Use and Application of ChatGPT-4o by Cataloguers and Bibliographers from Botswana, Nigeria, and South Africa

Introduction The integration of artificial intelligence (AI) tools into medical education presents new opportunities for enhancing students’ research skills and scientific writing. However, concerns remain about the potential for cognitive disengagement and the ethical use of AI when lacking appropriate educational supervision. This study aimed to evaluate a novel educational strategy combining structured AI assistance with mentor guidance to support narrative review writing among third-year medical students. Methods A structured framework was implemented during the endocrine module, involving AI-assisted objective formulation, mentor-guided objective refinement, literature search and summarization, review drafting followed by AI-assisted rephrasing. Students worked in groups, each supervised by a trained mentor. A validated questionnaire assessed student perceptions across four domains: framework and guidelines, AI-generated objectives, skills developed and mentor role, and overall satisfaction. Descriptive statistics were performed and chi-square tests evaluated associations between perceptions and AI tool usage (ChatGPT vs. DeepSeek). Results Eighty-seven students completed the survey. Perceived improvement in research readiness was observed; confidence in literature searching rose from 29.8% to 69%, while 75.8% reported increased familiarity with PubMed/Google Scholar. Most students (80.5%) expressed satisfaction with the AI mentor hybrid approach, and 82.8% agreed it prepared them for future research. There were no significant differences in perceived outcomes between AI tools used. Mentor involvement was deemed essential by 69% of students, and a minority believed AI alone could replicate the same outcomes. Common challenges included limited access to articles and peer collaboration difficulties, while key learning outcomes included improved summarization and ethical AI use. Discussion This study supports the integration of AI tools within a structured, mentor-guided educational framework to enhance critical evaluation and scientific writing in medical education. Human oversight and mentorship drive skill development and minimize the risk of unmoderated AI use in academic settings.

The integration of Artificial Intelligence (AI) in higher education has gained significant momentum, particularly in enhancing teaching methodologies and student learning outcomes. In higher education, AI enhances personalized learning, boosts student motivation, and improves academic achievement. In the context of Arts and Science colleges in Erode, there is a growing interest in adopting AI tools such as chatbots, virtual labs, and personalized learning platforms. However, the actual impact of these technologies on students’ academic performance and learning experiences remains underexplored. The integration of Artificial Intelligence in education faces several challenges in Arts and Science colleges in Erode. One major issue is the lack of awareness among students about the available AI tools and their potential academic benefits. Additionally, unequal access to AI resources across colleges hampers consistent usage. Students also exhibit varying levels of engagement with AI tools, which may affect their academic outcomes. Hence, the researchers developed a study for exploring the role of artificial intelligence in enhancing educational outcomes of college students in Erode, Tamilnadu. A descriptive research design is employed to align with the study’s objectives. Both primary and secondary data sources are used to gather information. The study targets students from Arts and Science colleges in Erode, Tamil Nadu, with a sample size of 185 selected through random sampling. The necessary data is collected using a structured questionnaire that covers students’ demographic profiles, AI tool usage, and their perceptions of AI’s role in enhancing educational outcomes, assessed through a 5-point Likert scale. The data are organized using MS Excel and analyzed using statistical techniques such as percentage analysis, mean scores, standard deviation, and ANOVA via SPSS 26.0 software. Additionally, null hypotheses are formulated to examine significant differences in the perceived role of AI across selected independent variables. This study mentioned from analysis that maximum level of experience with AI’s role in enhancing educational outcomes is perceived by the students belong to male, Science stream, studying second-year, using Virtual Labs (AI tool) and using AI tools frequently.

The integration of artificial intelligence (AI) tools in educational settings offers significant opportunities to promote sustainability by transforming learning experiences. This study analyses the usage, attitudes, and perceptions of AI tools among university students in Slovenia providing a comprehensive analysis that informs both academic practices and policy-making with emphasis on sustainability. We used a structured questionnaire with a sample of 422 participants reflecting a diverse demographic profile across various fields of study. The questionnaire was designed to measure the frequency of AI tool usage, the purposes for which these tools are employed, and students’ attitudes and perceptions towards AI’s potential benefits and drawbacks in education. Statistical analyses, including Analysis of Variance (ANOVA), were utilized to test hypotheses concerning differences in AI tool usage based on the level and field of study. Findings reveal that students recognize the efficiency of AI, but express concerns about its impact on learning quality and academic integrity, emphasizing the need for a balanced and responsible integration of AI in education to achieve sustainable outcomes. Results indicated that a majority of students are engaging with AI tools, with varied frequencies of use largely dependent on their field of study and academic level. The findings suggest that while AI tools are becoming an integral part of the educational landscape in Slovenia, there is a critical need to address the educational, ethical, and psychological impacts of these technologies. The results highlight the necessity for further research into the educational implications of AI, suggesting a balanced and sustainable approach to integrating these technologies into higher education curricula. Such an approach ensures that the adoption of AI not only enhances learning outcomes but also aligns with the principles of sustainability, promoting long-term benefits for both education and society.

The integration of artificial intelligence (AI) into the biological sciences marks a transformative era, reshaping research methodologies, data analysis, and hypothesis generation. This technological advancement accelerates discoveries and enhances our understanding of complex biological systems. As AI increasingly influences decision-making processes, the necessity for students and scientists to critically assess AI-generated outputs becomes paramount. The current narrative review explores the evolving role of critical thinking in biological sciences amidst the rise of AI, emphasizing the importance of skepticism, contextual understanding, and ethical considerations. It argues that while AI provides powerful tools for data interpretation and pattern recognition, human oversight and critical analysis remain indispensable to validate findings and prevent biases inherent in automated systems. Higher education institutions play a crucial role in fostering a culture of critical thinking, equipping biological scientists to effectively harness AI technologies while ensuring the integrity of their research and upholding scientific and ethical standards. Furthermore, AI tools, including chatbots, could be strategically employed in active learning methodologies, such as problem-based learning, flipped classrooms, and online learning. These methodologies enhance the ability of students to effectively utilize AI technologies while ensuring the rigor of scientific research. In conclusion, the current review underscores the benefits, challenges, and educational implications of AI integration, offering actionable insights for educators and learners seeking to adapt effectively to this rapidly evolving technological landscape.

This article examines the theoretical and practical implications of artificial intelligence (AI) integration in supply chain management (SCM). AI has developed dramatically in recent years, embodied by the newest generation of large language models (LLMs) that exhibit human‐like capabilities in various domains. However, SCM as a discipline seems unprepared for this potential revolution, as existing perspectives do not capture the potential for disruption offered by AI tools. Moreover, AI integration in SCM is not only a technical but also a social process, influenced by human sensemaking and interpretation of AI systems. This article offers a novel theoretical lens called the AI Integration (AII) framework, which considers two key dimensions: the level of AI integration across the supply chain and the role of AI in decision‐making. It also incorporates human meaning‐making as an overlaying factor that shapes AI integration and disruption dynamics. The article demonstrates that different ways of integrating AI will lead to different kinds of disruptions, both in theory and in practice. It also discusses the implications of AI integration for SCM theorizing and practice, highlighting the need for cross‐disciplinary collaboration and sociotechnical perspectives.

Nursing education is at the center of a rapidly transforming digital learning ecosystem, with the integration of artificial intelligence (AI), chatbots and simulation technologies. These innovations have the potential to strengthen clinical reasoning skills, increase student engagement, objectify assessment processes and support the retention of learning outcomes. However, effective and safe integration requires consideration of pedagogical alignment, ethical responsibilities, data security, algorithmic bias and human oversight. This study synthesises the extant evidence and proposes an integrated 'chatbot-centered integration model' for use in nursing education. The model synthesises Socratic questioning, gamified problem solving, simulation, virtual/augmented reality, and learning analytics within a unified framework, adopting a chatbot orchestrator role that facilitates pre-briefing, in-scenario counselling and debriefing stages. This synthesis is grounded in constructivism, situational learning, and pedagogical agents theory. The role of digital tools such as chatbots, simulations and artificial intelligence in nursing education is steadily increasing, as is their impact on students. These tools have numerous positive and negative impacts depending on the type and variety of use, and their effects are multidimensional.

This study investigates the influence of artificial intelligence (AI) integration on teachers’ professional identity and job satisfaction using a quantitative research design involving 251 respondents. Descriptive statistics showed relatively high levels of AI integration (M = 3.98, SD = 0.62) and professional identity (M = 4.12, SD = 0.58), indicating strong engagement with AI tools and a well-defined sense of professional role among teachers. Pearson correlation analysis revealed a moderately strong, statistically significant positive relationship between AI integration and professional identity (r = 0.612, p = 0.000), demonstrating that increased use of AI is associated with a strengthened professional identity. Mediation analysis further indicated that institutional factors significantly influence the relationship between AI integration and job satisfaction, with AI integration positively predicting institutional support (β = 0.54, p = 0.000) and institutional factors strongly predicting job satisfaction (β = 0.47, p = 0.000). Both a significant direct effect (β = 0.29, p = 0.001) and a strong indirect effect (β = 0.25, p = 0.000) were found, confirming partial mediation. These findings highlight that AI not only enhances teachers’ identity and satisfaction but that successful implementation relies heavily on institutional readiness and support. Overall, the results underscore the importance of adopting teacher-centered AI strategies that reinforce professional identity, reduce workload, and enhance well-being.

This study explores the impact of artificial intelligence (AI) integration on students' educational experiences. It investigates student perceptions of AI across various academic aspects, such as module outlines, learning outcomes, curriculum design, instructional activities, assessments, and feedback mechanisms. It evaluates the impact of AI on students' learning experiences, critical thinking, self-assessment, cognitive development, and academic integrity. This research used a structured survey distributed to 300 students through Microsoft Forms 365, yet the response rate was 29.67%. A structured survey and thematic analysis were employed to gather insights from 89 students. Thematic analysis is a qualitative method for identifying and analysing patterns or themes within data, providing insights into key ideas and trends. The limited response rate may be attributed to learners' cultural backgrounds, as not all students are interested in research or familiar with AI tools. The survey questions are about AI integration in different academic areas. Thematic analysis was used to identify patterns and themes within the data. Benefits such as enhanced critical thinking, timely feedback, and personalised learning experiences are prevalent. AI tools like Turnitin supported academic integrity, and platforms like ChatGPT and Grammarly were particularly valued for their utility in academic tasks. The study acknowledges limitations linked to the small sample size and a focus on undergraduate learners only. The findings suggest that AI can significantly improve educational experiences. AI provides tailored support and promotes ethical practices. This study recommends continued and expanded use of AI technologies in education while addressing potential implementation challenges.

The integration of artificial intelligence (AI) tools in the workflow of scholarly writing, including scientific and medical writing, offers transformative advantages while raising critical ethical and practical concerns. Among the pros, AI substantially enhances efficiency by automating several time-consuming tasks such as literature review, data synthesis and text editing. In particular, tools such as ChatGPT, Grammarly, and SciSpace Copilot remarkably empower researchers when they draft, refine, and format manuscripts, increasing precision and speed. In addition, AI tools may foster inclusivity by assisting non-native English speakers with seamless translations and also enabling interdisciplinary collaboration, thereby hopefully democratizing access and boosting scientific communication and cooperation. However, the rapid adoption of novel AI tools brings significant challenges. First, there is a distinct risk of perpetuating biases in training datasets, and other key issues include ambiguity in authorship accountability and the potential erosion of critical thinking skills. In addition, AI tools could be purposefully misused to generate mock datasets and fraudulent papers (e.g. by paper mills), and this clearly poses a threat to academic integrity. This challenge is all too pressing given that traditional plagiarism detection tools often fall short against sophisticated AI-generated content. We hereby explore the pros and cons of AI on medical writing, poignantly leveraging Sergio Leone’s The Good, The Bad, and The Ugly movie as a metaphor. By providing general concepts as well as focusing on several key AI tools, we are hopeful this overview may prove useful to anyone wishing to conscientiously adopt AI tools for scientific writing. In addition, we make a compelling case for transparent guidelines, robust and freely available detection mechanisms, and ongoing critical oversight to ensure AI will be able to serve as a catalyst for innovation and dissemination without compromising the credibility of scholarly endeavors.

The integration of Artificial Intelligence (AI) tools in education has the potential to transform learning experiences. However, the extent to which students find these AI tools useful may depend on the level of their AI Literacy (AIL). This study investigates the association between AI literacy level and the perceived usefulness of AI in the academe. A survey was conducted among 172 students from five colleges at Rizal Technological University – Boni Campus during the second semester of the academic year 2023-2024. The researchers collected data on their demographic profile, AI literacy level (AILL), and perceived usefulness (PU) of AI tools in the academe, which is then analyzed using statistical methods to identify significant differences and associations. The results indicated a positive association between AI literacy levels and students’ perceptions of AI usefulness in the academe. Additionally, a significant difference in AILL, particularly in AI ethics, was observed when respondents were grouped by year level. These findings suggest that promoting AI literacy can enhance students’ engagement with AI tools in educational contexts. The study offers valuable insights for educators and policymakers in designing AI-related curricula and resources. However, the researchers acknowledge that the sample size may have affected the normality of the data. Future research is recommended to develop a more comprehensive AI literacy questionnaire to assess students’ readiness for AI use and to include a larger sample size, potentially incorporating other universities in the Philippines.

Purpose: This study investigates the impact of artificial intelligence (AI) integration on organizational decision-making processes, with particular focus on how AI-driven systems transform traditional management approaches while balancing technological capabilities with human expertise. Design/methodology/approach: The research employs a mixed-methods approach combining systematic literature review with multiple case studies across diverse organizational contexts. The methodology incorporates quantitative analysis of implementation patterns from 50 organizations and qualitative assessment of managerial perspectives through semi-structured interviews with 30 senior executives. The study utilizes the PRISMA framework for literature review and grounded theory for thematic analysis of interview data. Findings: The analysis reveals three key findings: (1) organizations successfully integrating AI show a 35% improvement in decision-making speed and accuracy; (2) effective AI implementation requires a structured governance framework balancing automation with human oversight; and (3) organizational culture and leadership support are critical determinants of successful AI adoption, with companies having strong digital leadership showing twice the implementation success rate. Practical implications: The research provides managers with a practical framework for AI integration in decision-making processes, including specific guidelines for governance structure, risk assessment protocols, and change management strategies. Organizations can use the developed assessment tools to evaluate their AI readiness and identify specific areas requiring attention before implementation. Originality/value: This study presents the first comprehensive analysis of AI integration in organizational decision-making that combines both technological and human factors in a unified framework. The research contributes to both theory and practice by providing an evidence-based model for balanced AI implementation while introducing novel metrics for measuring AI–human collaboration effectiveness in management contexts.

The integration of artificial intelligence in interventional radiology is an emerging field with transformative potential, aiming to make a great contribution to the health domain. This overview of reviews seeks to identify prevailing themes, opportunities, challenges, and recommendations related to the process of integration. Utilizing a standardized checklist and quality control procedures, this review examines recent advancements in, and future implications of, this domain. In total, 27 review studies were selected through the systematic process. Based on the overview, the integration of artificial intelligence (AI) in interventional radiology (IR) presents significant opportunities to enhance precision, efficiency, and personalization of procedures. AI automates tasks like catheter manipulation and needle placement, improving accuracy and reducing variability. It also integrates multiple imaging modalities, optimizing treatment planning and outcomes. AI aids intra-procedural guidance with advanced needle tracking and real-time image fusion. Robotics and automation in IR are advancing, though full autonomy in AI-guided systems has not been achieved. Despite these advancements, the integration of AI in IR is complex, involving imaging systems, robotics, and other technologies. This complexity requires a comprehensive certification and integration process. The role of regulatory bodies, scientific societies, and clinicians is essential to address these challenges. Standardized guidelines, clinician education, and careful AI assessment are necessary for safe integration. The future of AI in IR depends on developing standardized guidelines for medical devices and AI applications. Collaboration between certifying bodies, scientific societies, and legislative entities, as seen in the EU AI Act, will be crucial to tackling AI-specific challenges. Focusing on transparency, data governance, human oversight, and post-market monitoring will ensure AI integration in IR proceeds with safeguards, benefiting patient outcomes and advancing the field.

This article overviews a study on integrating generative AI tools into education. English language education is critical for AI integration due to its reliance on text-based learning, communication skills and adaptive instructional approaches. Employing a systematic literature review to analyse existing research on AI tools in English language education, it focuses on their benefits and challenges. Articles from Scopus-indexed journals and other scholarly sources published between 2021 and 2024 were reviewed. The study discusses four significant findings: the classification of generative AI, key research topics on AI, the role of AI in cognitive offloading and academic dishonesty and the phenomenon of AI-generated hallucinations. The findings highlight the need for educators to stay updated with AI advancements and adapt their teaching practices accordingly. While AI offers opportunities for personalised learning, it also raises concerns about academic integrity and the reliability of AI-generated content. The review is limited to studies published between 2021 and 2024 and may not encompass all relevant research. Additionally, the focus is primarily on English language education, which may not fully represent the impact of AI tools across other educational contexts. Future research could explore the broader implications of AI integration across different subject areas and examine its long-term effects on pedagogy and student learning outcomes.

Disclosure: H.M. Heshmati: None. N. Zameni: None.Background: Robotic surgery is quickly replacing traditional surgery performed by humans. It has significantly influenced the field of endocrine surgery. This review presents an update on the use of robotic surgery and artificial intelligence for adrenal tumors. Methods: A systematic search of literature was conducted using the search terms robotic surgery, artificial intelligence, adrenal tumors, safety, outcome, and cost. Results: Adrenal tumors are found in 10% of people who have imaging of the adrenal gland. They are typically unilateral and benign in the majority of cases. Robotic systems such as da Vinci Surgical System provide surgeons with enhanced dexterity and 3D visualization. Autonomous surgical robots can be categorized into various levels of autonomy including supervised autonomy (e.g., performance of specific tasks with human oversight), collaborative autonomy (e.g., robots working alongside human surgeons, sharing tasks and responsibilities), and fully autonomous systems (e.g., robots performing entire surgical procedures independently with human intervention when necessary). Robotic surgery allows access through tight spaces to remove adrenal tumors with small incision and minimal impact on surrounding tissues. The review of 1,162 patients who underwent adrenalectomy (robotic surgery in 747 patients, laparoscopic adrenalectomy in 415 patients) showed comparable blood loss and complications with the two techniques but patients with robotic surgery had significantly shorter hospital stay. The future of robotic surgery can be revolutionized by the integration of autonomous robots and artificial intelligence (AI), a field rapidly advancing. These innovations promise to further enhance the precision, safety, and effectiveness of surgery involving the adrenal glands. AI-powered systems provide real-time guidance and feedback to surgeons, enhancing decision-making and surgical precision. Autonomous systems can ensure consistent execution of surgical tasks, reducing variability and enhancing patient safety. AI algorithms can analyze extensive surgical data to refine techniques and predict potential complications, improving surgical outcomes. AI can customize surgical plans based on individual patient anatomy and pathology, potentially leading to better outcomes. The current high cost of these new procedures is a potential limitation for their use. Conclusion: Robotic surgery represents significant progress in minimally invasive techniques for the removal of adrenal tumors. It has improved dexterity and precision, and is associated with shorter recovery time compared to laparoscopic adrenalectomy. Collaboration between human surgeons and robotic systems can improve patient care and surgical outcomes. The precision, safety, and effectiveness of the adrenal surgery can be further enhanced by the integration of AI and robotics.Presentation: Saturday, July 12, 2025

In recent years, the integration of Artificial Intelligence (AI) in education has shown transformative potential across various educational levels, from primary school to university. This paper examines the multifaceted applications of AI in education, highlighting its role in enhancing teaching and learning experiences, as well as personalizing educational content. The aim of this paper is to shed light on the implementation of various AI tools in primary, secondary and tertiary education through practical examples gained through action research. At the primary school level, AI tools are employed to create engaging and adaptive learning environments, catering to the diverse needs and learning paces of young students. In secondary education, AI facilitates the development of critical thinking and problem-solving skills through interactive and personalized learning platforms. At the university level, AI is revolutionizing research methodologies, providing sophisticated data analysis tools, and supporting the creation of innovative learning management systems. Additionally, the paper highlights the challenges and ethical considerations associated with AI integration in education, such as data privacy, algorithmic bias, and especially the need for teacher training in AI literacy. Conclusively, we provide an overview of the benefits and limitations of AI in education, offering insights into future trends and implications for educational stakeholders.