Ethical Landscape Research Articles

Influencer Marketing: Changing the Game

Influencer marketing has emerged as a transformative strategy in the new digital economy and is changing the ways brands connect with consumers. This paper explores the development, effectiveness, and impact of influencer marketing on consumer behavior and brand performance by focusing on how influencers bridge gaps between brands and their target audiences through authentic and relatable content. This study analyses the mega to nano influencer and show about reach, engagement, and cost-effectiveness. It examines how on such huge platforms, Instagram, YouTube, and TikTok tend to be a hotspot for influencer-driven campaigns. Lastly, it analyses how influencer marketing shapes consumer trust, increases purchase decisions, and changes brand perception. The research will focus on the ethical and regulatory landscape, which will highlight the importance of transparency, authenticity, and compliance with guidelines like those from the Federal Trade Commission (FTC). The paper also includes case studies of successful campaigns, best practices, and challenges in terms of fake followers, misleading content, and evolving audience expectations. Among them is emerging forward research on future trends, for instance, AI-picks influencer; the rise of virtual influencers and incorporation of immersed technologies such as augmented and virtual reality in their campaigns. Providing an all-around view of how to use an influencer campaign effectively, the research will aid marketers, researchers, and industry players to make workable decisions while harnessing the mighty tool.

Navigating the Minefield: Ethical Challenges and Considerations in Data-Driven Management

In today’s contemporary business climate, data-driven management has emerged as an essential approach for organizations progressing towards streamlining overall operations, elevating decision-making, and promoting creativity. However, when the same data analytics assimilates with managerial applications, it leads to intricate ethical issues to deal with. This chapter examines vital ethical dimensions inherent in data-driven management such as data protection and privacy, liability, transparency, and possibilities of fraudulent use. The key concern is protecting the data collected while ensuring that it is collected with informed and mutual consent from the subjects. At the same time, maintaining transparency in data collection and its eventual utilization is also crucial to gaining and securing trust. Another significant challenge is the liability of companies and individuals to maintain and employ the data ethically. This ethical landscape becomes further complicated by data manipulation or misuse and the detrimental effects of predictive analysis. Businesses can levy data-driven management to promote efficiency and innovation as well as trust and integrity in their operations by proactively tackling these ethical challenges. The chapter unfolds a comprehensive review that delves into the ethical challenges and considerations of corporate decision-making based on data analytics and summarizes the ethical framework encompassing principles of transparency and accountability of businesses in the era of pervasive data-driven decision-making

Systematic review of international ethics knowledge in the speech-language pathology literature (1980–2022)

Purpose A systematic review was conducted to describe various viewpoints expressed in speech-language pathology literature over four decades concerning ethics knowledge in the profession. Method The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were employed. Our sample search encompassed peer-reviewed journal articles sourced from electronic databases including MEDLINE, CINAHL, ERIC, MasterFILE Premier, E-Journals, Africa-Wide Information, and Academic Search Premier, spanning the period from 1980 to 2022. Result The initial search of articles identified 139 papers. After applying rigorous inclusion and exclusion criteria, 60 full-text papers were deemed suitable for inclusion. We conducted a qualitative, deductive analysis of the relevant data and findings. Studies primarily concentrated on the speech-language pathologist’s roles in rehabilitation/management and education, research, and administration. A predominant focus of these papers revolved around moral judgement, a fundamental component of moral behaviour. Conclusion Findings suggest an evolving landscape within the speech-language pathology discipline, with an increased focus on moral judgement, professional competency, and rehabilitation/management. The findings highlight a consistent need for further development and emphasis on ethics in education, research, administration, and advocacy/consultation roles. This underscores the necessity for ongoing ethical education and support to ensure practitioners are well-equipped to navigate the complex ethical landscape of their professional practice.

Navigating ethical dimensions in algorithmic radiology: A call for action to ensure representation of low-resource contexts

Artificial intelligence (AI) has revolutionised medicine, particularly radiology, transforming our ability to interpret complex imaging data. While traditional methods rely on subjective visual assessments, AI excels at recognising intricate patterns and providing quantitative and automated evaluations. In this commentary, I delve into the various ethical considerations in algorithmic radiology, beyond the conventional concerns. Focusing on utilitarianism, patient understanding, virtue ethics, and social contract theory, the paper contributes to a comprehensive understanding of the ethical landscape surrounding AI technologies. The challenges in the use of algorithmic AI in radiology underscore the significance of the ethics of care, responsiveness to context, and the role of human emotion, whether pertaining to the practitioner, the patient, or both. The social contract theory guides the responsibilities of healthcare professionals, urging action to address biases in AI algorithms and ensuring equitable representation of various ethnic and racial populations. Diversity in data must be prioritised to avoid disparities in administering healthcare and uphold patient rights in the adoption of AI. In terms of virtue ethics, professionalism and responsibility are crucial for radiologists adopting AI. Also, an absence of explicit guidelines on the use of AI in healthcare poses challenges, necessitating further discourse. Finally, a utilitarian perspective on public health mandates a fair distribution of imaging technologies to address prevalent health issues in a given population. In conclusion, this paper advocates for an ethical approach to AI integration, which aligns technology with human values and wellbeing, as we shape the future of healthcare.

Organoid Ethical Typology: varieties of three-dimensional stem cell constructs and the many issues they raise in bioethics.

The advancement of and prospects for stem cell research raise a number of specific ethical issues. While navigating the ethical landscape of stem cell research is often challenging for biology researchers and biotechnology innovators, it is also difficult for the public and other persons of concern (from ethicists to policy-makers) to grasp the technicalities of a burgeoning field that develops in many directions. Organoids are one of these new biotechnological constructs that are currently eliciting a rich debate in bioethics. In this guide, we argue that different types of organoids have different emerging properties with different ethical implications. Going from general properties to particular ones, we propose a typology of organoid technology and other associated biotechnology from a philosophical and ethical perspective. We point to relevant ethical issues and try to convey the sense of uncertainty peculiar to ongoing research and emerging technological objects.

Attitudes of Slovenian students towards whole-body donation, organ donation, and the use of donated bodies in medical education

BackgroundAnatomical dissection of whole bodies donated for teaching and research purposes remains a cornerstone of medical education. However, evolving sociocultural and ethical landscapes have historically influenced this practice, necessitating ongoing research to inform and enhance donation programs. This study examines the Slovenian students’ attitudes and factors influencing their decision-making regarding body and organ donation for medical education.Materials and MethodsAn anonymous cross-sectional cohort study was conducted at the University of Ljubljana using a 28-item questionnaire to assess student attitudes toward post-mortem whole-body and organ donation, as well as their perceptions of anatomical dissection in medical training and research. A five-point Likert scale was employed to evaluate attitudinal responses and identify underlying influencing factors.ResultsAnalysis of 1528 student responses revealed significant differences in attitudes toward body and organ donation based on religious affiliation and anatomy course completion. Catholic students exhibited statistically lower levels of support and registration for donation compared to agnostic and atheist students (p < 0.001). A willingness to donate their bodies for teaching and research purposes was reported by 33.1% of participants. Notably, 26.3% would recommend whole-body donation to a family member, 27.9% to a friend, and 31.8% to a member of the general public. Students who had completed an anatomy course demonstrated a significantly higher likelihood of registering for organ donation (p = 0.003), yet were less inclined to donate their bodies for teaching and research due to concerns regarding the treatment of donated bodies.ConclusionDespite general support for organ and body donation, registration rates among Slovenian students remain low, including among those exposed to anatomy courses. We propose integrating end-of-life education, humanistic perspectives, and donor commemorative ceremonies into medical curricula to foster trust, ethical awareness, and advocacy for donation.

Exploring Ethical Challenges in Emergency Medical Services: A Qualitative Study

Background: Emergency medical technicians (EMTs) face complex ethical dilemmas in their frontline role, involving resource constraints, time pressure, patient autonomy, and competing obligations. This qualitative study explored the experiences of EMTs, their decision-making processes, and the potential areas for enhancing ethical practices in emergency medicine, aiming to inform targeted policies and training programs that support EMTs and improve care quality. Methods: A total of 17 EMTs from Qom province participated in this study and in-depth interviews were conducted to gather rich and detailed insights into their experiences. The interviews were transcribed and analyzed using Graneheim and Lundman’s conventional content analysis to identify common themes and patterns related to ethical challenges. Results: The findings identified five main categories of ethical challenges faced by EMTs including ethical dilemmas, professional conduct, communication and consent, ethical training and support, and organizational challenges. Within these categories, specific subcategories emerged, including decision-making challenges, conflict of interest, resource allocation, end-of-life care, dual relationships, emotional boundaries, privacy and confidentiality, breaking bad news, language barriers, and informed consent. Each subcategory shed light on the unique ethical challenges faced by EMTs. Conclusion: This study highlighted the complex ethical landscape in which EMTs operate and provided valuable insights into the challenges they face. The findings underscored the need for comprehensive ethical training, support mechanisms, improved communication strategies, and an organizational culture that promotes ethical conduct. It is essential to address these challenges to enhance the ethical practices and decision-making skills of EMTs, ultimately leading to improved patient care and outcomes.

AI Ethics and Responsible AI Development: Navigating the Ethical Landscape of Artificial Intelligence

This in-depth article on the ethics and responsible development of artificial intelligence (AI) looks at how quickly AI technologies are changing society and how big those changes are. Ethical issues like bias, privacy, responsibility, and openness are looked at in detail. It outlines responsible AI development principles, encompassing fairness, human-centered values, and safety. It discusses practical strategies for implementing ethical AI practices, including diverse development teams and explainable AI techniques. The article examines applications of responsible AI across finance, recruitment, and healthcare. Finally, it looks at future directions for AI ethics, including regulatory frameworks, global collaboration, interdisciplinary research, and education initiatives. By addressing these crucial ethical challenges, the aim is to ensure AI technologies align with human values and benefit society as a whole.

Augmenting research consent: should large language models (LLMs) be used for informed consent to clinical research?

The integration of artificial intelligence (AI), particularly large language models (LLMs) like OpenAI’s ChatGPT, into clinical research could significantly enhance the informed consent process. This paper critically examines the ethical implications of employing LLMs to facilitate consent in clinical research. LLMs could offer considerable benefits, such as improving participant understanding and engagement, broadening participants’ access to the relevant information for informed consent and increasing the efficiency of consent procedures. However, these theoretical advantages are accompanied by ethical risks, including the potential for misinformation, coercion and challenges in accountability. Given the complex nature of consent in clinical research, which involves both written documentation (in the form of participant information sheets and informed consent forms) and in-person conversations with a researcher, the use of LLMs raises significant concerns about the adequacy of existing regulatory frameworks. Institutional Review Boards (IRBs) will need to consider substantial reforms to accommodate the integration of LLM-based consent processes. We explore five potential models for LLM implementation, ranging from supplementary roles to complete replacements of current consent processes, and offer recommendations for researchers and IRBs to navigate the ethical landscape. Thus, we aim to provide practical recommendations to facilitate the ethical introduction of LLM-based consent in research settings by considering factors such as participant understanding, information accuracy, human oversight and types of LLM applications in clinical research consent.

Navigating the Ethical Landscape of ChatGPT Integration in Scientific Research: Review of Challenges and Recommendations

This study aims to investigate the transformative potential of incorporating ChatGPT into academic research while addressing consequential challenges and ethical concerns. The chatbot, as a large language model (LLM), allows users to interact with it, providing advantages in the academic research context. The benefits of real-time interaction, information synthesis, and language generation have the potential to modernize how researchers engage with and extract insights from vast amounts of data. Additionally, ChatGPT demonstrates a strong command of programming languages, which is highly relevant in modern research related to machine learning and artificial intelligence. However, it still has certain limitations, such as the potential for generating fake citations and issues related to plagiarism. Users need to be aware of these limitations. Therefore, researchers must be careful regarding the potential and limitations of ChatGPT, as it can improve efficiency but may decrease the quality of work if misused. This study recommends guidelines in academic research for using LLMs like ChatGPT. Overall, it's not ChatGPT that downgrades the quality of academic work; rather, it's the irresponsible use of ChatGPT that does it.

Economics and Equity of Large Language Models: Health Care Perspective.

Large language models (LLMs) continue to exhibit noteworthy capabilities across a spectrum of areas, including emerging proficiencies across the health care continuum. Successful LLM implementation and adoption depend on digital readiness, modern infrastructure, a trained workforce, privacy, and an ethical regulatory landscape. These factors can vary significantly across health care ecosystems, dictating the choice of a particular LLM implementation pathway. This perspective discusses 3 LLM implementation pathways-training from scratch pathway (TSP), fine-tuned pathway (FTP), and out-of-the-box pathway (OBP)-as potential onboarding points for health systems while facilitating equitable adoption. The choice of a particular pathway is governed by needs as well as affordability. Therefore, the risks, benefits, and economics of these pathways across 4 major cloud service providers (Amazon, Microsoft, Google, and Oracle) are presented. While cost comparisons, such as on-demand and spot pricing across the cloud service providers for the 3 pathways, are presented for completeness, the usefulness of managed services and cloud enterprise tools is elucidated. Managed services can complement the traditional workforce and expertise, while enterprise tools, such as federated learning, can overcome sample size challenges when implementing LLMs using health care data. Of the 3 pathways, TSP is expected to be the most resource-intensive regarding infrastructure and workforce while providing maximum customization, enhanced transparency, and performance. Because TSP trains the LLM using enterprise health care data, it is expected to harness the digital signatures of the population served by the health care system with the potential to impact outcomes. The use of pretrained models in FTP is a limitation. It may impact its performance because the training data used in the pretrained model may have hidden bias and may not necessarily be health care-related. However, FTP provides a balance between customization, cost, and performance. While OBP can be rapidly deployed, it provides minimal customization and transparency without guaranteeing long-term availability. OBP may also present challenges in interfacing seamlessly with downstream applications in health care settings with variations in pricing and use over time. Lack of customization in OBP can significantly limit its ability to impact outcomes. Finally, potential applications of LLMs in health care, including conversational artificial intelligence, chatbots, summarization, and machine translation, are highlighted. While the 3 implementation pathways discussed in this perspective have the potential to facilitate equitable adoption and democratization of LLMs, transitions between them may be necessary as the needs of health systems evolve. Understanding the economics and trade-offs of these onboarding pathways can guide their strategic adoption and demonstrate value while impacting health care outcomes favorably.

Ethical considerations in surgical research conducted in African LMICs: a comprehensive narrative review.

Surgical research has revolutionized healthcare by improving the efficacy, safety, and efficiency of surgical interventions. This research has the potential to significantly impact healthcare delivery in Africa, where surgical diseases pose a major public health burden. Studies suggest surgery alone could reduce the global disease burden by 11%, with a substantial portion stemming from conditions prevalent in sub-Saharan Africa like traumatic injuries, childbirth complications, and surgical infections. However, conducting surgical research in Africa presents unique ethical challenges. The continent's diverse socio-economic environments and rich cultural contexts necessitate careful consideration of ethical principles. Traditional research models often don't translate well to African settings, raising concerns about informed consent, community engagement, and benefit-sharing. This review aims to shed light on various ethical dilemmas posed within surgical research, conducted in African countries. Further, standard practices and recommendations that involve a multi-pronged approach to mitigate said issues were explored. A meticulous analysis of the existing literature pertaining to the ethical issues proffered in Africa was performed. Databases employed comprised PubMed/MEDLINE, Embase, Scopus, and EBSCOhost. Pertinent case studies were also reviewed to comprehend said issues, providing comprehensive recommendations. In addressing the ethical challenges in the research, the importance of resource limitation, sociocultural factors, informed consent barriers, technological obsolescence, training deficits, power dynamics, vulnerable populations, regulatory oversight, and compliance was highlighted. The proposed approaches include conducting focused research lectures and practical workshops on surgery, organizing surgical boot camps for medical students, interns, and residents, promoting research in related fields such as anesthesia, radiology, and pathology, ensuring the presence of on-site surgeons as needed, and arranging outreach surgical and educational camps for patients. Promoting community engagement and training local researchers and surgeons are crucial for navigating the unique ethical landscape in Africa. By prioritizing ethical considerations, surgical research can contribute to improved healthcare outcomes and a more equitable healthcare system across the continent.

Privacy, confidentiality and ethical concerns in audio ai assistants: A comparative study of North American, European, and Asian Markets

This study examines the ethical, privacy, and confidentiality issues with audio AI assistants in the markets of North America, Europe, and Asia. Understanding the ramifications of data collecting, storage, and usage is crucial as the use of technologies like Google Assistant and Amazon Alexa grows. The study highlights significant regulatory differences between the areas; North America offers a fragmented landscape, and many Asian nations are still developing their data protection legislation, while Europe leads with strong frameworks like the General Data Protection Regulation (GDPR). Key challenges identified include a lack of user awareness regarding privacy rights, extensive data collection practices, unauthorized listening, data breaches, and cultural differences influencing attitudes toward data protection. The findings reveal that these challenges impact user trust and hinder the responsible innovation of AI technologies. Recommendations for improvement include establishing global regulations to promote consistency in data protection, adopting best practices such as privacy-by-design principles, and enhancing user education initiatives. Additionally, the study identifies critical areas for future research, particularly the role of AI assistants in sensitive sectors like healthcare and education, where privacy concerns may differ significantly. Overall, this comparative analysis provides a foundational understanding of audio AI assistants' ethical landscape and underscores the importance of addressing these issues to foster a secure and trustworthy digital environment.

Application of artificial intelligence in medical education: A meta-ethnographic synthesis

Background With the advancement of Artificial Intelligence (AI), it has had a profound impact on medical education. Understanding the advantages and issues of AI in medical education, providing guidance for educators, and overcoming challenges in the implementation process is particularly important. Objective The objective of this study is to explore the current state of AI applications in medical education. Methods A systematic search was conducted across databases such as PsycINFO, CINAHL, Scopus, PubMed, and Web of Science to identify relevant studies. The Critical Appraisal Skills Programme (CASP) was employed for the quality assessment of these studies, followed by thematic synthesis to analyze the themes from the included research. Results Ultimately, 21 studies were identified, establishing four themes: (1) Shaping the Future: Current Trends in AI within Medical Education; (2) Advancing Medical Instruction: The Transformative Power of AI; (3) Navigating the Ethical Landscape of AI in Medical Education; (4) Fostering Synergy: Integrating Artificial Intelligence in Medical Curriculum. Conclusion Artificial intelligence’s role in medical education, while not yet extensive, is impactful and promising. Despite challenges, including ethical concerns over privacy, responsibility, and humanistic care, future efforts should focus on integrating AI through targeted courses to improve educational quality.

Santykis su atliekomis žiedinėje ekonomikoje: rūpesčio etikos perspektyva

This article examines the ethical dimension of waste management within the context of the circular economy. The authors argue for a shift in focus from technological and practical issues in waste management to a more holistic consideration of the ontological and ethical aspects of waste. By building on the ethics of care perspective, the article proposes a relational approach which prioritizes attentiveness, responsibility, competence, responsiveness, and involvement in addressing waste issues. By examining the five stages of care within the ethics of care framework, the study shows how ethical considerations can inform individual and collective actions within the paradigm of the circular economy. Ultimately, the article reconceptualizes waste as an integral part of our moral and ethical landscape by highlighting relationships embedded in care across private, domestic, institutional, and public domains.

Investigation of the ethical and legal issues of euthanasia and assisted suicide

Euthanasia and assisted suicide are highly complex and ethically charged medical practices that challenge societal norms and legal frameworks worldwide. This research investigates the multifaceted ethical and legal dilemmas surrounding these end-of-life options. It examines the core ethical arguments for and against euthanasia and assisted suicide, including autonomy, beneficence, non-maleficence, and justice. The analysis explores different legal approaches to these practices globally, comparing permissive, restrictive, and prohibitive regimes. It delves into the legal complexities of informed consent, competency, vulnerability, and the potential for abuse. Furthermore, the research examines the role of palliative care and the impact of social, cultural, and religious factors on the debate. This investigation aims to provide a comprehensive understanding of the ethical and legal landscape of euthanasia and assisted suicide, contributing to informed policy-making and ethical medical practice. Euthanasia and assisted suicide are topics that spark intense ethical, legal, and social debate. Both are related to the right to die, but there are fundamental differences between them.

Ethical constraints and dilemmas in the provision of in-vitro fertilization treatment in Ghana: from the perspectives of experts

BackgroundInfertility presents both medical and public health challenges, with in vitro fertilization (IVF) emerging as a prominent solution, particularly when other alternatives are exhausted. However, IVF treatment raises significant ethical questions that have been under explored in the Ghanaian context. This study aimed to explore ethical constraints and dilemmas in the provision of in vitro fertilization (IVF) treatment in Ghana.MethodsA descriptive phenomenological qualitative design was employed. Purposive sampling techniques were used to recruit 12 participants including ART experts from three in vitro fertilization (IVF) centres, ethicists and a legal practitioner. In-depth face-to-face interviews guided by an open-ended interview guide were conducted. Thematic analysis of the interviews was performed to identify major themes.ResultsProviding IVF treatment in Ghana raises several ethical issues, including inequitable access due to high costs and limited availability, which favour wealthier individuals and leave marginalized populations with fewer options. There are significant ethical considerations in balancing the potential benefits of successful IVF treatment outcomes against the health risks and emotional tolls on patients. Decisions about the fate of surplus embryos present moral dilemmas, including whether to preserve, donate, or discard them. Differing personal beliefs about the moral status of the embryo further complicate the ethical landscape. Ensuring informed consent is challenging due to the complex medical, ethical, and emotional implications of IVF treatment, potentially leading to compromised consent. Additionally, there are ongoing risks of breaches in confidentiality, given the sensitive nature of reproductive health data and the importance of protecting patient privacy.ConclusionThis study revealed that several ethical dilemmas confront both healthcare providers and couples in the process of IVF. There is an urgent need for the development of clear and uniform regulations to govern the practice of IVF treatment in Ghana, with further attention given to mitigating financial barriers and enhancing support systems for couples considering IVF treatment.

Introducing ELLIPS: An Ethics-Centered Approach to Research on LLM-Based Inference of Psychiatric Conditions

As mental health care systems worldwide struggle to meet demand, there is increasing focus on using language models (LM) to infer neuropsychiatric conditions or psychopathological traits from language production. Yet, so far, this research has only delivered solutions with limited clinical applicability, due to insufficient consideration of ethical questions crucial to ensuring the synergy between possible applications and model design. To accelerate progress towards clinically applicable models, our paper charts the ethical landscape of research on language-based inference of psychopathology and provides a practical tool for researchers to navigate it. We identify seven core ethical principles that should guide model development and deployment in this domain, translate them into ELLIPS, an ethical toolkit operationalizing these principles into questions that can guide researchers' choices with respect to data selection, architectures, evaluation, and model deployment, and provide a case study exemplifying its use. With this, we aim to facilitate the emergence of model technology with concrete potential for real-world applicability.

Introduction to the Special Issues

Abstract Artificial intelligence (AI) is rapidly reshaping our world. As AI systems become increasingly autonomous and integrated into various sectors, fundamental ethical issues such as accountability, transparency, bias, and privacy are exacerbated or morph into new forms. This introduction provides an overview of the current ethical landscape of AI. It explores the pressing need to address biases in AI systems, protect individual privacy, ensure transparency and accountability, and manage the broader societal impacts of AI on labor markets, education, and social interactions. It also highlights the global nature of AI's challenges, such as its environmental impact and security risks, stressing the importance of international collaboration and culturally sensitive ethical guidelines. It then outlines three unprecedented challenges AI poses to copyright and intellectual property rights; individual autonomy through AI's “hypersuasion”; and our understanding of authenticity, originality, and creativity through the transformative impact of AI-generated content. The conclusion emphasizes the importance of ongoing critical vigilance, imaginative conceptual design, and collaborative efforts between diverse stakeholders to deal with the ethical complexities of AI and shape a sustainable and socially preferable future. It underscores the crucial role of philosophy in identifying and analyzing the most significant problems and designing convincing and feasible solutions, calling for a new, engaged, and constructive approach to philosophical inquiry in the digital age.

Diverging perceptions of artificial intelligence in higher education: A comparison of student and public assessments on risks and damages of academic performance prediction in Germany

The integration of Artificial Intelligence (AI) into higher education, particularly through Academic Performance Prediction (APP), promises enhanced educational outcomes. However, it simultaneously raises concerns regarding data privacy, potential biases, and broader socio-technical implications. Our study, focusing on Germany–a pivotal player in shaping the European Union's AI policies–seeks to understand prevailing perceptions of APP among students and the general public. Initial findings of a large standardized online survey suggest a divergence in perceptions: While students, in comparison to the general population, do not attribute a higher risk to APP in a general risk assessment, they do perceive higher societal and, in particular, individual damages from APP. Factors influencing these damage perceptions include trust in AI and personal experiences with discrimination. Students further emphasize the importance of preserving their autonomy by placing high value on self-determined data sharing and explaining their individual APP. Recognizing these varied perceptions is crucial for educators, policy-makers, and higher education institutions as they navigate the intricate ethical landscape of AI in education. This understanding can inform strategies that accommodate both the potential benefits and concerns associated with AI-driven educational tools.