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Scoping review Protocol

This research project aims to explore how artificial intelligence (AI) is currently being utilized to support assessments within Competency-Based Medical Education (CBME). CBME is an outcomes-focused approach to medical training that emphasizes the acquisition and demonstration of specific competencies rather than time-based progression. As this educational model gains global adoption, there is an increasing need for innovative assessment tools that are scalable, individualized, and capable of providing meaningful feedback on learner performance. Artificial intelligence, with its capacity to analyze complex data, detect patterns, and support real-time decision-making, has the potential to address many of these needs. The primary objective of this project is to systematically map the existing literature on the use of AI technologies for assessment purposes in CBME contexts. This includes identifying the types of AI tools being implemented (e.g., natural language processing, machine learning, computer vision), the competencies or assessment domains they target (e.g., clinical reasoning, communication skills, technical proficiency), and the settings in which they are applied (e.g., undergraduate medical education, postgraduate training, simulation environments, clinical workplaces). This review will also examine how AI-driven assessments align with the core principles of CBME, such as programmatic assessment, direct observation, and feedback-rich environments. Additionally, it will explore the benefits, challenges, and ethical considerations related to the use of AI in learner assessment, including issues of bias, transparency, and integration with existing educational systems. Expected outcomes of this project include: A comprehensive synthesis of how AI is currently being used to assess competencies in medical education. Identification of gaps in the literature and areas requiring further research. A conceptual framework to guide future applications of AI in CBME assessment practices. Practical insights for educators, institutions, and developers interested in leveraging AI to support competency-based training. By consolidating the current state of knowledge, this project aims to inform future innovations in medical education and promote the responsible, evidence-informed use of AI to enhance learner assessment.

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Artificial intelligence is becoming increasingly embedded in educational practice, especially since the rise of generative AI tools in late 2022. Although prior reviews have addressed the role of AI in education, most were published before the emergence of these tools or focus on narrow subtopics. A comprehensive, updated synthesis is needed to understand global trends, educational outcomes, and implementation challenges associated with AI adoption.

The expansion of Artificial Intelligence (AI) in radiography is predicted to transform future clinical workflows, decision-making, and the role and responsibilities of practitioners. If AI is to be an important aspect of radiography practice it may be reasonable to expect that graduates need to be educated and equipped to engage in competent and safe utilisation of these technologies. Therefore, the current and emerging status of AI in radiography education needs to be explored.

This scoping review provides comprehensive understanding of the available literature on this topic and serves as precursor for a systematic-review in this field.

A peer review with author input analyzing the open science principles and reproducibility of full papers, short papers, and posters sent to the 22nd and 23rd Artificial Intelligence in Education conference.

Strong communication skills, such as interviewing, empathetic communication, and explaining diagnosis or treatment options, are essential for healthcare professionals as they form the core of the patient-provider relationship and can affect the patient’s outcomes (Silverman et al., 2013). Traditionally, those skills have been taught through role plays or simulations (Tiuraniemi et al., 2011), yet in traditional education and large classrooms, role plays and simulations cannot be easily offered due to the high number of students or logistical constraints. However, recent advances in machine learning algorithms have also started to affect education. Generative AI powered by Large Language Models (LLM), such as ChatGPT, are able to give more context-dependent, human-like conversation responses (Wei et al., 2024), making training easily accessible by providing answers to frequently asked questions and directing questions to the learner to enhance the learning process (Nee et al.,2023). While some studies have investigated the impact of chatbots on specific skill training in various groups (Labadze, Grigolia, & Machaidze, 2023), the results are contradictory and there is a need to address the use of chatbots effect on the field of clinical communication skills to better prepare students for clinical interactions. As the use of chatbots in education continues to evolve rapidly, the current study will conduct a scoping review and meta-analysis to systematically map the existing literature and evaluate the effectiveness of chatbot-based communication skills training in healthcare education. The findings of this study can have significant implications for the healthcare education field, potentially guiding the development of future curricula to ensure students' readiness for clinical interactions with patients.

Artificial Intelligence (AI) has brought unprecedented growth and productivity to every socioeconomic sector. The application of AI in education is transformational, encompassing the reduction of teacher workload, individualized learning, intelligent tutoring, profiling and prediction, high-precision education, collaboration, and learner tracking. This paper highlights the directions of AI research in higher education (HE) through bibliometric analysis. We adhered to the PRISMA guidelines to select 1610 articles published in the Scopus database between 2014 and 2023. VOSviewer was employed for visualization, and text mining was utilized to identify hotspots in the field. Spain, the United States, the United Kingdom, China, and India dominate the publications. Articles on this topic are predominantly published in conference proceedings and journals. Four themes emerge: data as a catalyst, AI development, AI implementation in higher education, emerging trends, and the future of AI in higher education. This research contributes to the literature by synthesizing opportunities for AI adoption in higher education, topic modeling, and future research areas.

This scoping review aims to identify and describe research exploring the application of Generative Artificial Intelligence (Gen AI) as a teaching, learning, and assessment strategy in nursing education. Additionally, it seeks to determine the ethical considerations it presents and the measures taken to address them in the nursing educational setting.

Driven by globalization and technological development, English education in China is facing unprecedented challenges and opportunities. In recent years, discussions on the future development of English education have been heating up. How artificial intelligence and English education will coexist and influence each other has become a hot topic in the education community. This paper analyzes this issue from multiple perspectives and explores the opportunities and challenges of English education in China in the era of artificial intelligence.

This review considers quantitative studies analyzing a measured effect (outcome) of IA-based tools on assessment and or performance of health care professionals and/or trainees (medical students, medical trainees, interns, residents, health professionals, and nurses). We will exclude articles with no original data, reviews, surveys, case reports, comments, letters, book chapters, study protocols, studies not in English or French, and studies only published as abstracts or conference proceedings.

Long Island Youth Summit Best Research Paper

Long Island Youth Summit Best Research Paper

This study examines retracted research papers in the field of Artificial Intelligence in Education (AIED) from 1990 to 2025. It explores bibliometric characteristics and retraction patterns to identify trends related to publication year, citation impact, journal prestige, and authorship. The first research question is exploratory and aims to describe the landscape of AIED retractions. Additional questions test hypotheses about differences in retraction reasons and timing across journal types, citation levels, and authorship patterns. Data will be collected from Scopus, Web of Science, and OpenAlex, with analysis conducted using the bibliometrix package in R. The goal is to better understand retraction dynamics and promote research integrity in AIED. The study is expected to provide a detailed bibliometric profile of retracted AIED papers and add insights into how misconduct and retraction reasons differ by journal impact and citation status. In addition, the study aims to provide a clearer understanding of how authorship and peer review integrity may influence the likelihood and timing of retractions, and to inform future ethical and methodological considerations in AIED research.

This project shares the protocol of a systematic review on generative artificial intelligence (GenAI) and creativity in education. The review focuses on structured interventions where tools such as ChatGPT, DALL·E, or Midjourney are integrated into teaching and learning processes. By synthesizing empirical findings across primary, secondary, and higher education, the project seeks to clarify how GenAI influences creative outcomes, highlight methodological challenges, and provide guidance for educators and policymakers on the responsible integration of AI into curricula.

This research work, explores the important incidence that Artificial Intelligence has have in the development of modern higher education and education at all in our country, the Dominican Republic, before, during, and after the Sanitary Emergence brought by the COVID-19 Pandemic. Through this "Documentary Research," we examine in details the case of the Dominican Republic as compared to those cases occurred in the international arena, as a means for reflection and understanding of this phenomena called Artificial Intelligence on its different modalities since Computerizing and Internet changed our lives, decades ago. It is the purpose of this work to bring light to the said technological phenomenon, which has been ultimately the product of fears on the emergence of a "Robotized Society," where we the Human Beings would lose our capacity of controlling the machines. Although our focus of interest lies on EFL Teaching-Learning Processes, it does not refer exclusively to this particular field of education, but to it different components, such as Applied Linguistics, Psychological factors and Modern Technology (CIT'S) as well, reason why a Multidisciplinary Team was formed to undertake this task.

Abstract Background and Aim of Study:  The benefits and challenges of using artificial intelligence (AI) in higher education are discussed. This has been the subject of a great deal of discussion among the general public and in the academic periodicals. The aim of the study:  to specify the benefits and challenges of using AI in academic university teaching based on a review of periodical research, and to develop a classification of directions for the use of artificial intelligence in higher education for interacting stakeholders. Material and Methods:  The present study used a number of theoretical methods: analysis, synthesis, comparison, generalisation, systematisation to define the benefits and challenges of the use of AI by stakeholders; classification and modelling to develop a classification of directions for the use of AI in higher education. Results:  It highlights the key benefits and challenges of using AI in academic university teaching that stakeholders face. Classification of directions of AI use in higher education is developed. The following four criteria are highlighted: content of education; forms and methods of teaching; diagnosing of learning outcomes; administering of educational services. Conclusions : AI offers exciting new prospects for its application in higher education, but there are also many concerns about its rapid development First and foremost, there are the issues of the ethical and legal implications of using AI in higher education. The results of the study are important for stakeholders involved in developing strategies for the use of AI in higher education. The need to increase digital literacy and prepare all higher education institutions for the intensive process of information technology development in the coming years is highlighted.

Artificial intelligence (AI) technology is already challenging a variety of societal areas, including education. It is transforming education to data driven. AI-enhanced technologies in education (abbreviated AIinED) will have a significant role in changing the teaching and learning methods, as well as impacting the behavior and organization of the educational system. It is considered that the AIinED will change the paradigm of education in the future. And yet, AIinED is still more in the lab than being practically implemented in education and training. We consider three major players in the implementation of AIinED -- students, teachers, and society. All three can benefit from AIinED and at the same time be a potential target of the risks that AIinED brings along with its promises -- may be one of the reasons why main stakeholders (UNESCO, EC etc.) have been developing guidelines and recommendations for ethical use of AIinED. The literature shows that the center of AIinED system will be the student, but we consider the student not only as a target but also as a source of ideas for AIinED development with the potential to accelerate the process of adoption of AIinED. Hence, one of the big questions should be how the students foresee the role of artificial intelligence in education. To initiate such a question, though, it is important to know the level of understanding among the students about what and where artificial intelligence is. There are three major aspects that AIinED must be considered accordingly -- technological, lawful, and ethical. This paper presents the results of a study on high school students' understanding of AI technologies and their attitudes to their application in education. A survey was used as a tool to elaborate. The conceptual model of the research was developed on the basis of established theories linking attitudes to behavior and the acceptance of artificial intelligence technologies in education. Each element of this concept is explored with a different part of the questionnaire, which contains a total of 12 questions (some of which with sub-questions). The survey was elaborated online within October-November 2021. A link to the questionnaire in Bulgarian was provided to 178 high and vocational high schools educating students aged 14-19 (grades 8-12) across the country (Bulgaria). 766 students submitted their replies through the online survey form. Descriptive statistics and analysis of the frequencies of the respondents' opinions were made based on the data. The results show that the students participating in the survey:(a) understand the essence of AI technologies; (b) they are convinced of the usefulness of the application of artificial intelligence technologies in their daily activities and strongly believe that it improves it; (c) are not entirely clear about the benefits of artificial intelligence enhanced technologies in learning and teaching; (d) do not demonstrate sufficient knowledge and understanding of the necessity of ethical use of AI technologies in education; The latter reduces the positive influence of the perceived usefulness of artificial intelligence technologies in the learning process on students' attitudes. [For the full proceedings, see ED639391.]

There is tremendous potential for artificial intelligence to transform learning and education. But, for the technology to truly flourish, it requires insight from its most important stakeholders, young learners. The importance of youth voices in AI for education posits that the youth should not merely be on the receiving end of AI-informed educational experiences but proactive agents with important contributions. Educators who intersect with serving the needs of young students, outstanding fluency in technological tools among their peers, and idea-generative specialists can help direct how AI offerings should be created to make more engaging ones that work efficiently while accommodating them. In this article, the investigation describes how workshops, surveys, and co-creation initiatives are necessary ways to promote youth participation. The article, therefore, advocates for youth-friendly interventions (workshops, surveys) and co-creation processes. They round out by suggesting that the representation of youth voices in the design and practice of AI for education will not only be impactful but also expected. The future depends on the potential of young minds, so technological advancements must serve as enablers rather than obstacles to progress. The insights presented here highlight the importance of actively engaging young people in AI development and outline some of the obstacles they encounter, along with strategies for empowering them to contribute to the future through intelligent educational systems.

This meta-review aims to address this knowledge gap by answering the following review questions synthesizing existing SCLRs: a) what are the applications of different AI technologies in health professions education (HPE)?, b) what are the outcomes of AI applications in HPE?, and c) what are the factors influencing the applications of AI in HPE?

To systematically evaluate how artificial tools and systems contribute to competency based medical education

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To identify and map the range of research available in the literature on the use of artificial intelligence in the context of oral health education for patients.

Introduction: Rationale Kidney stone disease (KSD) is a common urological condition, with an estimated prevalence of 1% to 20%.1 It also represents a major burden for the patient, affecting his health and quality of life, but also an economic burden for society, due to the costs associated with managing the disease.2 Patient awareness of KSD is an important element in the prevention of this condition, its optimal management, and the reduction of the associated burden.3,4 Nevertheless, patients lack information about the disease, the preventive measures to be implemented, the investigations carried out, and the management.4 Moreover, the KSD resources available online, which are frequently used by patients, have a low level of readability,5 making access to information more difficult. Artificial Intelligence (AI) tools are currently undergoing rapid development, with a marked interest in integrating these tools into medical practice. Generative artificial intelligence represents a type of artificial intelligence capable of generating content based on large quantities of data. These tools can answer patients' questions, generate educational content, and offer explanations about the various stages of the management of a patient's illness.6 This is applicable to the field of urology, where several studies have looked at the use of AI for patient education regarding KSD. For example, these tools have been used to provide nutritional and lifestyle advice,7 symptoms to watch out for, help guide with medication, pain management, and surgical management.8 Several studies have evaluated these tools from different angles, including the accuracy of the information generated, readability, empathy and patient perception.9 Conclusions regarding these tools are heterogeneous, with some authors highlighting the accuracy of the information provided, which surpasses official educational material,10 while others highlight discrepancies with guidelines11 and readability difficulties.12 Moreover, there is a lack of studies that evaluate the use of generative AI in clinical practice and the perspective of patients regarding these tools. These elements justify the need for a scoping review to synthesize the available data and provide an overview of the use of generative AI for patient education on KSD, given the heterogeneity of designs and outcomes evaluation across studies. This scoping review will also serve as a foundation for a future prospective project, to evaluate the use of generative AI in clinical practice with a focus on patients’ perspectives. Research question and objectives Research question: Can generative artificial intelligence provide quality patient-targeted educational content about kidney stones? Objectives : Conduct a scoping review to: - Evaluate the quality of information provided by generative AI tools regarding patient education on kidney stones, with a focus on expert-rated outcomes such as information accuracy, readability, and adherence to guidelines, as well as patient perception when available. - Explore the benefits and limitations of integrating generative AI tools for kidney stones patient education into clinical practice - Identify gaps in the literature and suggest lines of research on the clinical integration of generative AI tools for patient education on kidney stones

The effective anti-corruption education requires careful understanding of the teaching-learning context. At the different stages of the designing an anti-corruption focuses course, we need to consider factors related to target learning group as well as the respective context in which they are. Learning style versatility is an important factor that needs to be analyzed for an effective and outcome linked course in Anti-Corruption. The emergence of artificial intelligence provides significant opportunities in improving the outcome-focused course design. Automating the identification of learning style versatility of target learning group can simplify the process of customizing the course contents, pedagogy and assessment framework for the given learning group. The article is conceptual in nature and provides a generic framework for applying artificial intelligence (AI) in developing the anti-corruption course, tailored to the target learning group characteristics. The suggested framework can be adapted under the different teaching-learnings contexts, as per the need. The anti-corruption is an extended and applied area of ethics and, therefore, the framework can also be extended in developing course modules related to other similar themes. [For the full proceedings, see ED654100.]

This work is done with the motto: ‘To make Artificial Intelligence (AI) a powerful tool for education, which will enable us to become masters of ourselves, our homeland, and our nation.’ The purpose of this work is to raise public awareness in Kosovo and Albania, especially among those responsible in government institutions, ministries of education, NGOs in the field of education, and other interested parties about the importance of AI technology for the advancement of education.

The objectives of this scoping review are to explore the current evidence on the utilization and integration of AI-powered Simulation Based Education (SBE) into healthcare education. Moreover, it will identify the perceptions, facilitators, and barriers of integrating this strategy within the healthcare education.

Note this registration has been withdrawn: Note this registration has been withdrawn: The researcher of this study will conduct a bibliometric with scoping analysis of the existing literature in the Philippines found on the Scopus database focusing on integration of AI in mathematics education from 2014 to 2024. Scopus indexing provides access to bibliometric indicators and citation tracking, which are valuable for evaluating academic impact and influence of a journal (Santos de Oliveira et al., 2024). Scoping and mapping reviews are particularly useful for efficiently exploring broad research areas (Christou et al., 2024).These methods provide a comprehensive overview of the field, helping researchers gain insights into application domains, participants, research methods, technologies, and key issues (Hwang & Tu, 2021; Kaushik et al., 2021). This research will be written with the guidance of Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) by Tricco et al.(2018). Researchers are encouraged to consult these guidelines during the initial preparation of manuscripts to ensure comprehensive and consistent reporting in scoping and bibliometric analyses (Miller & Colquhoun, 2020; Fourie & Grobbelaar, 2022).

Artificial intelligence (AI) is becoming increasingly influential in the academic sector, which is why it is important to explore the ethical dilemmas and concerns surrounding AI chatbots’ design, development, and deployment in educational contexts. Conducted as a thematic literature review, this paper explores existing research on AI in education, AI chatbots, and their integration with higher education to gather evidence and insights that discuss ethical implications and challenges. The study has analyzed several articles on AI chatbots and their integration into academic fields. Significant gaps have been identified, such as the need for more practical implications and the recognition of AI chatbots as a collaborative tool for academic purposes. More AI chatbots should be explicitly trained on data relevant to the learners’ study to examine their usefulness properly. The paper discusses the ethical dilemmas and concerns about the design, development, and deployment of AI chatbots in higher education. It seeks to provide insights and recommendations to ensure the ethical use of AI chatbots in higher education by identifying significant gaps in the existing literature and providing scenarios to expect in the development of AI in education.

The integration of Artificial Intelligence (AI) into the legal profession has accelerated in recent decades, reshaping both the teaching and practice of law. This article explores the historical development of AI in the legal sector, its growing role in legal education, international best practices in AI-assisted teaching, and its transformative impact on legal research. By examining AI’s progression from early expert systems to advanced machine learning and natural language processing tools, the study demonstrates how AI aids in personalized learning, efficient case analysis, and predictive analytics. However, the article also addresses the major challenges posed by AI’s increasing influence, including ethical concerns, bias, data privacy, and skill gaps. It concludes by emphasizing the need for careful, ethically grounded, and strategically planned integration of AI into legal education and research, ensuring that technological innovations serve the overarching goals of justice and professional integrity.

Introduction: Artificial Intelligence (AI) is a broad umbrella term referring to simulation of human intelligence through the use of computer systems. With the need for improvements in delivery, precision, and efficacy of patient care, AI has been rapidly integrated into healthcare systems. Equally as important in the delivery of healthcare is AI’s role in the education of healthcare providers. However, the use of AI for curriculum development and assessment of learning for current and future healthcare providers remains to be explored. As such, this scoping review primarily aims to explore the present use of AI in the education of healthcare professionals to identify learning needs for training, personalize learning based on user data, and to use data science for evaluating healthcare professional education programs. Methods and analysis: The study design has been adapted from Arksey and O’Malley’s scoping review methodology. We will work with an information specialist who will run searches in six multidisciplinary databases (PsychINFO, CINAHL, EMBASE, PubMED, IEEE, Ovid), inclusive of healthcare/medicine and technology search engines. To supplement the search, we will scan the reference lists of included studies. Two levels of screening will take place: a title and abstract review for articles that fit predefined criteria and a full-text review of articles that meet those criteria. Articles to be included will be those of which AI technology is used, articles focused on needs assessment, curriculum content, and evaluation of learning in healthcare professional education. Two investigators will review each article and extract data using Covidence. Data extraction will be carried out using several components of Kern’s 6-step curriculum development as an abstraction template to categorize articles related to the use of AI in health professions education. Ethics and Dissemination: Ethics approval was not required for any component of this scoping review. The results of this review will be submitted to a Medical Education Journal, presented at national and international health professions education conferences, shared with Temerty Faculty of Medicine and Toronto Academic Health Sciences Network education committees. Data from this review will be used to shape future integration of AI in learning assessment for healthcare professionals.

This scoping review explores the extent and nature of evidence on integrating Artificial Intelligence (AI) in dental education, focusing on its alignment with accreditation standards and mechanisms for ensuring the quality and reliability of AI-generated educational content.

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The concept of Artificial Intelligence (AI) was first proposed in the Dartmouth Conference in the 1950s, and now refers especially to the technology of simulating human intelligence through algorithms and data analysis. With the rapid development of AI technology, Large Language models (LLMS) have also developed rapidly in various fields, and ChatGPT, launched by OpenAI, has shown excellent performance in the field of medical education. Artificial intelligence and large language models can provide personalized and interactive learning experiences by integrating and analyzing data, which plays a huge role in the field of medical education. Artificial intelligence and large language models provide potential advantages in medical education. However, as a promising field, it also brings some ethical challenges. Privacy, autonomy and transparency have become the key issues of integrating artificial intelligence and large language models in medical education. Patients' right to know and privacy security issues, technology accessibility and affordability, responsibility division issues have become its key ethical challenges. This review aims to focus on the challenges of integrating artificial intelligence and large language models in medical education, analyze the ethical problems contained in them, and explore feasible solutions. Therefore, we conducted a scoping review of the existing literature to understand the main ethical challenges of AI and LLM in medical education and to provide guidance for future applications in academia.

This scoping review will assess the literature that documents the use of artificial intelligence in patient education in a health care setting.

The present study is part of a systematic review of original articles published between 2017 and 2023 on artificial intelligence in learning mathematical procedures in higher education in specialized databases. In that sense, a realist synthesis will be employed to unveil the performance of artificial intelligence in the specific disciplinary field.

This scoping review aims to map the existing literature on the application of Artificial Intelligence (AI) in medical education. It explores how AI tools are being utilized to enhance teaching and learning processes, as well as their impact on educational outcomes and student performance. The study will identify current AI technologies used in educational environments, evaluate their effectiveness, and highlight ethical considerations and challenges related to their implementation. Additionally, the review will identify gaps in the literature, providing recommendations for future research in this evolving field.

This scoping review is aimed at examining and describing the existing research on artificial intelligence based interventions in the realm of graduate medical education.

This scoping review will examine how artificial intelligence has been integrated in to pharmacy education. It will examine curricula content, teaching method, students outcomes, and reported barriers or supports, using studies published from 2018 onward.Finding will highlight gaps and provide guidance for integrating AI in to future pharmacy training.

This scoping review aims to systematically map the current landscape of Artificial Intelligence (AI) in Graduate Medical Education, identifying key concepts, types of evidence, and gaps in the research.

The use of artificial intelligence (AI)-powered educational tools is growing over time and has the potential to completely transform the manner that education is provided. This paper looks at the pedagogical ramifications of artificial intelligence applications utilized in educational institutions. The study is qualitative research that analyzes an array of research on artificial intelligence-powered educational technologies using articles from peer-reviewed journals and conference proceedings. Content analysis is used to examine the literature to establish, the use of artificial intelligence in education, including its capabilities in educational processes, its pedagogical implications, and its challenges. The paper discusses how artificial intelligence could transform educational settings and educational resources, creating opportunities for services to be made scalable both inside and outside of the classroom. The paper concludes that while integrating artificial intelligence (AI) into education brings benefits to the education landscape, there are also significant risks. To fully utilize AI's technological innovation for educational purposes, ethical considerations must be taken into account.

The use of artificial intelligence (AI)-powered educational tools is growing over time and has the potential to completely transform the manner that education is provided. This paper looks at the pedagogical ramifications of artificial intelligence applications utilized in educational institutions. The study is qualitative research that analyzes an array of research on artificial intelligence-powered educational technologies using articles from peer-reviewed journals and conference proceedings. Content analysis is used to examine the literature to establish, the use of artificial intelligence in education, including its capabilities in educational processes, its pedagogical implications, and its challenges. The paper discusses how artificial intelligence could transform educational settings and educational resources, creating opportunities for services to be made scalable both inside and outside of the classroom. The paper concludes that while integrating artificial intelligence (AI) into education brings benefits to the education landscape, there are also significant risks. To fully utilize AI's technological innovation for educational purposes, ethical considerations must be taken into account.

Investigating The Impact Of Artificial Intelligence In Education Sector By Predicting Student Performance

Investigating The Impact Of Artificial Intelligence In Education Sector By Predicting Student Performance

Clear away the fog surrounding AI in education―and regain your peace of mind Among teachers, there is a cloud of rumors, confusion, and fear surrounding the rise of artificial intelligence.  AI and the Future of Education  is a timely response to this general state of panic, showing you that AI is a tool to leverage, not a threat to teaching and learning. By understanding what AI is, what it does, and how it can be used to enhance education, you can let go of anxiety and uncertainty, and learn to embrace artificial intelligence. It's true that, along with tremendous opportunities, AI presents some challenges for the field of education. In this book, Priten Shah, a Harvard M.Ed. with a robust background in educational innovation, helps you face these challenges head on, so you can gain the knowledge and skills you need to use AI effectively in your classroom. Thanks to this thorough consideration of ethical considerations and practical approaches, you can develop your own strategy for leveraging AI in administrative tasks, lesson design, professional development, and beyond. Understand what AI and machine learning are, and learn about new developments like ChatGPT Discover strategies for engaging students more fully using AI Automate administrative tasks, grading and feedback, and assessments Use AI in innovative ways to promote higher-order thinking skills Examine ethical considerations of AI, including the achievement gap, privacy concerns, and bias For K-12 educators, as well as leaders and policymakers who want to understand the role of technology in education,  AI and the Future of Education  is a valuable resource that can change AI from an unknown entity to an indispensable tool.

To conduct a scoping review of the application of artificial intelligence (AI) in nursing education to understand the current state of AI application in this field and to provide clues for future research.The application of artificial intelligence in the field of nursing education is still in its infancy, with limited practical guidance resources, and there are differences in research methods and assessment tools , which pose challenges for the development of artificial intelligence in the nursing field.This article guided by the scoping review report framework , organizes the forms, content, evaluation indicators, and effects of the application of artificial intelligence in nursing education, aiming to provide a reference basis for the effective implementation of artificial intelligence in the field of nursing education.

With this study we intend to give the starting point for the development of artificial intelligence in wound care education.

This article assesses the potential impact of Artificial Intelligence (AI) on K-12 bilingual education and provides an actionable guide for educators. This paper discusses how AI may address some key opportunities related to differentiated instruction of multilingual learners, differentiation of language acquisition, and creating resources for emergent bilingual learning layouts. It is organized around existing theories that pertain to bilingual learners' educational experiences, to include Krashen's Input Hypothesis and developmentally appropriate discussion of Vygotsky's Sociocultural Theory. The article describes specific AI tools that promote speaking, listening, reading, and writing (e.g., conversational AI, adaptive platforms, simplified text). The paper also critiques ethical concerns (e.g., bias, privacy, integrity) and practical implications (e.g., digital divide, teacher preparedness) of using AI in K-12 education. The author's recollection of your mentor teacher's observation that "students try to get through hours as quickly as possible" illustrates student behaviors that support the need for thoughtfully engaging, educational experiences for multilingual learners, given the availability of AI in education. In summary, the article argues for human-centered and intentional uses of AI. It offers best practices, suggestions for future inquiry to arrive at appropriate agility, and integrative research to create fair, meaningful, and deeply engaging educational labs for emergent bilingual learners.

This project is a systematic literature review of research on the challenges and opportunities of generative artificial intelligence integration in foreign language teaching in higher education.