teaching and the cultivation of students' critical thinking for reference.

With the advancement of the new curriculum reform of senior high school English, English classroom teaching has adopted many new concepts, which have a significant impact on improving the effectiveness of English teaching. However, there is still the problem of students’ shallow learning in English reading teaching, which makes it difficult for students to explore the deeper connotations and meanings of the text and the nurturing value of English learning is often neglected. Deep learning provides new ideas and methods for carrying out high school English reading teaching, which is of great theoretical and practical significance for improving students’ shallow and fragmented learning and changing teachers’ shallow teaching methods. Based on the concept of deep learning, this paper discusses the ideas of senior high school English reading teaching pointing to deep learning by combining the reading discourses in the high school English teaching materials of the Beijing Normal University Edition, aiming to provide practical assistance for promoting the core competencies in English teaching.

Reading is one of the important links in high school English teaching. It is the fundamental way for students to accept texts, learn knowledge and exchange ideas. The key competency proposed in the revised English Curriculum Standards for Senior High schools is to develop students' core quality of English subject, and advocate teachers practice English learning activity view in order to promote the development students' core quality. Curriculum reform has put forward new requirements for high school English reading teaching, that is, teachers should explore ways to develop students' core qualities in teaching and lay a good foundation for students' future development. This paper systematically elaborates the connotation of core English literacy, analyzes the current situation of senior high school English reading teaching, and focuses on exploring the new English reading teaching strategies based on core literacy from three aspects: what to teach, how to teach, and evaluation.

This paper presents a literature on the impact of Artificial Intelligence (AI) in English language teaching and library practice and its impact on library operations. This study aims to provide researchers with a comprehensive understanding of AI in the English language teaching and library context. This paper aims at analyzing an artificial intelligence platform that can be used in examining lecturers’ proficiency levels in English teaching and library practice in Nigerian universities. It draws attention to how AI facilitates personalized learning experiences by adapting to individual teachers’ teaching style, pace, and proficiency levels. The paper examines the proficiency levels of lecturers in Nigerian universities regarding the utilization of AI features in three innovative presentation software tools: MS PowerPoint, Canva, and Gamma. Through the application of AI playing as a pivotal tool in enhancing research, teaching and learning experiences, the study explores the significance of PowerPoint literacy in modern English teaching and learning settings. The method of research design is descriptive. The population of the study consists of the entire academic staff in Nigerian universities. The sample size used is made up of 600 lecturers. Data are analyzed using descriptive statistics of mean, standard deviation on a 4-point Likert Scale. The findings of this study serve as a base for developing training programs and interventions to enhance PowerPoint literacy skills in the teaching of English Language and library practice in Nigerian universities. The findings also illustrate that Artificial Intelligence technology is useful for developing teaching of English Language and library practice. The major limitation of this study is that it does not examine the extent of AI role in teachers’ teaching and students’ English learning. The information presented in this paper serves as a valuable resource for researchers interested in exploring the use of AI in English language teaching and library practice.

The recent development of artificial intelligence (AI) technology is bringing about changes at a faster pace and on a larger scale than any other period in human history. With technological advancements overcoming the limitations of medical AI through training with databases, AI technology has made remarkable progress since the inception of deep learning for image processing with convolutional neural networks (CNN) in 2012. The recent advancements in natural language processing (NLP) have accelerated the utilization of AI through sophisticated natural language processing, enabling machines to identify and understand data regardless of the complexity of the language. This has laid the foundation for the rapid and precise development of generative AI. In the era where generative AI is being utilized without pausing in its developmental speed, we considered the civil liability of AI in our civil law principles, taking into account the inherent characteristics of AI such as unpredictability, opacity, and the black box effect. To do this, we first examined the legal liability considering the stages of AI technology development in discussing the tort liability caused by AI. Even “Weak AI,” created by AI developers, may fall under “Gefahr,” and while not all types, some may apply to strict liability in terms of risk liability. Furthermore, while reviewing civil liability applicable to AI under fault-based and no-fault liability, we also looked at the trends in the EU comparatively. In discussing no-fault liability, particularly under the Product Liability Act, we examined the possibility and implications of applying risk liability to pharmaceutical manufacturing using generative AI technology as a representative example to overcome the limitations of the existing Product Liability Act. Humanity currently lives in an era of rapid technological development and exploding big data, enjoying numerous benefits due to these advancements. As user convenience improves and massive added value is created through technological progress, the meaning of risk liability in the realm of civil liability can gain more significance. Generative AI has already drastically reduced the costs and time required for new drug development, providing substantial profits to pharmaceutical companies. However, even if the existing Product Liability Act is applied, it may be difficult to adequately remedy the harm to victims due to the reasonable alternative possibility defense regarding design defects. In the era of generative AI, we examined the possibility of applying enhanced risk liability by assuming the case of pharmaceutical manufacturing.

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.

Can an Artificial Intelligence (AI) Be an Author on a Medical Paper?

Advances in artificial intelligence (AI) technologies, together with the availability of big data in society, creates uncertainties about how these developments will affect healthcare systems worldwide. Compassion is essential for high-quality healthcare and research shows how prosocial caring behaviors benefit human health and societies. However, the possible association between AI technologies and compassion is under conceptualized and underexplored. The aim of this scoping review is to provide a comprehensive depth and a balanced perspective of the emerging topic of AI technologies and compassion, to inform future research and practice. The review questions were: How is compassion discussed in relation to AI technologies in healthcare? How are AI technologies being used to enhance compassion in healthcare? What are the gaps in current knowledge and unexplored potential? What are the key areas where AI technologies could support compassion in healthcare? A systematic scoping review following five steps of Joanna Briggs Institute methodology. Presentation of the scoping review conforms with PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews). Eligibility criteria were defined according to 3 concept constructs (AI technologies, compassion, healthcare) developed from the literature and informed by medical subject headings (MeSH) and key words for the electronic searches. Sources of evidence were Web of Science and PubMed databases, articles published in English language 2011-2022. Articles were screened by title/abstract using inclusion/exclusion criteria. Data extracted (author, date of publication, type of article, aim/context of healthcare, key relevant findings, country) was charted using data tables. Thematic analysis used an inductive-deductive approach to generate code categories from the review questions and the data. A multidisciplinary team assessed themes for resonance and relevance to research and practice. Searches identified 3,124 articles. A total of 197 were included after screening. The number of articles has increased over 10 years (2011, n = 1 to 2021, n = 47 and from Jan-Aug 2022 n = 35 articles). Overarching themes related to the review questions were: (1) Developments and debates (7 themes) Concerns about AI ethics, healthcare jobs, and loss of empathy; Human-centered design of AI technologies for healthcare; Optimistic speculation AI technologies will address care gaps; Interrogation of what it means to be human and to care; Recognition of future potential for patient monitoring, virtual proximity, and access to healthcare; Calls for curricula development and healthcare professional education; Implementation of AI applications to enhance health and wellbeing of the healthcare workforce. (2) How AI technologies enhance compassion (10 themes) Empathetic awareness; Empathetic response and relational behavior; Communication skills; Health coaching; Therapeutic interventions; Moral development learning; Clinical knowledge and clinical assessment; Healthcare quality assessment; Therapeutic bond and therapeutic alliance; Providing health information and advice. (3) Gaps in knowledge (4 themes) Educational effectiveness of AI-assisted learning; Patient diversity and AI technologies; Implementation of AI technologies in education and practice settings; Safety and clinical effectiveness of AI technologies. (4) Key areas for development (3 themes) Enriching education, learning and clinical practice; Extending healing spaces; Enhancing healing relationships. There is an association between AI technologies and compassion in healthcare and interest in this association has grown internationally over the last decade. In a range of healthcare contexts, AI technologies are being used to enhance empathetic awareness; empathetic response and relational behavior; communication skills; health coaching; therapeutic interventions; moral development learning; clinical knowledge and clinical assessment; healthcare quality assessment; therapeutic bond and therapeutic alliance; and to provide health information and advice. The findings inform a reconceptualization of compassion as a human-AI system of intelligent caring comprising six elements: (1) Awareness of suffering (e.g., pain, distress, risk, disadvantage); (2) Understanding the suffering (significance, context, rights, responsibilities etc.); (3) Connecting with the suffering (e.g., verbal, physical, signs and symbols); (4) Making a judgment about the suffering (the need to act); (5) Responding with an intention to alleviate the suffering; (6) Attention to the effect and outcomes of the response. These elements can operate at an individual (human or machine) and collective systems level (healthcare organizations or systems) as a cyclical system to alleviate different types of suffering. New and novel approaches to human-AI intelligent caring could enrich education, learning, and clinical practice; extend healing spaces; and enhance healing relationships. In a complex adaptive system such as healthcare, human-AI intelligent caring will need to be implemented, not as an ideology, but through strategic choices, incentives, regulation, professional education, and training, as well as through joined up thinking about human-AI intelligent caring. Research funders can encourage research and development into the topic of AI technologies and compassion as a system of human-AI intelligent caring. Educators, technologists, and health professionals can inform themselves about the system of human-AI intelligent caring.

With the vigorous implementation of education reform and the widespread implementation of quality education, the role of constructivist learning theory in English teaching, especially English reading teaching, has become increasingly prominent. This paper first learns about the current situation of high school English reading through investigation and research, and the overall low interest and learning motivation of students in English reading, and the low effectiveness of teachers' English reading teaching concepts and methods, and then puts forward the application strategies and methods of constructivist learning theory in high school English reading teaching: stimulating students' reading motivation through heuristic teaching, creating students' reading meaning background through contextual teaching, cultivating students' reading collaboration spirit through cooperative learning, and improving students' reading construction ability through inquiry learning.

Artificial Intelligence (AI) and Natural Language Processing (NLP) technologies play an essential role in the ongoing development and advancements of emergency response systems. This paper explores the new opportunities and possibilities offered by the incorporation of AI-driven automated emergency response systems, and the benefits of using NLP to increase the efficiency of human language processing and communication in real time. Using case studies and applications, we analyse how integrating AI and NLP technologies can reduce response times, improve situational awareness and coordination between emergency services. We then explore how AI-driven models can be used to predict natural disasters, optimise emergency vehicle routes and identify areas of need in real time through the analysis of social media and emergency communications via NLP. However, we also discuss the issues that come with implementing these technologies, such as data-privacy challenges, technical limitations and the need for sustained retraining of AI systems. The conclusions of this research indicate that continued collaboration between stakeholders is necessary to overcome these barriers, and that substantial innovation is needed to maximise the potential of AI and NLP technologies in emergency management.

Under the background of “Internet plus”, the application of English multi-modal reading teaching mode is of great significance. With the continuous development of Internet technology, the field of education has also been deeply influenced, especially in English reading teaching. Therefore, this study aims to explore how to integrate multi-modal resources into English reading teaching under the background of “Internet plus”. Through literature review and case analysis, it is found that multi-modal reading teaching mode can improve students' reading interest and effectiveness. The results show that the use of images, audio, video and other forms of media resources in the teaching process can help students better understand and absorb English reading materials. Therefore, this paper proposes that in the era of “Internet plus”, teachers should actively design multi-modal English reading teaching activities with the help of various digital media resources, so as to improve students' reading level and ability and promote the development and innovation of English teaching.

Simple SummaryArtificial intelligence (AI) technology has been advancing rapidly in recent years and is being implemented in society. The medical field is no exception, and the clinical implementation of AI-equipped medical devices is steadily progressing. In particular, AI is expected to play an important role in realizing the current global trend of precision medicine. In this review, we introduce the history of AI as well as the state of the art of medical AI, focusing on the field of oncology. We also describe the current status of the use of AI for drug discovery in the oncology field. Furthermore, while AI has great potential, there are still many issues that need to be resolved; therefore, we would provide details on current medical AI problems and potential solutions.In recent years, advances in artificial intelligence (AI) technology have led to the rapid clinical implementation of devices with AI technology in the medical field. More than 60 AI-equipped medical devices have already been approved by the Food and Drug Administration (FDA) in the United States, and the active introduction of AI technology is considered to be an inevitable trend in the future of medicine. In the field of oncology, clinical applications of medical devices using AI technology are already underway, mainly in radiology, and AI technology is expected to be positioned as an important core technology. In particular, “precision medicine,” a medical treatment that selects the most appropriate treatment for each patient based on a vast amount of medical data such as genome information, has become a worldwide trend; AI technology is expected to be utilized in the process of extracting truly useful information from a large amount of medical data and applying it to diagnosis and treatment. In this review, we would like to introduce the history of AI technology and the current state of medical AI, especially in the oncology field, as well as discuss the possibilities and challenges of AI technology in the medical field.

The advent of generative artificial intelligence (GAI) and the prominence of core literacy in senior high school have jointly promoted innovative transformations in teaching. This paper employs literature review methodology to first examine current problems in senior high school English reading class, subsequently proposing GAI’ s integrative advantages. Then further identifies implementation challenges and corresponding countermeasures. It is found that traditional English reading class has problems of single reading resources, lacking thinking training and one- sided reading evaluation system, while GAI can enrich reading materials, promote critical thinking, and achieve diversified evaluation. However, there are problems of lacking emotional interaction, teachers’ technical dependence and difficulties in changing teaching concepts. Therefore, this paper proposes a teacher-led, GAI-assisted emotional interaction mechanism, teacher-student-AI triadic collaboration, and progressive conceptual iteration strategies. The paper shows that the deep integration of GAI and English reading teaching needs to balance technological empowerment and traditional educational values.