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Scoping Review Protocol

In laboratory courses, the assessment of exercises and assignments typically is treated as a simple, quantifiable approach. This approach however rarely includes qualitative factors, especially if the grading is being automatically performed by the system, and provides little to no feedback for the students to reflect on their work. The role of the laboratory in engineering education however is very critical and engineering students must possess knowledge that goes beyond mere theory, therefore a diverse and multidimensional assessment of laboratory students is a necessity. In this paper, an educational tool for laboratory students implementing automatic assessment based on artificial intelligence is being proposed, based on a pilot version that has been developed for MATLAB-related coursework. The highlights of the proposed tool is that it is based on a proven cognitive theory, it is easy to compile and or modify its contents, it is based on the same laboratory environment that the students are being trained in, it can include qualitative evaluation and it can save time that the educators would otherwise require to manually evaluate the exercises. [Partial funding for this research was provided by Greek national resources under the framework of the "Archimedes III: Funding of Research Groups in TEI of Athens" project of the "Education & Lifelong Learning" Operational Programme.] [For the complete proceedings, see ED557189.]

In laboratory courses, the assessment of exercises and assignments typically is treated as a simple, quantifiable approach. This approach however rarely includes qualitative factors, especially if the grading is being automatically performed by the system, and provides little to no feedback for the students to reflect on their work. The role of the laboratory in engineering education however is very critical and engineering students must possess knowledge that goes beyond mere theory, therefore a diverse and multidimensional assessment of laboratory students is a necessity. In this paper, an educational tool for laboratory students implementing automatic assessment based on artificial intelligence is being proposed, based on a pilot version that has been developed for MATLAB-related coursework. The highlights of the proposed tool is that it is based on a proven cognitive theory, it is easy to compile and or modify its contents, it is based on the same laboratory environment that the students are being trained in, it can include qualitative evaluation and it can save time that the educators would otherwise require to manually evaluate the exercises. [Partial funding for this research was provided by Greek national resources under the framework of the "Archimedes III: Funding of Research Groups in TEI of Athens" project of the "Education & Lifelong Learning" Operational Programme.] [For the complete proceedings, see ED557189.]

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

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.

Scoping review that aims to map and analyze the use of generative artificial intelligence in health profession education

This registration focuses on a systematic review of the impact of artificial intelligence on personalized learning in higher education. It examines how AI-driven technologies enhance student engagement, adapt learning pathways, and improve academic outcomes. The review synthesizes existing literature to identify key trends, benefits, and challenges associated with AI implementation in higher education settings. Findings aim to provide insights for educators, policymakers, and researchers on optimizing AI for personalized learning experiences.

The aim of this survey is to explore the perceived knowledge of osteopathic educators and researchers with AI, as well as its usage patterns in osteopathy education and research. This study also aims to examine attitudes towards integrating AI in education and research, identify concerns and perceived risks associated with the integration of AI in education and research, as well as recognize the perceived necessity of AI integration in education and research.

The contribution of artificial intelligence in nursing education

As the prevalence of artificial intelligence (AI) continues to grow, it is crucial to recognize the potential drawbacks associated with its advancement. The rise of AI brings with it a multitude of concerns that have profound implications for both individuals and society as a whole, highlighting the pressing need for ethical considerations in the realm of AI. It is essential to recognize that misuse of AI technology can exacerbate existing inequalities, while also acknowledging that inappropriately programmed algorithms can result in unfair discrimination against individuals, such as denying them access to vital services such as health insurance.

If you thought the recent pandemic was a major disruptor, society is on the cusp of a far more pervasive and ubiquitous shift in human existence. The rapid development and roll-out of Artificial Intelligence (AI) technologies, is looming as a quintessential disruptor of quantum proportions and will eventually permeate most, if not all, corners of our lives. This paper investigates the initial fall-out and potential impact of AI generative technologies on the higher education sector. When ChatGPT and similar software programs were released in late 2022, a knee-jerk reaction from some Government authorities, and institutions was to ban their use in education. Others recommended the development of academic policies and strategies to mitigate the risks to academic integrity and quality assurance. Pragmatic and adventurous educators embraced the opportunities that AI technologies offer to enhance the education sector and expand opportunities for life-long learning. Whilst there is general consensus that that higher education will need to undergo major reform to address the changes that AI will force on the future of learning and higher education institutions. The way forward is less clear. The proposition explored in this paper is that in order to filter, interpret, evaluate and apply AI generated content it might be helpful to consider the challenge through a research lens. The preliminary result is a Framework which focuses on the interface between artificial intelligence and human intelligence in the development and design of future-orientated curriculum, pedagogy, learning activities and assessments in higher education.

Artificial Intelligence refers to the simulation of human intelligence in machines that are capable of performing tasks that typically require human cognitive functions. In the context of education, AI can encompass a variety of tools and technologies, such as Machine learning (ML), Natural language processing (NLP), Computer vision and Intelligent tutoring systems. These AI-driven technologies can be applied across different facets of the teaching-learning process. AI tools are transforming the teaching-learning process across various educational levels, from elementary schools to technical universities. By providing personalized learning experiences, automating administrative functions and assisting with content creation, AI is helping both educators and students optimize the learning experience. As these tools continue to evolve, they will further enhance the educational landscape, making learning more interactive, efficient and tailored to individual needs. This paper explores the role and impact of AI in education, examining various AI tools and their applications in teaching, learning, assessment and administrative functions. Furthermore, the paper examines the challenges and future directions for integrating AI in education, including ethical considerations, the need for upskilling educators and ensuring equitable access to these advanced tools.

Artificial Intelligence refers to the simulation of human intelligence in machines that are capable of performing tasks that typically require human cognitive functions. In the context of education, AI can encompass a variety of tools and technologies, such as Machine learning (ML), Natural language processing (NLP), Computer vision and Intelligent tutoring systems. These AI-driven technologies can be applied across different facets of the teaching-learning process. AI tools are transforming the teaching-learning process across various educational levels, from elementary schools to technical universities. By providing personalized learning experiences, automating administrative functions and assisting with content creation, AI is helping both educators and students optimize the learning experience. As these tools continue to evolve, they will further enhance the educational landscape, making learning more interactive, efficient and tailored to individual needs. This paper explores the role and impact of AI in education, examining various AI tools and their applications in teaching, learning, assessment and administrative functions. Furthermore, the paper examines the challenges and future directions for integrating AI in education, including ethical considerations, the need for upskilling educators and ensuring equitable access to these advanced tools.

This paper seeks to understand approach and pattern of Artificial Intelligence (Al). It examines the contemporary approaches to Artificial Intelligence. Artificial Intelligence (Al)is a branch of Science which deals in development of modern machines, which can find solutions to complex problems in human. Artificial intelligence basically works on human intelligence characteristics and algorithms so that machine can behave like human.

Artificial Intelligence (AI) has witnessed significant advancements in recent years, with potential applications in various sectors, including education. Early childhood education (ECE) is a critical stage that lays the foundation for a child's future development and learning. This study aims to explore the attitudes and perceptions of educators in Ghana towards implementing AI in ECE settings, focusing on its long-term viability. The research employs a qualitative approach, utilising interviews and focus group discussions to gather data from educators in selected ECE settings across four (4) early childhood settings in Ghana. The study investigates eight (8) educators' experiences, concerns, and expectations regarding integrating AI technologies into their teaching practices. It explores the factors influencing their attitudes towards AI, including familiarity with the technology, pedagogical beliefs, cultural context, and training opportunities. Preliminary findings indicate a diverse range of perspectives among educators towards AI implementation in ECE settings. Some educators perceive AI as promising for enhancing teaching and learning experiences, providing personalised instruction, and facilitating early childhood development. They recognise the potential benefits of AI in supporting cognitive, social, and emotional growth among young learners. Concerns are expressed regarding the implications of AI on human interaction, child privacy, and the role of educators in fostering holistic development. This study contributes to the emerging field of AI in education by examining the context of ECE in Ghana. The findings informed policymakers, educational institutions, and AI developers about the perceptions and concerns of educators, ultimately guiding the development and implementation of AI technologies in a manner that aligns with the needs and aspirations of the Ghanaian ECE community.

This review contains an overview of past and present trends in the application of what is called "artificial intelligence" in traditional face-to-face education and in distance education. The reviewed trends are illustrated with examples of research projects and results throughout the world. The first section of the review discusses intelligence in general, describing classic and modern theories of intelligence. Definitions of artificial intelligence in education are presented, with attention to these areas: (1) intelligent tutoring systems; (2) interactive learning environments; (3) virtual environments for education; (4) computer-mediated communication; (5) intelligent agents for education; (6) the World Wide Web and education; and (7) intelligent interfaces for learning support systems. Perspectives for the future of artificial intelligence in education are discussed. Nine appendixes contain a discussion of the Internet, a list of Web search engines, and other supplemental information related to artificial intelligence applications. (Contains 17 figures and 3 tables.) (SLD)

This quantitative study examined how university students perceive and use generative AI technology, specifically ChatGPT, in higher education. The main objective is to explore and describe university students' perception and use of generative AI, particularly ChatGPT, in the context of higher education. A 5-point Likert scale questionnaire was adopted and used in the study. Five hundred students from various universities in Region XI were surveyed using a stratified random sampling technique. Further, the validity and reliability of the scale used in the study were assessed using Cronbach's Alpha and Factor Analysis and demonstrated acceptable construct validity and reliability indices. The findings revealed that university students demonstrated high levels of understanding, knowledge, perception of the advantages and disadvantages, positive attitude, and strong intention to use generative AI technologies in the context of higher education. Moreover, the respondents showed a moderate level of concern about generative AI.

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.

The advent of artificial intelligence (AI) has revolutionized numerous sectors, with radiology being one of the most impacted fields. As AI continues to evolve, its integration into radiology education has become a pivotal area of interest. This scoping review aims to explore the current landscape of AI applications in radiology education, identifying key trends, benefits, and challenges. By examining existing literature and educational practices, this review seeks to provide a comprehensive overview of how AI is shaping the training and development of future radiologists, enhancing diagnostic accuracy, and fostering innovative learning methodologies.

Rapid advancements in Artificial Intelligence (AI) are being made to support digital healthcare transformation and provide evidence-based care. Implementing a reform in medical Education requires students’ awareness regarding the importance of artificial intelligence (AI) in modern medical practice. This study aimed to systematically analyze student perception in medical Education in clinical practice and theoretical courses.

In recent years, the rapid development of Generative Artificial Intelligence (GenAI) is reshaping the practice model of various industries around the world. Large Language Models (LLMs), represented by ChatGPT, are capable of generating human-like text, images, and even multimodal content through natural language processing and deep learning technologies, and have shown great potential in the fields of healthcare, education, and scientific research. In nursing education, the application of GenAI has gradually become a focus of innovation, and GenAI's unique advantage lies in its high scalability and interactivity, which research has shown that GenAI can effectively make up for the shortcomings of traditional teaching through virtual patient simulation, personalized learning path design, and real-time feedback system.This review systematically comprehends the current status, advantages, and challenges of GenAI in nursing education, fills the gap of comprehensive research in this field, provides an empirical reference for educators, and lays the foundation for policy makers to optimize AI integration strategies, and helps nursing education to achieve sustainable development amidst the wave of digitization.

The COVID 19 epidemic has undoubtedly caused an increase in the use of online learning, leading institutions and instructors to investigate cutting edge technology to improve the educational process. This study explores how, in the post COVID 19 era, social media and digital marketing might be strategically integrated to strategically extend online education and leverage artificial intelligence AI . This study uses artificial intelligence AI to investigate how social media and digital marketing might be used to expand and improve online learning, providing insights into the prospects and difficulties in this field.The study is to investigate how artificial intelligence AI technologies, such as natural language processing and machine learning algorithms, can be used to improve material delivery based on individual student needs, enable adaptive learning paths and customize online learning experiences. The goal of the project is to investigate how artificial intelligence AI technologies, such as machine learning algorithms and natural language processing, might be applied to improve the delivery of content that is customized to each students needs. Furthermore, the study looks into how AI might offer personalized online learning environments and adaptive learning pathways. The study also looks at how AI powered analytics affe|ct student performance monitoring and assessment, giving teachers insightful information for ongoing development.The importance of social media in education cannot be overstated, as sites like Facebook, LinkedIn and Twitter enable learners to interact, collaborate in learning environments and share knowledge. The study looks into how user behavior analysis, preference prediction and content recommendation powered by AI might improve the effectiveness of social learning.Google Meet, Zoom, and Jio Meet played a significant role in expanding online education during the COVID 19 pandemic. These platforms provided essential tools for educators and students to connect, collaborate, and engage in remote learning.Additionally, the study investigates how digital marketing tactics might be used to advance online learning, draw in a larger audience, and improve participation. To maximize the impact of educational marketing initiatives, AI driven marketing automation solutions being investigated for their potential in personalized outreach, targeted advertising and data driven decision making. The potential of these technologies to apply targeted advertising, make data driven decisions, and customize outreach is being researched.The purpose of this studys conclusions is to shed light on how AI, social media, and digital marketing work together to support online learning in the post COVID 19 era. The research intends to offer practical suggestions and tactics for using AI, social media and digital marketing in the educational setting by comprehending how these technologies might cooperate to enhance online learning. In the end, the research advances our knowledge of how these tools might work together to revolutionize and broaden the field of online education, creating new opportunities for engaging, customized, and accessible learning. Kritika Srivastava | Prof. Himanshu Shekhar Singh "Artificial Intelligence: A Boon in Expanding Online Education through Social Media and Digital Marketing Post Covid-19" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63449.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/63449/artificial-intelligence-a-boon-in-expanding-online-education-through-social-media-and-digital-marketing-post-covid19/kritika-srivastava

This report begins by summarizing current applications of ideas from artificial intelligence (Al) to education. It then uses that summary to project various future applications of Al--and advanced technology in general--to education, as well as highlighting problems that will confront the wide­ scale implementation of these technologies in the classroom.

This report begins by summarizing current applications of ideas from artificial intelligence (Al) to education. It then uses that summary to project various future applications of Al--and advanced technology in general--to education, as well as highlighting problems that will confront the wide­ scale implementation of these technologies in the classroom.

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 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.

The field of Artificial Intelligence (AI) and Education has traditionally a technology-based focus, looking at the ways in which AI can be used in building intelligent educational software. In addition AI can also provide an excellent methodology for learning and reasoning from the human experiences. This paper presents the potential role of AI in the various aspects of education. Six AI fields are presented. The first presents the knowledge representation (KR) which includes ontologies; new concepts for representing, storing and accessing knowledge; and schemes for representing knowledge. The second is related to the use of Case-Based Reasoning (CBR) methodology in developing interactive intelligent educational systems for learning and teaching. The third topic is related to the use of Natural Language Processing (NLP) for analyzing the educational Web pages. The fourth is concerned with the Intelligent Tutoring Systems (ITSs), which are capable of adaptive instruction by means of multiple representations of domain knowledge. The fifth is the Intelligent Tutoring System Authoring Sheels (ITSASs), which allow a course instructor to easily enter domain and other knowledge without requiring computer programming skills. The last area deals with the learning in Distributed Artificial Intelligence (DAI). Moreover, the paper will explore a proposal for a master's degree in artificial intelligence in education. (Author)

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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).

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.

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.

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

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.

Artificial Intelligence (AI) is one of the disruptive technologies which is being used to customize the experiences of various learning groups, instructors, and tutors. It is considered the most in-demand technology in today's education system. It is expected that AI will enhance the education system worldwide. The educational institutions are nowadays facing various challenges like high dropout rates, unavailability of smart content, lack of customized content as per the textbooks, lack of personalized learning systems, rigid examination patterns etc. The use of AI brings hope to overcome these challenges. It helps to create personalized learning experiences, develop smart content, expand the range of education, and facilitate the management & administration of education by integrating information and disseminating data as per the needs of the target group. With AI, teachers/instructors can identify the learning styles and hard points of students and provide new avenues of teaching and support to students. The current study discusses about AI in various areas of education and its application in Indian education. AI has the potential for enabling students to achieve their goals and streamline the process of education. It can analyse the students' previous learning history, identify their weaknesses, and improve the upcoming learning opportunities for personalized learning experiences. AI can be used effectively for transcription of faculty lectures in local languages which can thus become a good educational resource to students of technical education. The AI tools and programs are inclusive which makes the classrooms accessible to all irrespective of language or any other disabilities.

 The article is devoted to the problem of using artificial intelligence in the activities of after-school educational institutions of tourism and local studies. The possible directions of using artificial intelligence specifically in the educational process of study groups, in the organizational and managerial activities of after-school educational institutions, in the organization and arrangement of mass events are identified. Specific forms of using artificial intelligence in after-school tourism and local studies education institutions are highlighted to ensure the safety of events, prepare tourist trips, hikes, excursions, and organize the work of tourist route and qualification commissions. Based on a educators' survey of after-school education institutions of Ukraine using Google forms, the level of use of information and communication technologies and artificial intelligence by pedagogical staff of after-school education institutions of tourism and local studies is determined. The level of understanding by teachers of the concept of “artificial intelligence” and ways of its use in educational activities in after-school education is determined. The technologies of artificial intelligence used by educators, the directions of using artificial intelligence (search for necessary information, creation of text, graphic and test educational materials, creation of news and advertising materials, monitoring of the security situation) are determined. The organizational, methodological and technical problems that, in the opinion of educators, hinder the use of artificial intelligence technologies, ways for educators to acquire competencies in the use of artificial intelligence technologies are highlighted. It has been established that the main source of educators' acquisition of competencies in artificial intelligence is self-education.

 The article is devoted to the problem of using artificial intelligence in the activities of after-school educational institutions of tourism and local studies. The possible directions of using artificial intelligence specifically in the educational process of study groups, in the organizational and managerial activities of after-school educational institutions, in the organization and arrangement of mass events are identified. Specific forms of using artificial intelligence in after-school tourism and local studies education institutions are highlighted to ensure the safety of events, prepare tourist trips, hikes, excursions, and organize the work of tourist route and qualification commissions. Based on a educators' survey of after-school education institutions of Ukraine using Google forms, the level of use of information and communication technologies and artificial intelligence by pedagogical staff of after-school education institutions of tourism and local studies is determined. The level of understanding by teachers of the concept of “artificial intelligence” and ways of its use in educational activities in after-school education is determined. The technologies of artificial intelligence used by educators, the directions of using artificial intelligence (search for necessary information, creation of text, graphic and test educational materials, creation of news and advertising materials, monitoring of the security situation) are determined. The organizational, methodological and technical problems that, in the opinion of educators, hinder the use of artificial intelligence technologies, ways for educators to acquire competencies in the use of artificial intelligence technologies are highlighted. It has been established that the main source of educators' acquisition of competencies in artificial intelligence is self-education.

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Scoping review Protocol

Background: Artificial intelligence (AI) has the potential to revolutionise paramedic education. As well as allowing for personalised learning experiences tailored to individual needs and learning styles, it can provide simulations, intelligent tutoring systems, automated grading and assessment, and predictive analytics. Objective: To investigate the role of artificial intelligence in transforming the landscape of paramedic education and evaluate its potential to improve learning outcomes. Methods: This review presented the role of AI in paramedic education and its perspective over the past twenty years. It included high-quality data and comprehensive investigations of articles available in renowned databases. Results: AI-based training and simulation technologies, such as virtual patients, surgical simulators, and intelligent tutoring systems,are increasingly being used in paramedic education. Virtual patients use computer-generated avatars to display symptoms and react to therapies, while surgical simulators use accurate anatomical models and haptic feedback devices to simulate surgical operations. Conclusion: AI has the potential to fundamentally alter how students learn, the kind of education they receive, and the efficiency with which healthcare is delivered. It can create immersive training environments, analyse medical data, and help students feel more competent, confident, and capable. This potential can be harnessed to enhance paramedic education and improve patient care outcomes.

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

The current study was conducted to determine the attitudes of preservice preschool teachers toward artificial intelligence and to identify their views on AI and its application in education. This study was conducted using a sequential mixed methods design. Initially, the attitudes of 145 preservice preschool teachers toward artificial intelligence were determined using the General Attitude Toward Artificial Intelligence Scale. After evaluating the results according to their educational levels, focus group interviews were conducted with at least one participant from each level, including the five highest-scoring and five lowest-scoring individuals, to ascertain their views on AI and its use in early childhood education. At the conclusion of the study, the overall average score of the preservice teachers was determined to be 66.71, which is slightly above the general average. When examined by educational level, it was found that the group with the highest average score was the seniors, while the group with the lowest average score was the freshmen. Participants who scored high on the scale suggested that AI can be beneficial in various areas, including finding activity ideas, preparing and accessing educational materials, attaining otherwise inaccessible products and facilities, asking the right questions in education, and employing observation and other evaluation methods. Conversely, low-score participants indicated that AI could be used primarily for finding activity ideas, accessing educational materials, and parent education. It was found that the responses of the high-scoring participants were more diverse, and those with low scores had limited knowledge of AI. [This paper was published in: "EJER Congress 2024 International Eurasian Educational Research Congress Conference Proceedings," edited by Senel Poyrazli, Ani Publishing, 2024, pp. 99-104.]

The current study was conducted to determine the attitudes of preservice preschool teachers toward artificial intelligence and to identify their views on AI and its application in education. This study was conducted using a sequential mixed methods design. Initially, the attitudes of 145 preservice preschool teachers toward artificial intelligence were determined using the General Attitude Toward Artificial Intelligence Scale. After evaluating the results according to their educational levels, focus group interviews were conducted with at least one participant from each level, including the five highest-scoring and five lowest-scoring individuals, to ascertain their views on AI and its use in early childhood education. At the conclusion of the study, the overall average score of the preservice teachers was determined to be 66.71, which is slightly above the general average. When examined by educational level, it was found that the group with the highest average score was the seniors, while the group with the lowest average score was the freshmen. Participants who scored high on the scale suggested that AI can be beneficial in various areas, including finding activity ideas, preparing and accessing educational materials, attaining otherwise inaccessible products and facilities, asking the right questions in education, and employing observation and other evaluation methods. Conversely, low-score participants indicated that AI could be used primarily for finding activity ideas, accessing educational materials, and parent education. It was found that the responses of the high-scoring participants were more diverse, and those with low scores had limited knowledge of AI. [This paper was published in: "EJER Congress 2024 International Eurasian Educational Research Congress Conference Proceedings," edited by Senel Poyrazli, Ani Publishing, 2024, pp. 99-104.]

The current study was conducted to determine the attitudes of preservice preschool teachers toward artificial intelligence and to identify their views on AI and its application in education. This study was conducted using a sequential mixed methods design. Initially, the attitudes of 145 preservice preschool teachers toward artificial intelligence were determined using the General Attitude Toward Artificial Intelligence Scale. After evaluating the results according to their educational levels, focus group interviews were conducted with at least one participant from each level, including the five highest-scoring and five lowest-scoring individuals, to ascertain their views on AI and its use in early childhood education. At the conclusion of the study, the overall average score of the preservice teachers was determined to be 66.71, which is slightly above the general average. When examined by educational level, it was found that the group with the highest average score was the seniors, while the group with the lowest average score was the freshmen. Participants who scored high on the scale suggested that AI can be beneficial in various areas, including finding activity ideas, preparing and accessing educational materials, attaining otherwise inaccessible products and facilities, asking the right questions in education, and employing observation and other evaluation methods. Conversely, low-score participants indicated that AI could be used primarily for finding activity ideas, accessing educational materials, and parent education. It was found that the responses of the high-scoring participants were more diverse, and those with low scores had limited knowledge of AI. [This paper was published in: "EJER Congress 2024 International Eurasian Educational Research Congress Conference Proceedings," edited by Senel Poyrazli, Ani Publishing, 2024, pp. 99-104.]

Long Island Youth Summit Best Research Paper

The rising integration of artificial intelligence into the process of creating artwork and its deployment in various professional tasks signifies a notable change in how technology is utilized across different domains. AI is rapidly becoming favoured among students to address various learning challenges. A thoughtful selection of how AI is applied in creative assignments can enhance student creativity and improve outcomes when producing diverse visual content. This article reports on an experiment aimed at generating art products using various AI applications, resulting in image creations based on verbal descriptions. The objective of the educational experiment is to motivate students to create artistic works by utilizing AI, guided by descriptive words, and improving their projects by applying them to different AI software. The research employs visual analysis and comparative analysis to assess the educational artworks produced by students, contrasting those stemming from their imagination with those generated through AI based on their descriptions. Survey Analysis of Using AI in Visual Arts Education. Findings reveal that student satisfaction levels correlate with their creativity; more creative students tend to be less satisfied with the outcomes. There are significant distinctions in the artistic and aesthetic effects of images relating to lines, shapes, and techniques. In conclusion, AI-generated images can greatly enrich the creative process by providing artists with new visual interpretations of their concepts. While AI can effectively produce new images in response to artists’ creative briefs, it does not replace human creativity in the artistic process. Instead, the incorporation of AI tools serves as a complementary resource, enhancing the artistic ecosystem in the creative process.

Artificial intelligence (AI) has revolutionized various fields, including research and higher education. Thanks to its innovative applications, it has changed traditional teaching methods. This article aims to explore the impact of AI on these domains in Moroccan universities, focusing on its transformative influence, benefits, challenges, and future prospects. By analyzing current literature, case studies, and expert opinions, we elucidate how AI has enhanced research methodologies, empowered educators and students, and fostered innovation in academia. In addition, we discuss ethical considerations and potential concerns associated with the increasing integration of AI. Finally, we highlight the future prospects and opportunities offered by AI for research and higher education in Morocco.

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.