ARTIFICIAL INTELLIGENCE AS PRIMARY SCHOOL TEACHER ASSISTANT

In the dynamic field of programming education, integrating artificial intelligence (AI) tools has started to play a significant role in enhancing learning experiences. This paper presents a case study conducted during a foundational programming course for first-year students in higher education, where students were encouraged to utilize generative artificial intelligence programming copilot extensions in their programming IDE and browser-based generative AI tools as supportive AI tools. The primary objective was to observe the impact of AI on the learning curve and the overall educational experience.Key findings suggest that the introduction of AI tools significantly altered the learning experience for students. Many who initially struggled with grasping elementary programming concepts found that AI support made understanding basic programming concepts much easier, enhancing their confidence and skills. This was particularly evident in the reduced levels of anxiety typically associated with early programming learning, as the AI copilot provided a non-judgmental, always-available source for clarifying doubts, including queries that students might hesitate to ask in a traditional classroom setting.Notably, some students leveraged the AI to generate similar exercise problems, reinforcing their understanding and skills. The AI's capability to address basic queries also freed up the instructor's time, allowing for more personalized student guidance in more advanced problems. This shift in the instructional dynamic further contributed to a learning environment where students felt more comfortable engaging with complex topics, thereby reducing the psychological barriers often linked with early-stage programming education.The course's structure, enriched by AI, enabled students to delve into more complex programming constructs earlier than traditional curricula would allow. For instance, students were tasked with simulating basic e-commerce operations, such as user registration, product browsing, and cart functionalities. These practical challenges naturally introduced advanced concepts like external data storage, unit testing, and user interface design, which are typically reserved for more advanced courses. With the help of generative AI programming copilot tools, students at any programming skill level were able to develop nearly functional complex structures. Interestingly, even when their projects were not fully functional, students remained motivated. Instead of feeling discouraged by these imperfect outcomes, they showed resilience and a keen interest in understanding and improving their code. This reaction is a significant shift from traditional learning settings, where unfinished or flawed projects often lead to increased anxiety or a drop in motivation.Furthermore, the AI's proactive suggestions inspired students to explore beyond the curriculum. Advanced learners delved into databases, cryptography libraries in Python, and even more advanced user interface design, ensuring that they remained engaged and challenged. This elementary course, enhanced by generative AI tools, also inspired students to learn other programming languages since they now learned that individual learning is more available with the aid of generative AI.In conclusion, the integration of AI in programming education offers a promising avenue for enhancing both the learning experience and outcomes. This case study underscores the potential of AI to revolutionize traditional teaching methodologies, fostering a more dynamic, responsive, and inclusive learning environment.This paper handles the results, possibilities and challenges of AI empowered education in programming. It also gives practical examples as well as future research perspectives.

Ідея використання можливостей штучного інтелекту для потреб навчального процесу не нова для сучасної педагогіки, але її розвиток до початку пандемії COVID-19 був досить фрагментованим. Пандемія, самоізоляція та тривалий карантин змусили шукати нові можливості та форми для відтворення навчального процесу в цій ситуації. І тут на перший план вийшли можливості організації навчального процесу для всіх рівнів освіти з використанням штучного інтелекту. Мета цієї статті - проаналізувати форми використання можливостей штучного інтелекту для організації навчального процесу для учнів початкових класів, що активізувалися через пандемію COVID-19. Використання авторських методів особистого навчання (UPTM) під час навчання в школі завжди вважалося ознакою ідентичності та елітарності навчального процесу в конкретній країні. UPTM, з одного боку, більше відповідає потребам країни та суспільства у підготовці молодого покоління. З іншого боку - це можливість вузької конкретизації навчального процесу. У процесі написання статті були використані емпіричні та теоретичні методи дослідження, характерні для наукових досліджень такого роду. В результаті можна зазначити, що карантин, спричинений пандемією COVID-19, зіграв сприятливу роль у розширенні спектру можливостей використання штучного інтелекту в галузі освіти, навіть у тих сферах, які традиційно вважалися непридатними або неефективними для їх використання через особливості навчальних завдань. Статтю опубліковано в рамках дослідницької програми «Порівняльне дослідження професійного розвитку вчителів початкових класів у Китаї та Південній Кореї» (реєстраційний номер ZKNUC2017038)

Background: The rapid advancement of artificial intelligence (AI) in various sectors has revolutionized problem-solving approaches, particularly in healthcare. Developed countries have invested significantly in AI research and applications in healthcare, while low-income countries such as Somalia lag due to various challenges. This study aimed to assess the knowledge, attitudes, and practices (KAP) of AI among healthcare professionals in Somalia and explore their familiarity with AI technologies and practices. Methods: A cross-sectional study was conducted from January 1, 2024, to March 15, 2024, among 441 healthcare professionals in Somalia, using an online questionnaire. The questionnaire assessed the participants' sociodemographic information, knowledge of AI applications in healthcare, attitudes towards AI capabilities, and practical experience with AI in healthcare. Results: Most participants demonstrated good knowledge of AI (67.6%) and a positive attitude towards its potential in healthcare (80.5%). However, a significant gap was observed in the practical application of AI, with 79.1% of the respondents reporting poor practice. The study also found that sociodemographic factors such as age, gender, and income level did not significantly influence knowledge or attitudes towards AI but did affect its practical use. Professionals in certain fields such as midwifery and public health are more likely to use AI in their work. Knowledge and attitude scores were also significant predictors of practice scores. Conclusion: Healthcare professionals in Somalia demonstrate a good understanding and positive attitudes towards AI but encounter challenges in its practical application. This study emphasizes the necessity of an enhanced infrastructure, technical expertise, and data access to fully utilize AI's potential in healthcare. It also highlights the significance of addressing ethical considerations and implementing regulations to ensure responsible use of AI in healthcare. Efforts are needed to translate awareness and receptiveness into effective practice, which could result in a better healthcare system.

This paper discusses the integration of artificial intelligence (AI) tools in education, delineating their potential to transform pedagogical practices alongside the challenges they present. Generative AI models like ChatGPT, had a disruptive impact on teaching and learning, due to their ability to create text, images, and sound, revolutionizing educational content creation and modification. However, nowadays the educational community is polarized, with some embracing AI for its accessibility and efficiency thus advocating it as an indispensable tool, while others cautioning against risks to academic integrity and intellectual development. This document is designed to raise awareness about AI tools and provide some examples of how they can be used to improve education and learning. From an educator’s perspective, AI is an asset for curriculum development, course material preparation, instructional design and student assessment, while reducing bias and workload. For students, AI tools offer personalized learning experiences, timely feedback, and support in various academic activities. The Turkish Biochemical Society (TBS) Academy recommends educators to embrace and utilize AI tools to enhance educational processes, and engage in peer learning for better adaptation while maintaining a critical perspective on their utility and limitations. The transfer of AI knowledge and methods to the teaching experiences should complement and not replace the educator’s creativity and critical thinking. The paper advocates for an informed embrace of AI, AI fluency among educators and students, ethical application of AI in academic settings, and continuous engagement with the evolving AI technologies, ensuring that AI tools are used to augment critical thinking and contribute positively to education and society.

Purpose: This analysis aims to propose guidelines for artificial intelligence (AI) research ethics in scientific publications, intending to inform publishers and academic institutional policies in order to guide them toward a coherent and consistent approach to AI research ethics.Methods: A literature-based thematic analysis was conducted. The study reviewed the publication policies of the top 10 journal publishers addressing the use of AI in scholarly publications as of October 2024. Thematic analysis using Atlas.ti identified themes and subthemes across the documents, which were consolidated into proposed research ethics guidelines for using generative AI and AI-assisted tools in scholarly publications.Results: The analysis revealed inconsistencies among publishers’ policies on AI use in research and publications. AI-assisted tools for grammar and formatting are generally accepted, but positions vary regarding generative AI tools used in pre-writing and research methods. Key themes identified include author accountability, human oversight, recognized and unrecognized uses of AI tools, and the necessity for transparency in disclosing AI usage. All publishers agree that AI tools cannot be listed as authors. Concerns involve biases, quality and reliability issues, compliance with intellectual property rights, and limitations of AI detection tools.Conclusion: The article highlights the significant knowledge gap and inconsistencies in guidelines for AI use in scientific research. There is an urgent need for unified ethical standards, and guidelines are proposed for distinguishing between the accepted use of AI-assisted tools and the cautious use of generative AI tools.

Introduction The integration of artificial intelligence (AI) into higher education ushers in a new era of teaching and learning possibilities for both educators and students. Statement of the Problem These technologies not only offer unparalleled opportunities for personalized learning and instructional support, but also introduce challenges related to academic integrity. Literature Review The literature highlights AI's versatile applications in education, from facilitating research to acting as a tutor. Teaching Implications Educators can create AI-informed assignments that encourage active learning, critical thinking, and creativity. AI tools can be integrated as supports rather than substitutes for learning and foster an ethical framework within which students can explore AI's capabilities. Conclusion This review of AI's impact on education and the discussion of teaching strategies underscore the need for a nuanced approach to integrating AI in the classroom. By acknowledging both the challenges and opportunities presented by AI, educators can enhance teaching and learning while upholding academic standards.

The paper presents the results of the research study conducted on students at the Faculty of Humanities and Social Sciences in Zagreb (FHSS), Croatia, at the end of 2024, focusing on their experiences with artificial intelligence (AI) tools, and its use in academic activities, students' trust in AI, and the future use of AI by students. While over 81 % of respondents reported using AI tools for study purposes, more than 89% did not receive any education about AI and expressed a need for such education. Generative AI tools for text creation are the most frequently used type of AI tools, but students still require additional knowledge to use them effectively. AI tools are also utilized for seeking advice, finding information, being available 24/7, and assisting in writing student papers, though not yet on a massive scale. Teachers remain the most trusted source for information and for recommending AI tools. Despite known shortcomings of AI tools such as content errors, potential costs and ethical or privacy concerns, students expressed their intention to continue using AI tools in the future.

PurposeArtificial intelligence (AI) can assist in the worldwide shortage of cybersecurity workers in technical and managerial roles. Thus, the purpose of this study was to investigate the role of AI in automating many of the routine tasks associated with cybersecurity. As such, AI enables cybersecurity personnel to reduce their workloads and focus on more strategic aspects of their work.Design/methodology/approachThis study is an exploratory field study. The authors started by conducting a literature review to assess the possibility that AI tools can provide and how they can improve cybersecurity efficacy. Following this, the authors identified the specific core tasks for two cybersecurity work roles (technical and managerial) and searched for specific commercial tools that can perform each of the tasks. Then, the authors used the free ChatGPT 3.5 to list the current cybersecurity systems that use AI for the associated tasks, which the authors then reviewed with the tools’ documentation and websites to confirm these tasks were conducted or assisted by AI.FindingsResults indicated that all 14 cybersecurity tasks of the technical work role are currently noted to be performed by commercial cybersecurity systems with AI-integrated capabilities, while only 11 of the 17 managerial work role tasks currently appear to be performed by AI.Practical implicationsThe rapid integration of AI capabilities into commercial cybersecurity systems may suggest that the cybersecurity workforce must be currently trained on how to use AI tools in their daily operations, especially as it pertains to technical cybersecurity work roles.Social implicationsThe cybersecurity workforce shortage is reported to exceed four million cybersecurity workers worldwide in 2023. Thus, further understanding of the role of AI in improving the efficiency of technical and managerial cybersecurity tasks is significant.Originality/valueThe value of this research lies in the initial assessment of the current AI capabilities of commercial cybersecurity systems, which will ultimately provide the “super-human” performances resulting from human-AI teaming.

Will ChatGPT undermine ethical values in nursing education, research, and practice?

Background: Artificial intelligence (AI) tools have demonstrated success in US medical licensing examinations; however, their utility in infection prevention and control (IPC) remains unknown. Methods: The program of hospital epidemiology handles consultation calls and records each question and answer. Using 2022 data, we selected 31 frequently asked questions. We utilized four AI tools, including Chat GPT-3.5 and 4.0, Bing AI, and OpenEvidence, to generate answers. We predefined scales (Table 1) to capture responses by three reviewers, including two hospital epidemiologists and one infection preventionist. The mean score of ≥ 3 and ≥ 4 was considered acceptable in accuracy and completeness, respectively. We reported the percentage of responses with acceptable accuracy and completeness out of assessed questions for each category. Results: Among 31 questions, 16 were associated with isolation duration, 9 with healthcare personnel (HCP) exposure, 4 with cleaning contaminated rooms, and 2 with patient exposure. Regarding accuracy, most AI tools performed worse in questions about isolation duration, ranging between 75% and 93.8%. All AI tools, except OpenEvidence, had a 100% accuracy rate for HCP and patient exposure. All AI tools had a 100% accuracy rate for contaminated room handling. The highest overall acceptable accuracy rate was observed in Chat GPT-3.5. Regarding completeness, most AI tools performed worse in questions about isolation duration, ranging between 44% and 75%. All AI tools, except OpenEvidence, had a 100% completeness rate for contaminated rooms and patient exposure. The highest overall acceptable completeness rate was observed in Bing AI (Table 2). Conclusions: All AI tools provided reasonable answers to commonly asked IPC-related questions, although, there were variations among different tools used. AI could be used to supplement the infection control program, especially if resources are limited.

PurposeThis paper presents pyBibX, a Python library devised to conduct comprehensive bibliometric and scientometric analyses on raw data files sourced from Scopus, Web of Science and PubMed, seamlessly integrating state-of-the-art artificial intelligence (AI) capabilities into its core functionality.Design/methodology/approachThe library executes a comprehensive exploratory data analysis (EDA), presenting outcomes via visually appealing graphical illustrations. Network capabilities have been deftly integrated, encompassing citation, collaboration and similarity analysis. Furthermore, the library incorporates AI capabilities, including embedding vectors, topic modeling, text summarization and other general natural language processing tasks, employing models such as sentence-BERT, BerTopic, BERT, chatGPT and PEGASUS.FindingsAs a demonstration, we have analyzed 184 documents associated with “multiple-criteria decision analysis” published between 1984 and 2023. The EDA emphasized a growing fascination with decision-making and fuzzy logic methodologies. Next, network analysis further accentuated the significance of central authors and intra-continental collaboration, identifying Canada and China as crucial collaboration hubs. Finally, AI analysis distinguished two primary topics and chatGPT’s preeminence in text summarization. It also proved to be an indispensable instrument for interpreting results, as our library enables researchers to pose inquiries to chatGPT regarding bibliometric outcomes. Even so, data homogeneity remains a daunting challenge due to database inconsistencies.Originality/valuePyBibX is the first application integrating cutting-edge AI capabilities for analyzing scientific publications, enabling researchers to examine and interpret these outcomes more effectively. pyBibX is freely available at https://bit.ly/442wD5z.

Background Histological remission is an important target for Ulcerative Colitis (UC) treatment; however, scoring of histological images is time-consuming and prone to inter and intra-observer variability. Thus, a need exists for an accurate, reproducible, and reliable automated method. Previously, we demonstrated an Artificial Intelligence (AI) Tool using image processing and machine learning algorithms to measure histological disease activity using the Nancy index consistently and accurately.1 Here, we aim to enhance the capabilities of the AI Tool, by adding substantially more population-diversified training data while maintaining accuracy and robustness of results. Methods Eight global sites submitted 600 UC histological images. These were added to the 200 images previously used to train and validate the AI Tool. The 800-image dataset was divided into 2 groups: 90% used for training, 10% for testing. The novel AI algorithms were trained using state-of-the-art image processing and machine learning techniques based on deep learning and feature extraction. Cell and tissue regions of each training image were manually annotated, measured, and assigned a Nancy Index independently by 3 histopathologists, and used to further train the AI using over 43,000 characterisations. The AI Tool fully characterises histological images, identifying tissue types, cell types, cell numbers and locations, and automatically measures the Nancy Index for each image. Intra Class Correlation (ICC) and Confusion Matrix analyses were performed to evaluate the AI Tool and assess accuracy. Results The average ICC was 92.1% among the histopathologists and 91.1% between histopathologists and AI Tool, compared with 88.3% and 87.2% in the previous study.1 Confusion matrix analysis (Table 1) demonstrated the strongest correlation at the extremes of the Nancy Index, with 80% correlation between predicted and true labels for Nancy Scores of 0 or 4. When 2 adjacent scores were combined, correlations were stronger: 96% for a true Nancy score of 0 being predicted as 0 or 1, and 100% for a true Nancy score of 2 being predicted as 2 or 3. Conclusion By adding a larger number of images to the AI Tool training data, the robustness of the AI Tool was substantially improved while maintaining accuracy. The continued high correlation of AI Tool performance with the histopathologists reinforces the potential role for the AI Tool for IBD clinical applications. Fully characterising whole slides could standardise and validate an AI-driven scoring system for histology slides in IBD, eliminating the subjectivity of the human pathologist in assessment of disease activity.

This chapter explores the practical applications of artificial intelligence (AI) tools in enhancing business efficiency, demonstrating how AI-driven solutions can streamline operations, optimize decision-making, and foster innovation across various industries. It provides a structured guide to understanding AI-powered business applications, including machine learning algorithms, predictive analytics, automation tools, and AI-driven customer engagement systems. Through real-world case studies, the chapter highlights how businesses of all sizes—from small startups to large corporations—can leverage AI to reduce costs, improve productivity, and gain competitive advantages. The discussion includes scalable AI solutions, debunking the myth that AI is only accessible to large enterprises. The chapter also examines sector-specific AI applications, showcasing AI’s impact in finance, healthcare, marketing, retail, manufacturing, and logistics. Additionally, this chapter addresses the ethical and societal implications of AI adoption, including privacy concerns, algorithmic bias, workforce transformation, and regulatory considerations. It provides a practical roadmap for AI implementation, guiding business owners in identifying the right AI tools, overcoming challenges, and ensuring responsible AI integration. By the end of this chapter, readers will have a clear understanding of the AI landscape, including how to strategically implement AI tools to maximize business efficiency while maintaining ethical integrity.

The emergence of generative Artificial Intelligence (AI) presents both opportunities and challenges for higher education, requiring holistic approaches to developing students’ AI literacy. This interactive workshop addressed the critical need for a holistic integration of AI literacy at module and programme levels, supporting educators in developing comprehensive integration strategies. Drawing on the AI in teaching and learning framework (Zhou and Schofield, 2024), this session engaged Learning Developers and educators in practical approaches to embedding AI literacy across the curriculum. The framework encompassed four essential dimensions developed by Ng et al. (2023): know and understand, use and apply, create and evaluate, and AI ethics. The workshop provided participants with practical strategies through mapping learning outcomes to AI literacy dimensions, aligning teaching and learning activities with AI tools and embedding ethical considerations in AI-enhanced learning activities. Participants engaged in interactive activities to develop practical implementation plans suited to their practice/module. Drawing on two case studies (one at the module level and the other at the programme level) of the framework implementation, the session demonstrated how holistic AI integration enhances student AI literacy at a progressive level. During the workshop, participants engaged using Mentimeter to discuss linking learning outcomes, teaching activities, and AI tools, developing plans for adopting the frameworks within their own practice. This session contributed to the growing discourse on AI in higher education by providing practical, framework-based approaches to AI integration.

Purpose: Artificial Intelligence (AI) is rapidly transforming various sectors, including education, and significantly impacting students' academic experiences from school to college. This study explores how BAMS undergraduate students perceive the use of AI and AI-based tools for educational purposes and assess their prakriti. Method: This cross-sectional observational study was conducted among undergraduate BAMS students of Pt. Khushilal Sharma, Govt. Ayurveda College and Institute campus, Bhopal, Madhya Pradesh State, Central India, 200 undergraduate students aged 18 to 30. The collected data were analyzed using the software IBM SPSS Statistics Version 29.0.2.0(20). Data analysis used descriptive statistics (frequency, percentages, mean, standard deviation, etc.) as appropriate and inferential statistics (reliability, validity, and regression). Result: Among 200 undergraduate students, 33% were male and 67% were female. The mean age of students was 23.3±1.92(SD) years. 40% students belonged to Vatapittaja, 34% to Pittakaphaja, 22% to Kaphavataja, and 4% to Vatapitttakaphaja prakriti. The finding indicated that 78.5% of students were familiar with AI concepts. ChatGPT, Grammarly, and Google Assistant emerged as the most frequently used AI tools. Only 6.5% of students use AI tools daily for academic purposes. Most of the students were satisfied with using AI tools for study purposes. Conclusion: The surveyed students had little formal or informal experience with AI tools and limited awareness of AI's potential applications in education. However, they generally held positive opinions about ChatGPT, Grammarly, and AI and were hopeful about AI's future role in medical education and health care.