Ethical Guidelines Research Articles

Ethics Concerns in the Use of Computer-Generated Images for Human Communication

The rapid growth of artificial intelligence (AI) tools has contributed to various image-generation techniques. These tools have had a significant effect on how we perceive contemporary communication. This study presents a broad overview of the challenges and consequences associated with the influence of AI on communication, and its findings are supported by thoughts collected from various books and academic publications. Nevertheless, incorporating AI into communication through synthetic imagery raises concerns about communication that require careful consideration. This study investigates various ethical concerns and the significance of ethical guidelines and responsible practices in creating these applications. It intricately weaves together philosophical reflections, ethical deliberations, and societal responsibilities, shedding light on the complex interplay between ethics, technology, and human values within the context of machine development and broader societal realms.

Integration of Artificial Intelligence into Sustainable Development Goals in India

In the pursuit of sustainable development, the fusion of artificial intelligence (AI) with legal mechanisms emerges as a potent force for transformative change. This paper delves into the intricate interplay between AI technologies and the legal framework, elucidating their collective capacity to address multifaceted challenges and propel progress towards the Sustainable Development Goals (SDGs). At the forefront of environmental stewardship, AI-driven solutions offer unprecedented capabilities in pollution monitoring, waste management, and climate change adaptation. From predictive analytics for air quality assessment to real-time monitoring of deforestation patterns, AI empowers stakeholders with actionable insights to mitigate environmental degradation and foster resilience. Simultaneously, AI applications hold promise in addressing socio-economic inequities through innovative approaches to poverty alleviation, financial inclusion, and inclusive growth strategies. By leveraging predictive modelling and data analytics, AI facilitates targeted interventions tailored to the unique needs of marginalized communities, thereby fostering sustainable development trajectories. However, the ethical and regulatory dimensions of AI deployment necessitate careful consideration within the legal framework. Ensuring data privacy, algorithmic transparency, and equitable access emerges as imperatives in harnessing the full potential of AI for societal benefit. Through the formulation of robust regulatory frameworks and ethical guidelines, the legal system plays a pivotal role in safeguarding against potential risks and maximizing the societal dividends of AI innovation. Moreover, interdisciplinary collaboration and stakeholder engagement are paramount in navigating the evolving landscape of AI governance, fostering dialogue, and consensus-building among diverse stakeholders. Thus, this paper advocates for a holistic approach to sustainable development, synergizing the transformative potential of AI with the protective mechanisms of the legal system. By fostering innovation, inclusivity, and accountability, this symbiotic relationship serves as a catalyst for advancing the global agenda towards a more equitable, resilient, and sustainable future. Keywords: Artificial Intelligence, Climate Change Adaptation, Deforestation, Education, Healthcare, Legal System, Pollution, Poverty, Sustainable Development Goals, Water and Waste Management.

“We do others’ Jobs”: a qualitative study of non-nursing task challenges and proposed solutions

IntroductionNon-nursing tasks (NNTs) have become a prevalent issue among healthcare professionals, affecting nurses globally. This study delves into the experiences of Jordanian nurses regarding NNTs, aiming to uncover challenges and propose solutions within the Jordanian healthcare context.ObjectiveExplore the impact of NNTs on Jordanian nurses’ roles, workload, and satisfaction. Additionally, the study aims to identify various types of NNTs performed by nurses, understand their impact, and propose solutions to mitigate challenges associated with these tasks.MethodsA qualitative-exploratory research design was employed for this study. Semi-structured interviews were conducted with Jordanian nurses using a purposeful sampling approach to ensure a diverse representation of experiences and perspectives. Thematic analysis was used to identify recurring themes and patterns related to NNTs, their challenges, and potential solutions. Ethical guidelines were strictly followed to maintain participant confidentiality and ensure the integrity of the data collected.ResultsAnalysis of the interviews revealed four major themes: challenges of NNTs, types of NNTs, impact of NNTs, and proposed solutions. Nurses faced significant difficulties due to task ambiguity, role confusion, and increased workload from NNTs, which included administrative duties, clerical work, and tasks typically performed by other healthcare professionals. These NNTs negatively impacted nurses’ effectiveness, productivity, and job satisfaction by diverting time and energy from primary nursing responsibilities, causing professional strain. To address these issues, participants suggested clearer job descriptions, stricter task assignment protocols, and systemic changes to tackle the root causes of NNTs.ConclusionThis study sheds light on the pervasive challenges posed by NNTs among Jordanian nurses and emphasizes the importance of addressing these issues to enhance nursing care quality and nurse well-being. By proposing actionable solutions tailored to the Jordanian context, this research contributes to the global discourse on NNTs and underscores the need for organizational support and advocacy to optimize nurses’ roles and improve patient care outcomes.

Development and validation of the Environmental Confinement Stressors Scale (ECSS-20).

The COVID-19 pandemic has generated a global crisis with severe consequences for public health. There have been negative impacts on people's quality of life and mental health due to various stressors arising in this context, such as physical, social, economic, and psychological challenges. Noteworthy among these are the indirect effects of health measures, especially social distancing and confinement, which have significantly altered people's daily lives and social activities, producing high levels of anxiety, depression, and stress. This study proposes developing and validating a cross-sectional scale called the "Environmental Stressors Scale (ECSS-20)" to address the need to measure the impact of environmental stressors during confinement. The scale, which has been validated following ethical and methodological guidelines, consists of four dimensions: economic stressors (EE), social activities (SA), habitability (H), and exposure to virtual media (EMV). A pilot study (n = 113) and a main study (n = 314) were applied. The results showed that the instrument has a reliable and valid structure, with satisfactory internal consistency and factorial validity. Likewise, gender invariance tests supported its suitability for its applicability to women and men. Overall, the ECSS-20 is a valuable instrument for assessing the impact of confinement and improving the understanding of people's subjective experiences in this situation. Future research could further develop its applicability in different contexts and populations to better understand its usefulness and psychometric properties.

Exploring the impact of artificial intelligence on higher education: The dynamics of ethical, social, and educational implications

The increasing prevalence of Artificial Intelligence (AI) in higher education underscores the necessity to explore its implications on ethical, social, and educational dynamics within the sector. This study aims to comprehensively investigate the impact of AI on higher education in Saudi Arabia, delving into stakeholders’ attitudes, perceptions, and expectations regarding its implementation. The research hones in on key facets of AI in higher education, encompassing its influence on teaching and learning, ethical and social implications, and the anticipated role of AI in the future. Employing a quantitative approach through an online survey questionnaire (N = 1113), this study reveals positive attitudes toward AI in higher education. Stakeholders recognize its potential to enhance teaching and learning, streamline administration, and foster innovation. Emphasis is placed on ethical considerations and guidelines for AI implementation, highlighting the imperative need to address issues such as privacy, security, and bias. Participants envision a future characterized by personalized learning experiences, ethically integrated AI, collaboration, and ongoing support for lifelong learning. Furthermore, the results illuminate the intricate interplay between AI usage, purposes, difficulties, and their impact on attitudes, perceptions, and future implications. Accordingly, the research underscores the necessity for a comprehensive understanding of AI integration, considering not only its technical aspects but also the ethical, social, and educational dimensions. By acknowledging the role of AI uses, AI usage purposes, and addressing associated difficulties, educational stakeholders can work towards harnessing the benefits of AI while ensuring responsible and effective implementation in teaching and learning contexts.

ВЕЛИКІ МОВНІ МОДЕЛІ ШТУЧНОГО ІНТЕЛЕКТУ В МЕДИЦИНІ

This study conducts a thorough analysis of the current state and prospects of artificial intelligence (AI) technologies, particularly focusing on large language models (LLMs) and their implementation in healthcare. In an age of rapid digital technology advancement, this research is crucial for understanding the potential influences of AI on medical practices and research. A diverse range of methods, including analysis and synthesis, comparison, generalization, induction and deduction, systematization, as well as the phenomenological method, were employed for a comprehensive analysis. These techniques enabled not only an in-depth examination of the technical aspects of AI application but also facilitated an evaluation of its prospective impact on the field of medicine. The paper highlights the essential role of integrating large language models into the medical field. These models are not only capable of substantially enhancing the efficiency of processing vast medical data but also play a fundamental role in refining diagnostic procedures and reforming clinical decision-making processes. Furthermore, the article examines potential challenges associated with the use of AI in healthcare, particularly focusing on concerns regarding transparency, privacy, bias, and accountability These issues demand meticulous attention and effective solutions to ensure the successful integration of AI into medical practices. The research includes a complex, interdisciplinary approach surrounding the field of medicine, informatics, ethics, and law, underscoring the synergy among these diverse knowledge domains for the effective understanding and utilization of AI in healthcare. Moreover, the article underscores the present status and the prospective evolution of large language models within the medical sphere, emphasizing their significance and the imperative for ongoing research in this area. In summary, the authors support a holistic strategy for integrating AI into the medical sector. This strategy involves crafting models that prioritize personal data protection, generating high-quality and representative datasets for training, establishing ethical guidelines, and formulating relevant standards and legal frameworks. Additionally, the paper stresses the necessity of addressing technical challenges and innovating new methodologies for assessing AI's efficacy. The significance of this research is underscored by its potential benefits and hurdles associated with AI's incorporation into healthcare, highlighting the critical need for the medical community to be prepared for these evolving dynamics.

Minimally Invasive Bilateral Duo-Keyhole Approach for Giant Falcine Meningioma: 2 Dimensional Operative Video.

One of the key concepts behind minimally invasive keyhole cranial surgery is that as the operative corridor deepens, it also widens. The corridor should therefore be designed parallel to the long-axis of the tumor to optimize visualization.1 These ideas were applied in a duo-keyhole operation for a falcine meningioma. The patient is a 79-year-old woman diagnosed with a large falcine meningioma compressing both frontal lobes. Her tumor was oriented with the long-axis, perpendicular to the superior sagittal sinus, and has bulbous extensions on both sides of the falx. Incisions on the falx, anterior and posterior to the tumor, in addition to a cut between it and the undersurface of the superior sagittal sinus, would render it practically devascularized and "free-floating.2" One keyhole was insufficient, but one anterior and another posterior to the tumor would make the falcine cuts feasible. The operation was performed with the above scheme and the posterior keyhole to the left and anterior one to the right of midline to facilitate surface vascular detachment on both sides. The patient recovered well and was discharged home after 4 days. This procedure highlights that flexible application of the principles of minimally invasive keyhole. Neurosurgery can tailor-make an operation to precisely fit the unique features of a patient and the tumor. The procedure was performed under the ethical guidelines of our hospital. No Institutional Review Board consent was required or sought because the patient gave specific consent to the procedure and publication of her image.

The Comparative Efficacy of Intravenous Ibuprofen 400 mg and 800 mg in Acute Mechanical Low Back Pain: A Prospective, Randomized, Double-Blind Clinical Study

Objective: This study aimed to compare the analgesic efficacy of intravenous (IV) ibuprofen at doses of 400 mg and 800 mg for acute mechanical low back pain. Methods: A Prospective, randomized, double-blind controlled clinical trial design was employed. Patients with moderate to severe acute low back pain were recruited from emergency departments. Ethical approval was obtained, and patients provided written consent. The study adhered to ethical guidelines and Good Clinical Practice principles. Results: Of the 144 patients, demographic characteristics were similar between groups. The 800 mg IV ibuprofen group demonstrated superior analgesic efficacy compared to the 400 mg group, with no significant adverse effects reported. Conclusion: The study concludes that the 800 mg IV dose of ibuprofen was more effective in providing analgesia control for patients with acute mechanical low back pain at all time intervals except for the 30th minute.

Incorporation of artificial intelligence into nursing research: A scoping review.

The integration of artificial intelligence (AI) across different sectors, notably healthcare, is on the rise. However, a thorough exploration of AI's incorporation into nursing research, as well as its advantages and obstacles, is still lacking. The aim of this scoping review was to map the roles, benefits, challenges, and potentials for the future development and use of AI in the context of nursing research. An exhaustive search was conducted across seven databases: MEDLINE, PsycINFO, SCOPUS, Web of Science, CINAHL, Google Scholar, and ProQuest. Articles were additionally identified through manual examination of reference lists of the articles that were included in the study. The search criteria were restricted to articles published in English between 2010 and 2023. The Joanna Briggs Institute (JBI) approach for scoping reviews and the PRISMA-ScR guidelines guided the processes of source selection, data extraction, and data presentation. Twenty articles met the inclusion criteria, covering topics from ethical considerations to methodological issues and AI's capabilities in data analysis and predictive modeling. The review identified both the potentials and complexities of integrating AI into nursing research. Ethical and legal considerations warrant a coordinated approach from multiple stakeholders. The findings emphasized AI's potential to revolutionize nursing research, underscoring the need for ethical guidelines, equitable access, and AI literacy training to ensure its responsible and inclusive use.

Exploring Ethical Principles Within Feminist Foreign Policy

The paper intends to argue how ethics can find its functional position within the implementation of Feminist Foreign Policy (FFP) without challenging the reality of the social, economic, and political conditions of the country by describing the main features of feminist foreign policy and focusing on the ethical principles of the policy. The study aims to understand how feminist foreign policy adopts ethical views and achieves the successful realization of these principles in a global context. Considering the challenging nature of the international arena and the different national interests of the actors, feminist foreign policy faces various difficulties in achieving its goals. To understand the problems and investigate the evolution of FFP, ethical principles in the current political system are looked through to determine which values are compromised to maintain the state interests and why national concerns are above ethical principles. In the second chapter, the examination of FFP and the benefits of following the moralities are studied for deep analyses of feminist ethics. Although there is no agreed definition of the concept, the trend suggests an alternative approach to promoting gender equality and empowerment of women based on ethical and moral guidelines.

Confirmation of the Spiritual Nature of Individual and Society in Yevhen Spektorskyi’s Works of the Emigrant Period

The article presents the main issues of the works by the famous philosopher, jurist and educator Yevhen Vasyliovych Spektorskyi (1875‒1951) written during the period of forced emigration (1920‒1951) and professional activity at the universities of Belgrade, Prague, Ljubljana and the St. Volodymyr Orthodox Theological Seminary in New-York. In the intellectual biography of the thinker, these prolific years are marked by the development of issues in social science, philosophy, moral theology, and Christian ethics. In numerous works in various foreign languages, some of which have been analyzed in this article for the first time, Spektorskyi substantiated the importance of moral — as opposed to physical and mental — dimension of the existence of both individuals and society, for which he regarded the Christian religion to be a reference point. The latter, in his opinion, can truly explore the essence of the individual and social with the help of Christian sociology. The history of social philosophy is essential for social science as well as philosophy itself. Positioned between science and religion, philosophy has the capacity for free thinking, which is crucial for achieving a genuine understanding of society. As it is revealed in the article, the philosopher developed ethical guidelines within the framework of moral theology, criticizing naturalistic and mechanistic approaches to understanding society and the individual. Spektorskyi regarded freedom, dignity, and a righteous life according to the Christian ideal, as well as cultural activity, as fundamental values. The author argues that Spektorskyi’s affirmation of absolute values and the study of the relationship between the individual and society on the basis of the Christian religion provide grounds for evaluating Spektorskyi as an exceptionally religious philosopher.

Adoption of ChatGPT and other Artificial Intelligence among Physicians around the world: Barriers and the way-forward

Artificial Intelligence (AI) has emerged as a transformative force in modern society, revolutionizing various industries, including healthcare. ChatGPT, a language processing tool, has garnered significant attention for its ability to simulate human-like conversations and assist in a myriad of tasks. Despite its potential, the adoption of ChatGPT among physicians worldwide remains limited, particularly in regions like Asia, Latin America and Sub-Saharan Africa. This reluctance poses barriers to leveraging AI for novel diagnosis and treatment approaches, hindering progress in global healthcare. In this study, we explore the medical importance of AI, particularly ChatGPT, highlighting its potential to reduce medical errors and provide accurate healthcare information. However, concerns regarding bias, dependency, and plagiarism have surfaced, necessitating cautious integration and human oversight in its application. Recommendations include increasing awareness among physicians, implementing ethical guidelines, and utilizing AI-detecting software to mitigate risks. Furthermore, collaboration between international medical associations, policymakers, and governing bodies is essential to promoting AI adoption and ensuring its ethical use. While AI holds promise for advancing healthcare, its full potential hinges on overcoming current barriers and fostering a symbiotic relationship between technology and human expertise. Future research must address lingering uncertainties and ethical considerations to realize the transformative impact of AI in medicine.

Artificial Intelligence in education: A comprehensive study

This comprehensive study delves into the multifaceted role of AI in education, exploring its applications, benefits, challenges, and future implications. The purpose of the study is to show how AI n education helps educators identify gaps in student knowledge and provide targeted feedback to improve learning outcomes. As a methodology, the library method and the study and review of various documents have been used in this research. The study examines the diverse range of AI technologies employed in educational settings, including intelligent tutoring systems, personalized learning platforms, educational chatbots, and virtual reality simulations. Furthermore, the study delves into the numerous benefits that AI brings to education. It highlights how AI-powered analytics and data-driven insights enable educators to gain deeper insights into student learning patterns, identify areas for improvement, and tailor instructional strategies accordingly. Additionally, AI-driven tools promote inclusivity by providing personalized support to learners with diverse needs and learning styles. Despite its transformative potential, the study also acknowledges the challenges and ethical considerations associated with integrating AI into education. Data privacy, algorithmic bias, and the digital divide are examined in detail, emphasizing the importance of responsible AI deployment and ethical guidelines. Looking ahead, the study explores the future implications of AI in education and the evolving role of educators in AI-enabled classrooms. It discusses how AI technologies will continue to evolve, offering new opportunities for collaborative learning, skill development, and lifelong education. In conclusion, this comprehensive study underscores the profound impact of AI on education and the need for thoughtful implementation strategies that prioritize equity, inclusivity, and ethical considerations. By harnessing the potential of AI, education systems can better prepare learners for the challenges and opportunities of the future.

Paradoxes, challenges, and opportunities in the context of ethical customer experience management

AbstractThis article examines paradoxes, challenges, and opportunities in the context of ethical customer experience management. Central to this discussion are different stakeholders such as firms, customers, policymakers, regulators, and society at large, as well as key issues associated with them. Firms aim to design and manage compelling experiences using customer data but face challenges balancing cost, customization, ethical concerns, and fairness in customer experience management. Customers should be encouraged to reflect on whether to share personal information for customized experiences, as well as on how far their responsibility goes in ensuring critically informed decision‐making. Policymakers and regulators must integrate the views of various stakeholders into policies and guidelines that promote a healthy society. Importantly, the digitization of customer experience has given rise to unprecedented ethical concerns related to the prospect of ‘programming’ these experiences. The shrinking temporal and spatial distance between stimuli, customer action, data footprint, and tracking poses significant reflections and dilemmas for both customers and firms. In this context, it is essential to determine ethical reflections and guidelines for customer experience design and management. We argue that firms should involve customers in assessing the implications of their experiences, while policymakers should facilitate the involvement of all stakeholders (including firms, customers, regulators, academics, and NGOs) to create an ongoing, healthy dialogue. This dialogue should guide a continuing debate aimed at evaluating the implications of specific actions in customer experience management.

Safety and efficacy of PCSK9 inhibitor (evolocumab) in patients with non-ST segment elevation acute coronary syndrome and non-culprit artery critical lesions: a randomised controlled trial protocol (SPECIAL study)

IntroductionPatients with non-ST segment elevation acute coronary syndrome (NSTE-ACS) and concomitant multivessel coronary artery disease (CAD) are considered patients with extremely high-risk atherosclerotic cardiovascular disease (ASCVD), and current guidelines specify...

Comparative Analysis of Logistic Regression, Gradient Boosted Trees, SVM, and Random Forest Algorithms for Prediction of Acute Kidney Injury Requiring Dialysis After Cardiac Surgery.

This study aimed to identify the best-performing algorithm for predicting Acute Kidney Injury (AKI) necessitating dialysis following cardiac surgery. The dataset encompassed patient data from a tertiary cardiothoracic center in Malaysia between 2011 and 2015, sourced from electronic health records. Extensive preprocessing and feature selection ensured data quality and relevance. Four machine learning algorithms were applied: Logistic Regression, Gradient Boosted Trees, Support Vector Machine, and Random Forest. The dataset was split into training and validation sets and the hyperparameters were tuned. Accuracy, Area Under the ROC Curve (AUC), precision, F-measure, sensitivity, and specificity were some of the evaluation criteria. Ethical guidelines for data use and patient privacy were rigorously followed throughout the study. With the highest accuracy (88.66%), AUC (94.61%), and sensitivity (91.30%), Gradient Boosted Trees emerged as the top performance. Random Forest displayed strong AUC (94.78%) and accuracy (87.39%). In contrast, the Support Vector Machine showed higher sensitivity (98.57%) with lower specificity (59.55%), but lower accuracy (79.02%) and precision (70.81%). Sensitivity (87.70%) and specificity (87.05%) were maintained in balance via Logistic Regression. These findings imply that Gradient Boosted Trees and Random Forest might be an effective method for identifying patients who would develop AKI following heart surgery. However specific goals, sensitivity/specificity trade-offs, and consideration of the practical ramifications should all be considered when choosing an algorithm.

Empowering health care consumers & understanding patients' perspectives on AI integration in oncology and surgery: A perspective.

Artificial intelligence (AI) is transforming oncology and surgery by improving diagnostics, personalizing treatments, and enhancing surgical precision. Patients appreciate AI for its potential to provide accurate prognoses and tailored therapies. However, AI's implementation raises ethical concerns, data privacy issues, and the need for transparent communication between patients and health care providers. This study aims to understand patients' perspectives on AI integration in oncology and surgery to foster a balanced and patient-centered approach. The study utilized a comprehensive literature review and analysis of existing research on AI applications in oncology and surgery. The focus was on examining patient perceptions, ethical considerations, and the potential benefits and risks associated with AI integration. Data was collected from peer-reviewed journals, conference proceedings, and expert opinions to provide a broad understanding of the topic. The perspectives of patients was also emphasized to highlight the nuances of their acceptance and concerns regarding AI in their health care. Patients generally perceive AI in oncology and surgery as beneficial, appreciating its potential for more accurate diagnoses, personalized treatment plans, and improved surgical outcomes. They particularly value AI's role in providing timely and precise diagnostics, which can lead to better prognoses and reduced anxiety. However, concerns about data privacy, ethical implications, and the reliability of AI systems were prevalent. Consequently, trust in AI and health care providers was deemed as a crucial factor for patient acceptance. Additionally, the need for transparent communication and ethical safeguards was also highlighted to address these concerns effectively. The integration of AI in oncology and surgeryholds significant promise for enhancing patient care and outcomes. Patients view AI as a valuable tool that can provide accurate prognoses and personalized treatments. However, addressing ethical concerns, ensuring data privacy, and building trust through transparent communication are essential for successful AI integration. Future initiatives should focus on refining AI algorithms, establishing robust ethical guidelines, and enhancing patient education to harmonize technological advancements with patient-centered care principles.

Significance of p16 in Site-Specific Human Papillomavirus-Positive and Negative Head and Neck Squamous Cell Carcinoma (HNSCC).

Head and neck squamous cell carcinoma (HNSCC) represents a group of cancers characterized by diverse origins and changing epidemiological patterns. The significance of high-risk human papillomavirus (HPV) infection in certain HNSCC cases has gained attention for its impact on the disease's behavior. Our current research focused on exploring the importance of using p16 as an HNSCC biomarker, particularly in the context of HPV infection, assessing its value in prognosis, and examining its variation across different tumor locations. A retrospective analysis was carried out on 100 HNSCC patients from a tertiary care center, with particular attention paid to p16 expression, HPV status, clinic-pathological characteristics, and prognosis. HPV was detected using polymerase chain reaction (PCR) techniques, and p16 expression was evaluated by immunohistochemistry. According to the ethical guidelines outlined in the Declaration of Helsinki, multivariate analysis assessed the prognostic value of p16. Our analysis demonstrated a significant correlation between HPV status and p16 expression in HNSCC cases. A vast majority of 58 (96.7%) HPV-+cases exhibited p16 overexpression, contrasting sharply with only two (5%) in the HPV--group. Patients with tumors that were both p16+and HPV+exhibited more favorable overall survival rates. In contrast, those with p16-and HPV-tumors experienced the poorest survival outcomes. Notably, having a p16--status in HPV+cases emerged as an independent factor for reduced survival. Additionally, the study revealed distinct variations in p16 expression based on tumor location, particularly within the oropharyngeal area. The study established that p16 is a dependable indication for the existence of HPV in HNSCC and highlights its significant role as a prognostic factor, particularly in cases that are p16--yet HPV-+. These findings underscore the importance of adopting site-specific treatment approaches in HNSCC management and contribute to a deeper understanding of p16's role in the disease, thereby aiding in more effective risk assessment and treatment planning.

Navigating the Educational Landscape: Unveiling the impact of ChatGPT in Teacher-Student Dynamics

Objective: The study aims to explore the role of ChatGPT as an Artificial Intelligence tool in education, critically evaluate its performance, discuss potential problems for students, and propose enhancements to improve teacher-student relations. Method: The research utilizes secondary data gathered from various publications, including magazines, books, journals, and websites relevant to the subject. The study focuses on unveiling the impact of ChatGPT on teacher-student dynamics. Results: ChatGPT offers several advantages in education, including customized learning, constant availability, instant feedback, homework assistance, and support for language learning. It can also enhance engagement and provide consistent quality at a lower cost. However, the study identifies significant drawbacks, such as a lack of emotional intelligence, contextual understanding, and personalization. There are concerns about misinformation, over-reliance on technology, privacy, security, and inadvertent bias reinforcement. To address these issues, the study recommends implementing effective feedback mechanisms, conducting regular security audits, promoting collaborative learning environments, and continuously updating AI models. Conclusions: While ChatGPT has transformative potential in education, its implementation must be carefully managed to avoid depersonalization and reliance on AI at the expense of human interaction. The study emphasizes the importance of balancing AI with the human touch in education, ensuring ethical guidelines, and promoting digital literacy. A collaborative effort among educators, policymakers, and technologists is crucial for responsible and ethical integration of AI in education.

Comparative evaluation of shear bond strength of orthodontic brackets bonded with two self-etching adhesive systems: an in vitro study

Abstract Objective To evaluate and compare the shear bond strength (SBS of the new Optibond eXTRa Universal and Adper Prompt L-Pop self-etch adhesive systems using an in vitro experimental design. Patients and methods The sample included 40 freshly extracted human maxillary first premolars collected from orthodontic patients, adhering to ethical guidelines. The teeth were randomly assigned to two groups, one treated with Optibond eXTRa Universal self-etch adhesive and the other with Adper Prompt L-Pop. Stainless-steel brackets were then bonded to the teeth using BracePaste orthodontic adhesive composite resin. After storage and thermocycling, SBS was measured using a universal testing machine, and adhesive remnant index (ARI) scores were assessed under a digital microscope. Statistical analyses were performed, including the Shapiro–Wilk method, Student’s t-test for SBS, and χ 2 test for ARI scores. Results The mean SBS showed no significant difference between both groups (P = 0.076). However, the distribution of ARI scores exhibited a highly significant difference between the two groups (P < 0.001). Conclusion While both adhesives exhibited similar bond strengths, variations in adhesive residue patterns highlight the importance of considering adhesive performance in orthodontic applications such as ease of cleanup and potential impact on enamel health.