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Background: The influence of AI and Big Data on the healthcare sector and health delivery systems provides a hopeful path to achieving the Sustainable Development Goals (SDGs) by way of improved decision-making and operational efficiencies. Methodology: A review of existing literature was carried out to evaluate the current landscape of AI applications in healthcare and its potential implications for SDG achievement. Results: Big data is at the center of health transformation, which, combined with artificial intelligence and Internet of Things, enables real-time processing and analysis of huge datasets. Big data enhances customization in marketing, streamlines operations in manufacturing and redefines healthcare by means of precision medicine. Big data also helps to drive changes in society through better public policy choices and steps taken to ensure sustainable development. It is through its analytic power that big data can identify obscure patterns and insights which can lead to pioneering solutions and strategic growth. Conclusion: Applying big data and AI strategically plays a crucial role in advancing the sustainable development agenda. However, we must exercise responsibility when utilizing the power of data. It is important to enforce strict measures of protecting data so as to avoid its misuse and also empower underdeveloped regions with relevant tools and capabilities for efficient data management. This way proactive moves will make sure that all people benefit from the information revolution, and it helps to drive global progress on SDGs.

The Artificial Intelligence Recommender System has emerged as a significant research interest. It aims at helping users find things online by offering recommendations that closely fit their interests. Recommenders for research papers have appeared over the last decade to make it easier to find publications associated with the field of researchers’ interests. However, due to several issues, such as copyright constraints, these methodologies assume that the recommended articles’ contents are entirely openly accessible, which is not necessarily the case. This work demonstrates an efficient model, known as RPRSCA: Research Paper Recommendation System Using Effective Collaborative Approach, to address these uncertain systems for the recommendation of quality research papers. We make use of contextual metadata that are publicly available to gather hidden relationships between research papers in order to personalize recommendations by exploiting the advantages of collaborative filtering. The proposed system, RPRSCA, is unique and gives personalized recommendations irrespective of the research subject. Thus, a novel collaborative approach is proposed that provides better performance. Using a publicly available dataset, we found that our proposed method outperformed previous uncertain methods in terms of overall performance and the capacity to return relevant, valuable, and quality publications at the top of the recommendation list. Furthermore, our proposed strategy includes personalized suggestions and customer expertise, in addition to addressing multi-disciplinary concerns.

The article discusses the potential of advanced technologies in the field of cultural heritage. It highlights how these technologies, such as virtual reality, artificial intelligence, and 3D printing, can be used to understand, preserve, and enhance cultural heritage. The article also presents scientific papers from the Florence Heri-Tech International Conference, showcasing the various applications of these technologies. The papers cover topics such as the use of hyperspectral imaging for hieroglyph recognition, the enhancement of user experience in cultural spaces through advanced systems, and the use of non-invasive techniques for conservation. Overall, the article emphasizes the significant impact of technology on the research, preservation, and promotion of cultural heritage. [Extracted from the article]

Avoiding financial losses requires preventing catastrophic oil‐filled power transformer breakdowns. Continuous online transformer monitoring is needed. The authors use paper insulation to evaluate transformer health for continuous online transformer monitoring. The study suggests a new artificial intelligence method for estimating paper insulation residual life in oil‐immersed power transformers. The four artificial intelligence models use backpropagation‐based neural networks to predict paper insulation lifespan. Four primary transformer insulating paper failure indices—degree of polymerisation, 2‐furfuraldehyde, carbon monoxide, and carbon dioxide—form the basis of these models. Each model, including the backpropagation‐based neural networks, estimates paper insulation life using one failure index, along with moisture and temperature data. Optimisation techniques enhance hidden layer neurons and epoch count for improved performance. Results are validated against literature‐based life models, establishing a precise input–output correlation. This method accurately predicts the remaining useable life of power transformer paper insulation, enabling utilities to take proactive measures for safe and efficient transformer operation. [ABSTRACT FROM AUTHOR]

Phishing is one of the major web social engineering attacks. This has led to demand for a better way to predict and stop them in a commercial environment. This paper seeks to understand the research done in the field and analyse the next steps forward. This is done by focusing on what goes into the selection of proper features, from manual selection to the use of Genetic Algorithms such as ADABoost and MultiBoost. Then a look into the classifiers in use, Neural Networks and Ensemble algorithms which were prominent alongside some novel approaches. This information is then processed into a framework for cloud-based and client-based phishing website detection, alongside suggestions for possible future research and experiments that could help progress the field.

Artificial intelligence (AI) is increasingly being applied to wastewater treatment to enhance efficiency, improve processes, and optimize resource utilization. This review focuses on objectives, advantages, outputs, and major findings of various AI models in the three key aspects: the prediction of removal efficiency for both organic and inorganic pollutants, real-time monitoring of essential water quality parameters (such as pH, COD, BOD, turbidity, TDS, and conductivity), and fault detection in the processes and equipment integral to wastewater treatment. The prediction accuracy ( R 2 value) of AI technologies for pollutant removal has been reported to vary between 0.64 and 1.00. A critical aspect explored in this review is the cost-effectiveness of implementing AI systems in wastewater treatment. Numerous countries and municipalities are actively engaging in pilot projects and demonstrations to assess the feasibility and effectiveness of AI applications in wastewater treatment. Notably, the review highlights successful outcomes from these initiatives across diverse geographical contexts, showcasing the adaptability and positive impact of AI in revolutionizing wastewater treatment on a global scale. Further, insights on the ethical considerations and potential future directions for the use of AI in wastewater treatment plants have also been provided., Competing Interests: The authors declare there is no conflict., (© 2024 The Authors This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY-NC-ND 4.0), which permits copying and redistribution for non-commercial purposes with no derivatives, provided the original work is properly cited (http://creativecommons.org/licenses/by-nc-nd/4.0/).)

Source code is essential for researchers to reproduce the methods and replicate the results of artificial intelligence (AI) papers. Some organizations and researchers manually collect AI papers with available source code to contribute to the AI community. However, manual collection is a labor-intensive and time-consuming task. To address this issue, we propose a method to automatically identify papers with available source code and extract their source code repository URLs. With this method, we find that 20.5% of regular papers of 10 top AI conferences published from 2010 to 2019 are identified as papers with available source code and that 8.1% of these source code repositories are no longer accessible. We also create the XMU NLP Lab README Dataset, the largest dataset of labeled README files for source code document research. Through this dataset, we have discovered that quite a few README files have no installation instructions or usage tutorials provided. Further, a large-scale comprehensive statistical analysis is made for a general picture of the source code of AI conference papers. The proposed solution can also go beyond AI conference papers to analyze other scientific papers from both journals and conferences to shed light on more domains. [ABSTRACT FROM AUTHOR]

This paper presents the prediction of aging state of oil impregnated insulating Kraft paper using textural properties. Under conditions of prolonged thermal stress, the insulating capacity of Kraft paper decreases due to carbonization and degradation of cellulose. To analyze these effects, a new accelerated aging kit is developed for the experiments. Four groups of samples are collected, and the morphology is examined. Texture feature is extracted for the samples using normalization and Gray-Level Co-occurrence Matrix (GLCM). Supervised (Support Vector Machine (SVM)) and unsupervised (K-means) machine intelligence methods are trained to classify images based on the feature. In addition, new samples are checked for feasibility of the prediction of aging state. The calculated training accuracy of SVM is 92.67%, and for k-means is 95.0%, on the other hand, the testing accuracy of SVM is 91.0%, and for k-means is 92.6%. Therefore, this machine intelligence-based methodology using image features is effective in classifying the aging state of the Kraft Insulating Paper. [ABSTRACT FROM AUTHOR]

Nowadays, artificial intelligence, which is used in different fields with the increase in the number of works and transactions carried out in digital environment, is also used in the field of education. The main purpose of this study is to examine the views about artificial intelligence of preschool children, their mothers and teachers. The research was planned with phenomenology research design, one of the qualitative research methods. The study group of the research was formed by selecting a total of 30 people in the convenience sampling method, including 5 preschool teachers teaching in public and private independent kindergartens, 1 student from each teacher's 5year-old class and his/her mother. The data were collected through interviews and the interview questions were prepared by the researchers as open-ended questions. According to the results of the research, it was concluded that although the teachers defined artificial intelligence on the correct scientific basis, they did not have sufficient knowledge, did not use it in educational environments to increase the productivity of children and were worried. Considering the answers given by mothers and children to the questions about artificial intelligence, it can be interpreted that artificial intelligence is not defined correctly and there is a lack of knowledge, mothers have concerns that artificial intelligence may be harmful for their children, and children see artificial intelligence as a living being that they can talk to. [Paper presented at the International Preschool Education Congress (8th).]

Artificial intelligence becomes increasingly prominent in digital language teaching and learning and it will gain even more significance in the future of EFL education (cf. Schmidt & Strasser 2022). Therefore, it is key to explore how those who will shape this future perceive the newest development: pre-service EFL teachers. As Zheng puts it: "EFL pre-service teacher beliefs are the focus of change in the process of education" (2009), consequently their perceptions and beliefs regarding this evolving area of teaching and learning are of utmost importance. Focussing on the latest developments in ChatGPT, this paper aims to give a first insight into the potential and limitations of this AI platform from the perspectives of pre-service teachers in Germany. Therefore, this research project explores the following research questions: (1) Do student teachers consider ChatGPT relevant for their future teaching practice? (2) In which areas of language education do they see potential changes due to ChatGPT? In what ways? and (3) Are there any differences in the perception of ChatGPT between tech-savvy and non-tech-savvy student teachers? The research instrument is a combination of the "technology commitment" scale (Neyer et al. 2016) and items developed specifically for this research project. For RQ3, the analysis refers to existing preliminary work that was able to establish a relationship between technology commitment and the probability of technology application (cf. Knetzek & Christensen 2016). Based on the results of the survey, this paper then aims to offer new perspectives for EFL teacher education and outline concrete reflection activities that further pre-service teachers' critical engagement with AI in language education.

ChatGPT, a generative AI program developed by OpenAI, has raised serious questions about the future of education since its launch in November 2022. This paper argues that ChatGPT has the potential to redefine existing educational theories and the role of teachers in language education. Specifically, the paper examines ChatGPT's impact on language education and suggests ways to incorporate it into teaching practice. Since people have been proclaiming that AI would replace teachers since the 1970s, the history of AI in education is relevant to this paper. Therefore, the paper first discusses the history of AI in education to provide context and highlight how ChatGPT fits into this larger conversation. Afterwards, the paper then explores how ChatGPT might redefine existing learning theories such as Constructivism and Self-Determination Theory using the AIED framework. ChatGPT has the potential to provide learners with personalized and adaptive learning experiences that align with these learning theories. For this reason, the paper will investigate how ChatGPT may impact the role of learners through the lens of Learner Autonomy before looking at how it might also redefine the role of the teacher in the classrooms of the near future. While ChatGPT has the potential to automate certain aspects of language education, the role of the teacher as a mentor and guide in the learning process is still essential. Finally, the paper offers suggestions for how educators can effectively incorporate generative AI programs like ChatGPT into their teaching practice and curriculum. The paper concludes by emphasizing the importance of approaching the integration of AI in education with caution and a critical perspective.

This paper approaches AI in TEFL teacher education from a perspective of digital text sovereignty (digitale Textsouveränität). Digital sovereignty (digitale Souveränität) is a concept that goes beyond media literacy and data literacy as a set of skills, to include personal competences in a more Humboldtian vision of education. Digital text sovereignty focuses on all those aspects of digital sovereignty that apply to the reception and production of texts, from the ability to produce and consume digital texts to the development of a critically-reflexive attitude to these texts (Frederking, 2022; Frederking & Krommer, 2022), making it especially useful in contexts of TEFL teacher education. This paper is based on an empirical study conducted within the EFL teacher education department of a German metropolitan university during the 2023 summer term. It analyzes 21 student essays written after an interactive 90-minute training session on ChatGPT, conducted in three intact TEFL teacher education seminars. Qualitative content analysis is used to identify domains of digital text sovereignty discussed in these essays, and to identify key themes related to four aspects of digital text sovereignty: Mediality, Source Code, Intentionality, and Veracity. The findings might be of interest for researchers and teacher educators interested in (a) modelling digital text sovereignty as it applies to AI; and (b) developing teacher education material and programs that target digital text sovereignty in an AI context, or that aim to support (future) teachers in developing an AI-informed pedagogy.

In K-12 computing education, there is a need to identify and teach concepts that are relevant to understanding machine learning technologies. Studies of teaching approaches often evaluate whether students have learned the concepts. However, scant research has examined whether such concepts support understanding digital artefacts from everyday life and developing agency in a digital world. This paper presents a qualitative study that explores students' perspectives on the relevance of learning concepts of data-driven technologies for navigating the digital world. The underlying approach of the study is data awareness, which aims to support students in understanding and reflecting on such technologies to develop agency in a data-driven world. This approach teaches students an explanatory model encompassing several concepts of the role of data in data-driven technologies. We developed an intervention and conducted retrospective interviews with students. Findings from the analysis of the interviews indicate that students can analyse and understand data-driven technologies from their everyday lives according to the central role of data. In addition, students' answers revealed four areas of how learning about data-driven technologies becomes relevant to them. The paper concludes with a preliminary model suggesting how computing education can make concepts of data-driven technologies meaningful for students to understand and navigate the digital world.

The evolution of mathematics coincided with advancements in its teaching. The 19th and 20th centuries marked a pedagogical revolution in mathematics education. This paper argues that Bruner's (1966) model, Gagné's (1985) taxonomy, innovative teaching methods emphasizing research and problem-solving, and the inclusion of data analysis topics have shaped modern mathematics education. Additionally, the paper explores transformative trends, emphasizing mathematics literacy and the integration of virtual reality (VR) and artificial intelligence (AI) in education. This evolution emphasizes practical, contextually relevant approaches. VR enhances engagement and comprehension of abstract concepts, while AI offers personalized learning experiences, fostering deeper understanding and skill development.

In the context of education, the issues of integrating artificial intelligence (AI) into teaching and maintaining academic integrity in students' use of AI are particularly relevant. This paper empirically examined the issue of ChatGPT usage for writing homework from the perspectives of students and professors. Study research methods included both quantitative and qualitative approaches. In Study 1, an anonymous questionnaire was administered to 350 Croatian students, users of ChatGPT, to investigate their perceptions, attitudes, habits, and intentions regarding ChatGPT usage for homework assignments. In Study 2, twelve faculty members were tested on their accuracy of distinguishing between original students' papers and ChatGPT-generated papers. For this purpose, 25 different versions of papers for 8 different courses were prepared. The results of the students' survey showed that most students still do not use ChatGPT regularly and have neutral attitudes about its usefulness, ease of use, risks, and intentions for future use. In addition, they were moderately concerned about ethical issues around its usage. Differences across gender and field of study were found. Professors, on the other hand, reported having average self-efficacy in appraising authorship, which is in line with their low average accuracy of 53%. Accuracy in distinguishing was lowest when ChatGPT was instructed to write a paper as a student. These results strongly suggest the necessity for clear guidelines, plagiarism detection tools, and educational initiatives to promote ethical use of AI technology.

This paper considers the impact of technological processes on human thought, specifically the implications of artificial intelligence (AI) on writing instruction. The main purpose of this paper is to present instructional considerations that will elevate human voice and reduce student temptations to turn to AI unreasonably to produce a piece of writing while still providing responsible options for the incorporation of AI in the writing process.

Purpose: To align with the artificial intelligence and robotics (AIR) research and policy agenda, this paper puts forth an adapted five big ideas framework specifically tailored to teaching young children about artificial intelligence (AI) via robotics. Design/Approach/Methods: Grounded in early childhood education research, the proposed framework emphasizes the use of robotics and play-based learning to make AI accessible and encourage engagement among young children who have not started formal schooling. Findings: We comparatively analyze the commonalities and differences in AI big ideas between the original K-12 framework and the redeveloped early childhood education framework. To pique children's interest, key concepts are presented through interactions with robotics and robot role-play. This paper also provides recommendations for age-appropriate topics, storytelling, and play-based teaching methods. Originality/Value: This framework aims to equip researchers and educators with strategies for successfully integrating introductory AI education into early childhood classrooms. Teaching AI in a developmentally responsive manner can help nurture young children's curiosity toward and understanding of an increasingly AI-driven world.

This position paper explores the current state of artificial intelligence (AI) tools, educator support of and opposition to AI tools in teaching and learning, and the ethical and social implications of AI tools in higher education. As technology continuously develops in the educational community, educators must have a voice in how AI exists in the classroom. This paper addresses support of and opposition to AI implementation and the need for more studies on teacher and student experiences with AI tools, such as personalized learning platforms and intelligent tutoring systems. The practical applications of AI in future studies should explore how to best implement AI tools while advancing knowledge and maintaining academic integrity as students and faculty become more technologically literate citizens. Studies in educational technology have acknowledged that social and ethical implications arise from the advance of AI. Programming diverse practices into AI applications impact the data output when generating new content. Therefore, this position paper acknowledges the need for being inclusive in framing technology and AI tools across less developed countries, emerging economies, and developed countries using varying theories, such as situated learning theory, technology affordances and constraints theory, decolonial theory, and intersectionality.

This paper addresses the pedagogical implications of incorporating ChatGPT into the college English classroom specifically and, more broadly, into any college course with a focus on writing and research. Historically, advances in technology in the college classroom have characteristically promoted two juxtaposed reactions: relief and anxiety. Students customarily exhibit relief that a new technology will lessen their workload and embrace it wholeheartedly. Conversely, faculty often experience anxiety at how some newfangled computerized application will impact student learning. This juxtaposition creates barriers to an effective integration of new technology into the classroom. What students view as a cool new tool faculty see as a platform that promotes student slacking or, at worst, cheating. Such is the case with ChatGPT. I review generally the ethics of using ChatGPT as a classroom tool to conclude that the potential for advancing educational equity among students outweighs any potential for misuse of this quickly evolving technology. Relying upon established principles of classroom instruction as well as significant trial-and-error experience, I propose a pedagogical framework that allows for limited application of ChatGPT in selected scaffolded assignments. I further offer specific lesson plans to show how incorporation of ChatGPT into the college composition classroom can align with universally accepted goals, objectives, and student learning targets in both freshman composition and traditional literature courses, all while removing barriers and promoting equity. This paper provides faculty who are not already well-versed in ChatGPT with information to evaluate its efficacy for their courses and a flexible framework to include into their pedagogy easily modifiable ChatGPT-based lesson plans that present challenging yet fun scaffolded assignments for any writing or research curriculum.

In the realm of language acquisition, the integration of Artificial Intelligence (AI) presents a promising frontier. However, gaps exist in understanding the practical application of AI-driven tools, particularly in second language learning contexts. This study delves into the usability of ChatGPT, an advanced AI language model, within the domain of second language acquisition. This paper synthesizes existing literature on ChatGPT's multifaceted capabilities, its effectiveness, its associated challenges, and the potential solutions to these challenges in language learning environments. This review demonstrates ChatGPT's substantial potential in enhancing language learning outcomes, including fostering learner autonomy, improving motivation, and developing language proficiency. Yet, nuanced challenges such as preserving academic integrity and difficulties in crafting effective prompts emerge as crucial considerations. To address these issues, possible solutions including enhancing AI literacy among learners and educators are discussed. This paper sheds light on the complex dynamics of AI-assisted language education, urging ongoing research and refinement to fully utilize these technologies in enhancing second language learning.

This scoping review explores the ethical challenges of using ChatGPT in higher education. By reviewing recent academic articles in English, Chinese, and Japanese, we aimed to provide a deep dive review and identify gaps in the literature. Drawing on Arksey & O'Malley's (2005) scoping review framework, we defined search terms and identified relevant publications from four databases in the three target languages. The research results showed that the majority of the papers were discussion papers, but there was some early empirical work. The ethical issues highlighted in these works mainly concern academic integrity, assessment issues, and data protection. Given the rapid deployment of generative artificial intelligence, it is imperative for educators to conduct more empirical studies to develop sound ethical policies for its use.

Artificial Intelligence (AI) and related technologies have a long history of being used in education for motivating learners and enhancing learning. However, there have also been critiques for a too uncritical and naïve implementation of AI in education (AIED) and the potential misuse of the technology. With the release of the virtual assistant ChatGPT from OpenAI, many educators and stakeholders were both amazed and horrified by the potential consequences for education. One field with a potential high impact of ChatGPT is programming education in Computer Science (CS), where creating assessments has long been a challenging task due to the vast amount of programming solutions and support on the Internet. This now appears to have been made even more challenging with ChatGPT's ability to produce both complex and seemingly novel solutions to programming questions. With the support of data collected from interactions with ChatGPT during the spring semester of 2023, this position paper investigates the potential opportunities and threats of ChatGPT for programming education, guided by the question: What could the potential consequences of ChatGPT be for programming education? This paper applies a methodological approach inspired by analytic autoethnography to investigate, experiment, and understand a novel technology through personal experiences. Through this approach, the authors have documented their interactions with ChatGPT in field diaries during the spring semester of 2023. Topics for the questions have related to content and assessment in higher education programming courses. A total of 6 field diaries, with 82 interactions (1 interaction = 1 question + 1 answer) and additional reflection notes, have been collected and analysed with thematic analysis. The study finds that there are several opportunities and threats of ChatGPT for programming education. Some are to be expected, such as that the quality of the question and the details provided highly impact the quality of the answer. However, other findings were unexpected, such as that ChatGPT appears to be "lying" in some answers and to an extent passes the Turing test, although the intelligence of ChatGPT should be questioned. The conclusion of the study is that ChatGPT have potential for a significant impact on higher education programming courses, and probably on education in general. The technology seems to facilitate both cheating and enhanced learning. What will it be? Cheating or AI-enhanced learning? This will be decided by our actions now since the technology is already here and expanding fast. [Note: The publication year (2023) shown in the citation on the PDF is incorrect. The correct publication year is 2024.]

This paper provides a thorough examination of the role of Artificial Intelligence (AI), particularly ChatGPT and other AI language models, in the realm of education. Drawing insights from existing literature and a novel study on educator perspectives, the paper delves into the potential advantages, ethical dilemmas, and factors shaping educators' attitudes towards AI integration in education. AI language models have the potential to revolutionize educational content creation, personalize learning experiences, and streamline assessment and feedback processes. These capabilities hold the potential to enhance teaching and learning outcomes while catering to the diverse needs of students. However, ethical concerns loom large in the adoption of AI in education. Bias in generated content is a chief concern, as it can perpetuate societal biases and lead to unfair treatment or the dissemination of inaccurate information. The solution lies in rigorous data curation to ensure equitable educational experiences for all students. Moreover, the potential for generating inappropriate or misleading content poses a significant ethical challenge, impacting students' well-being, civic understanding, and social interactions. Safeguards must be implemented to detect and rectify biased or inappropriate content, fostering inclusive and unbiased learning environments. Transparency emerges as a crucial ethical consideration. The opacity of AI models like ChatGPT makes it difficult to comprehend their decision-making processes. Enhancing model interpretability and explainability is vital for accountability and addressing embedded ethical issues. Privacy concerns related to data collection and usage are emphasized in the literature. Clear policies and guidelines must govern data collection, use, and protection, ensuring data is solely employed for educational purposes and maintaining robust data security measures. Our study expands upon these insights by exploring socio-demographic factors, motivations, and social influences affecting educators' AI adoption in higher education. These findings inform institutions on tailoring AI integration strategies, emphasizing responsible usage through training, and assessing the impact on learning outcomes. As educational institutions increasingly embrace AI, including advanced models like GPT-4, a cautious and thoughtful approach is vital. Balancing potential benefits with ethical challenges ensures that AI enhances teaching and learning while upholding fairness, equity, and accountability. In summary, this paper illuminates the potential of AI in education, accentuates ethical concerns, and highlights the significance of understanding educators' perspectives. Collaboration between educators and policymakers is essential to navigate the complexities of AI integration, ensuring that education remains a realm of equitable, efficient, and accountable learning experiences.

This paper charts a forward-looking roadmap for open universities, drawing upon their historical evolution and current practices. It advocates a shift toward a universally accessible, personalized education system. At the heart of this proposed advancement lies the customization of learning paths and experiences, where individualized advising and mentorship, and a variety of learning content, resources, and environments are essential. The study underscores the importance of integrating advanced technologies such as artificial intelligence and blockchain into the open and distance education system. Within the discourse, the paper delineates three primary areas for open universities to address: system transformation, expansion of openness, and integration of digital innovation. The concluding part of the paper offers possible strategic recommendations for policymakers and researchers of open universities. The essence of these recommendations is advocating for a universally personalized educational paradigm while making a strong case for addressing the digital divide, fostering strong partnerships at both global and community levels, and supporting the use of the latest technology to its fullest potential. By navigating this transformative journey, open universities are not just participating in the evolution of educational models but also poised to lead a revolution in the broader landscape of higher education.

Artificial intelligence in education (AIEd) is a fast-growing field of research. In previous work, we described efforts to explore the possible futures of AIEd by identifying key variables and their future prospects. This paper re-examines our discussions on the governance of data and the role of students and teachers by considering the implications of (1) a recent case related to the General Data Protection Regulation (GDPR) and (2) the release of ChatGPT, a generative AI model capable to producing 'human-like' text. These events raise questions for the future of AIEd and the underlying function of assessment, and highlight the importance of active student participation in the integration of AI in education. [This article has been presented in the 5th International Open & Distance Learning Conference-IODL 2022.]

The rapid advancement of artificial intelligence technologies, exemplified by systems including Open AI's ChatGPT, Microsoft's Bing AI, and Google's Bard (now Gemini 1.5Pro), present both challenges and opportunities for the academic world. Higher education institutions are at the forefront of preparing students for this evolving landscape. This paper examines the current state of AI education in universities, highlighting current obstacles and proposing avenues of exploration for researchers. This paper recommends a holistic approach to AI integration across disciplines, fostering industry collaborations and emphasizing the ethical and social implications of AI. Higher education institutions are positioned to shape an educational environment attuned to the twenty-first century, preparing students to be informed and ethical contributors in the AI-driven world.

Since the advent of computer-mediated communication (CMC) technologies, the landscape of education, especially in English Language Teaching and learning, has undergone significant transformations. Academic writing, a key skill in this context, has witnessed notable benefits from technological advancements, particularly in the contemporary era. One prominent technology reshaping the academic landscape is ChatGPT. While it offers abundant learning opportunities for second language learners and teachers, it also poses significant ethical challenges. This paper provides an overview of the opportunities and challenges presented by ChatGPT and other AI-based technologies concerning writing skills, specifically academic writing in English as a second language. The study includes a descriptive account of my interview with ChatGPT regarding the opportunities that it has presented and the challenges posed for L2 students and teachers. The interview results indicate that ChatGPT can be employed in various ways to enhance the second language writing process for both students and teachers, notwithstanding the latter's reservations about its ethical implications. In addition, the paper offers some practical activities that can be implemented in L2 academic writing classes.

Artificial intelligence (AI) is now affecting all aspects of our social lives. Without always knowing it, we interact daily with intelligent systems. They serve us invisibly. At least that is the goal we assign to them: to make our lives better, task by task. Artificial intelligence has the potential to make biology education more engaging, personalized, and effective by providing students with interactive simulations, personalized learning experiences, and other tools that help them understand complex biological concepts. In this paper, we discuss the integration of AI into the virtual classroom, which significantly enhances student learning experiences in various ways. The study shows that an effective integration of technology into the virtual classroom requires a thoughtful approach that aligns with educational goals and the specific needs of students. In fact, interactive simulations can help make biology more engaging and memorable for students. Besides, personalized learning AI algorithms can help biology students receive a more tailored and effective learning experience, helping them to better understand the course material and develop a deeper appreciation for the natural world. In this work, we will discuss the use of AI to enhance interactive simulation-based cellular processes, with additional application in anatomy, physiology, and ecology teaching. Moreover, this paper discusses how AI could be used to analyze student data and propose personalized learning using adaptive assessments, content recommendations, and data sciences. This paper illustrates examples of AI algorithms that could be useful for teaching biology.

Abstract To achieve clinical impact in daily oncological practice, emerging AI-based cancer imaging research needs to have clearly defined medical focus, AI methods, and outcomes to be estimated. AI-supported cancer imaging should predict major relevant clinical endpoints, aiming to extract associations and draw inferences in a fair, robust, and trustworthy way. AI-assisted solutions as medical devices, developed using multicenter heterogeneous datasets, should be targeted to have an impact on the clinical care pathway. When designing an AI-based research study in oncologic imaging, ensuring clinical impact in AI solutions requires careful consideration of key aspects, including target population selection, sample size definition, standards, and common data elements utilization, balanced dataset splitting, appropriate validation methodology, adequate ground truth, and careful selection of clinical endpoints. Endpoints may be pathology hallmarks, disease behavior, treatment response, or patient prognosis. Ensuring ethical, safety, and privacy considerations are also mandatory before clinical validation is performed. The Artificial Intelligence for Health Imaging (AI4HI) Clinical Working Group has discussed and present in this paper some indicative Machine Learning (ML) enabled decision-support solutions currently under research in the AI4HI projects, as well as the main considerations and requirements that AI solutions should have from a clinical perspective, which can be adopted into clinical practice. If effectively designed, implemented, and validated, cancer imaging AI-supported tools will have the potential to revolutionize the field of precision medicine in oncology.

Machine learning has been leveraged for image analysis applications throughout a multitude of subspecialties. This position paper provides a perspective on the evolutionary trajectory of practical deep learning tools for genitourinary pathology through evaluating the most recent iterations of such algorithmic devices. Deep learning tools for genitourinary pathology demonstrate potential to enhance prognostic and predictive capacity for tumor assessment including grading, staging, and subtype identification, yet limitations in data availability, regulation, and standardization have stymied their implementation.

The world is undergoing a thoughtful revolution with the arrival of the intelligent information era. The central realms accompanying smart living such as transportation, entertainment, healthcare and smart cities are projected to improve service quality assuring a high-end user experience. Future mobile networks are projected to foster the future of ubiquitously connected data-intensive intelligent society powered with complete automation by seamless integrating of all sorts of wireless networks spread over the ground, underwater, air and space. This special issue aims to bring together foremost researchers in academia and engineering from various backgrounds to disseminate to the technical community an outline of emerging technologies, advanced architectures, challenges, open issues and future directions of modern networks in artificial intelligence, internet of things, and blockchain-based applications.