Redefining Postgraduate Supervision in the Age of AI: Balancing Technology and Human Mentorship

With the increasing use of Generative Artificial Intelligence (AI) tools like ChatGPT and Bard, universities face challenges in maintaining academic integrity. This research investigates the impact of these tools on learning outcomes (factual knowledge, comprehension, critical thinking) in selected universities of Ghana's Upper East Region during the 2023-2024 academic year. The study specifically analyzes changes in student comprehension and academic integrity concerns when using Generative AI for content generation, research assistance, and summarizing complex topics. A mixed-methods approach was employed, combining qualitative data from interviews and open-ended questions with quantitative analysis of survey data and academic records. The research focuses on three institutions: C. K. Tedam University of Technology and Applied Sciences, Bolgatanga Technical University, and Regentropfen University College. A purposive sampling technique recruited 150 participants (50 from each university) who had used Generative AI tools. Key findings show that 72% of students reported improved understanding of course material through Generative AI use, yet 75% cited academic integrity as a primary concern. Quantitative analysis revealed a weak to moderate positive correlation (r = 0.45) between AI tool usage and improved grades, with variations depending on the specific AI tasks performed. Qualitative data highlighted concerns about overreliance on AI and its impact on critical thinking skills. This research contributes to the ongoing debate on AI's role in education by providing valuable insights for educators and policymakers worldwide. The findings suggest that while AI tools can enhance comprehension, ethical considerations and potential drawbacks related to critical thinking require careful attention. The study concludes with recommendations for integrating AI literacy programs, developing ethical guidelines, and implementing advanced plagiarism detection systems to harness the benefits of Generative AI while mitigating risks to academic integrity. Although specific to the Upper East Region of Ghana, these insights may be applicable to other educational systems with similar characteristics.

In the digital age, the intersection of artificial intelligence (AI) and higher education (HE) poses novel ethical considerations, necessitating a comprehensive exploration of this multifaceted relationship. This study aims to quantify and characterize the current research trends and critically assess the discourse on ethical AI applications within HE. Employing a mixed-methods design, we integrated quantitative data from the Web of Science, Scopus, and the Lens databases with qualitative insights from selected studies to perform scientometric and content analyses, yielding a nuanced landscape of AI utilization in HE. Our results identified vital research areas through citation bursts, keyword co-occurrence, and thematic clusters. We provided a conceptual model for ethical AI integration in HE, encapsulating dichotomous perspectives on AI’s role in education. Three thematic clusters were identified: ethical frameworks and policy development, academic integrity and content creation, and student interaction with AI. The study concludes that, while AI offers substantial benefits for educational advancement, it also brings challenges that necessitate vigilant governance to uphold academic integrity and ethical standards. The implications extend to policymakers, educators, and AI developers, highlighting the need for ethical guidelines, AI literacy, and human-centered AI tools.

The integration of artificial intelligence (AI) tools into postgraduate supervision in higher education has accelerated globally, offering opportunities to enhance efficiency in research processes and academic mentoring. However, limited empirical evidence exists regarding the risks and challenges of this integration, particularly within Global South contexts such as South Africa. This study investigates the challenges associated with the use of AI tools in postgraduate supervision from a South African perspective. Anchored in a constructivist paradigm, the study adopts a qualitative research design, employing semi-structured interviews with 20 purposively selected participants—10 postgraduate students and 10 supervisors from faculties that are actively integrating AI into supervisory practices. Data were analysed thematically using qualitative content analysis. The findings identify six key challenges: increasing dependence on AI that may erode students’ critical thinking and originality; insufficient digital literacy and institutional support; financial and sustainability constraints; the questionable reliability and accuracy of AI-generated outputs; ethical dilemmas and limited cultural contextualisation; and resistance to technological change among supervisors. While acknowledging the potential of AI to enhance research productivity and the quality of supervision, the study cautions against its uncritical adoption, which may compromise academic integrity, creativity, and equity. It recommends institutional strategies, including subsidised AI access, structured training on ethical and critical AI use, the embedding of digital literacy in postgraduate curricula, and the fostering of collaboration with AI developers to ensure culturally relevant systems. A context-sensitive approach is essential to balance the affordances of AI with the preservation of human intellectual agency and critical scholarly engagement in postgraduate supervision.

The rise of artificial intelligence (AI) is transforming educational practices, particularly in assessment. While AI may support the students in idea generation and summarization of source materials, it also introduces challenges related to content validity, academic integrity, and the development of critical thinking skills. Educators need strategies to navigate these complexities and maintain rigorous, ethical assessments that promote higher order cognitive skills. This article provides practical guidance for educators on designing take-home assessments (e.g. research-based assignments) in the AI era. This guidance was developed through a collaborative, consensus-driven process involving a consortium of three educators with diverse academic backgrounds, career stages, and perspectives on AI in education. Members, holding experience in higher education across the United Kingdom, United States of America, Australia, and Middle East and North Africa regions, brought varied insights into AI's role in education. The team engaged in an iterative process of refining recommendations through biweekly virtual meetings and offline discussions. Four key recommendations are presented 1) codeveloping AI literacy among students and educators, 2) designing assessments that prioritize process over output, 3) validating learning through AI-free assessments, and 4) preparing students for AI-enhanced workplaces by developing AI communication skills and promoting human-AI collaboration. These strategies emphasize ethical AI use, personalized feedback, and creativity. By adopting these approaches, educators can balance the benefits and risks of AI in assessments, fostering authentic learning while preparing students for the challenges of an AI-driven world.NEW & NOTEWORTHY This paper presents a framework to effectively design take-home assessments in the generative artificial intelligence (AI) era with four key recommendations to navigate the challenges and opportunities posed by generative AI. From codeveloping AI literacy to fostering human-AI collaboration, the strategies empower educators to promote authentic learning, critical thinking, and ethical AI use. Adaptable to various contexts, these insights help prepare students for an AI-driven future while maintaining academic rigor and integrity.

Artificial intelligence (AI) is scaling rapidly in higher education globally. Considering the increasing significance of artificial intelligence in higher education (AIHEd) and the absence of a comprehensive review on it, this paper delves into the evolving landscape of artificial intelligence in higher education (AIHEd), its academic integrity and ethical concerns. The study has applied qualitative approach by using literature review as a research design and method to facilitate the aim of the study.The analysis of the paper reveals that AI has the potential to make a significant contribution to enhancing teaching and learning experiences, improving productivity and efficiency, as well as fostering inclusivity and accessibility. On the contrary, the increasing utilization of AI in higher education (AIHEd) raises the concerns about academic integrity and ethical issues, as it has the potential to lead to plagiarism, impede critical thinking, suppress creativity, and erode originality in teaching, research, and scholarship. Hence, upholding the integrity of scientific research requires a rigorous commitment to ethical and academic principles, placing human intelligence and critical thinking at the forefront of the research process. The advancement of artificial intelligence in higher education not only brings significant advantages, but also poses challenges to the fundamental principles, methodologies, standards, ethical considerations and academic integrity in both teaching and research. As a result, the primary focus should be on embracing the opportunities and benefits that arise from this advancement and effectively addressing any potential risks and challenges.

Artificial Intelligence (AI) is revolutionizing the field of health sciences, reshaping how we teach, learn, and practice medicine. As AI technologies become increasingly integrated into healthcare systems, their impact on health sciences education cannot be overstated. From personalized learning experiences to advanced diagnostic training, AI is poised to enhance the quality and accessibility of education for future healthcare professionals. However, this transformation also raises critical questions about ethics, equity, and the future role of educators in an AI-driven world. The transformative role of Artificial Intelligence (AI) in health sciences education is increasingly recognized as a pivotal factor in shaping the future of medical training and practice. As AI technologies continue to evolve, their integration into educational curricula presents both opportunities and challenges that must be carefully navigated to enhance the learning experience for future healthcare professionals. One of the most significant contributions of AI to health sciences education is its ability to personalize learning. Traditional teaching methods often follow a one-size-fits-all approach, which can leave some students struggling to keep up while others are not sufficiently challenged. AI-powered platforms, such as adaptive learning systems, analyze individual student performance and tailor content to meet their unique needs. For example, tools like Osmosis and AMBOSS use AI to provide customized study plans, ensuring that students focus on areas where they need the most improvement (Topol, 2019). This personalized approach not only improves learning outcomes but also fosters a more inclusive educational environment. AI is also transforming clinical training by simulating real-world scenarios. Virtual patient simulations, powered by AI, allow students to practice diagnosing and treating conditions in a risk-free environment. These simulations can replicate rare or complex cases that students might not encounter during their clinical rotations. For instance, platforms like Touch Surgery and SimX use AI to create immersive surgical and emergency care simulations, providing students with hands-on experience before they enter the operating room (McGaghie et al., 2011). Such tools bridge the gap between theory and practice, preparing students for the complexities of modern healthcare. Moreover, AI is enhancing the role of educators by automating administrative tasks and providing data-driven insights into student performance. Grading, attendance tracking, and even curriculum design can be streamlined using AI, allowing educators to focus on mentoring and engaging with students. AI-driven analytics can also identify at-risk students early, enabling timely interventions to support their academic success (Wartman & Combs, 2018). By augmenting the capabilities of educators, AI empowers them to deliver more impactful and student-centered teaching. AI's potential to revolutionize health sciences education lies in its ability to personalize learning experiences and improve educational outcomes. For instance, AI-driven tools can facilitate realistic simulations and automated assessments, allowing students to engage in practical scenarios that mimic real-world clinical situations (Santos & Lopes, 2024). This capability not only enhances the learning process but also prepares students for the complexities of patient care in a technology-driven environment (Grunhut et al., 2022). Furthermore, the incorporation of AI into curricula can foster critical thinking and decision-making skills, essential for navigating the ethical dilemmas that arise in medical practice (Grunhut et al., 2022). Despite the promising applications of AI in education, the integration of these technologies into medical curricula has been slow. A scoping review highlighted that many medical schools have yet to adopt AI training, primarily due to a lack of systematic evidence supporting its implementation (Lee et al., 2021). Additionally, concerns regarding data protection and the ethical implications of AI use in healthcare education have been raised, indicating a need for comprehensive AI education that addresses these issues (Veras et al., 2023; Frehywot & Vovides, 2023). Students have expressed a desire for more robust training in AI, emphasizing the importance of understanding its role in healthcare delivery and decision-making processes (Ahmad et al., 2023; Derakhshanian et al., 2024). Moreover, the rapid advancement of AI technologies necessitates continuous curriculum updates to keep pace with emerging trends. As noted in recent literature, the integration of AI into biomedical science curricula should include subjects related to informatics, data sciences, and digital health (Sharma et al., 2024). This approach not only equips students with the necessary skills to utilize AI effectively but also prepares them for the evolving landscape of healthcare, where AI will play an integral role in diagnostics, treatment personalization, and patient management (Santos & Lopes, 2024; Secinaro et al., 2021). However, the implementation of AI in health sciences education is not without challenges. Ethical considerations surrounding AI's impact on healthcare equity and the potential for bias in AI algorithms must be addressed (Frehywot & Vovides, 2023; Han et al., 2019). Ensuring that AI technologies are used responsibly and equitably in education and practice is crucial to avoid exacerbating existing disparities in healthcare access and outcomes (Rigby, 2019). Furthermore, the lack of faculty expertise in AI poses a significant barrier to its integration into medical education, highlighting the need for targeted training and resources for educators (Derakhshanian et al., 2024). However, the integration of AI into health sciences education is not without challenges. Ethical concerns, such as data privacy and algorithmic bias, must be addressed to ensure that AI tools are used responsibly. Additionally, there is a risk of over-reliance on AI, potentially undermining the development of critical thinking and clinical judgment skills. Educators must strike a balance between leveraging AI’s capabilities and preserving the human elements of teaching and learning. Equity is another pressing issue. While AI has the potential to democratize education, access to these technologies remains uneven. Institutions in low-resource settings may struggle to adopt AI-driven tools, exacerbating existing disparities in global health education. Policymakers and educators must work together to ensure that the benefits of AI are accessible to all, regardless of geographic or socioeconomic barriers. In conclusion, AI is a powerful tool that holds immense promise for transforming health sciences education. By personalizing learning, enhancing clinical training, and supporting educators, AI can help prepare the next generation of healthcare professionals to meet the demands of an increasingly complex healthcare landscape. However, its integration must be guided by ethical principles and a commitment to equity, However, the successful integration of AI into educational curricula requires a concerted effort to address ethical concerns, update training programs, and equip both students and faculty with the necessary knowledge and skills. As the healthcare landscape continues to evolve, embracing AI in education will be essential for fostering a new generation of healthcare providers who are adept at leveraging technology to improve patient care. As we embrace this technological revolution, we must remember that AI is not a replacement for human expertise but a complement to it. The future of health sciences education lies in the synergy between human ingenuity and artificial intelligence.

This article investigates the relationship between Sustainable Development Goal 4 (SDG 4), academic integrity as its part, and artificial intelligence (AI) through a bibliometric analysis, assessing whether this intersection represents a clash or win-win cooperation. SDG 4 aims to ensure equitable access to quality education, while AI technologies have the potential to enhance educational practices but demote academic integrity. By analyzing a comprehensive body of the literature, this study identifies key trends and thematic areas where AI is applied in educational settings, particularly concerning maintaining academic integrity. The findings reveal a growing body of research highlighting AI’s role in personalizing learning experiences, improving educational accessibility, and supporting educators’ teaching methodologies. However, challenges such as ethical considerations, data privacy, and the digital divide are also addressed, indicating potential conflicts that need to be navigated. Ultimately, this analysis suggests that while there are significant opportunities for synergy between AI and SDG 4, the management of careful implementation and policy frameworks is essential to ensure that AI serves as a tool for promoting inclusive and sustainable education rather than exacerbating existing inequalities. AI transforms science management by enhancing data analysis, streamlining research processes, and improving decision-making, ultimately leading to more efficient and effective scientific research and innovation. The findings reveal that while AI can facilitate personalized learning and enhance educational accessibility, it also poses challenges related to academic misconduct, such as plagiarism and the misuse of AI-generated content. This duality highlights the need for educational institutions to develop robust frameworks that leverage AI’s capabilities while safeguarding academic integrity. The article concludes that a collaborative approach, integrating AI into educational practices with a strong emphasis on ethical considerations and integrity, can lead to a synergistic relationship that supports the goals of SDG 4. Recommendations for future research and practical implications for managers, educators, scientists, and policymakers are also discussed, emphasizing the importance of fostering an educational environment that embraces innovation while upholding ethical standards.

Artificial Intelligence (AI) has emerged as one of the most transformative technological developments of the modern era, offering a profound impact on various sectors, including education. The integration of AI into education presents a complex interplay of opportunities, challenges, and risks that require comprehensive exploration. This study adopts a mixed-methods approach, utilizing bibliometric, conceptual, and hermeneutic analyses to examine the role of AI in educational contexts. Bibliometric analysis quantitatively and visually maps research trends, employing tools to analyze 8,288 documents retrieved from the Scopus database using the keywords "Artificial Intelligence" OR "AI" AND "Education." Over the past two decades, the field has seen exponential growth, with contributions from 28,740 authors worldwide. The mapping highlights key areas of AI application, such as machine learning, deep learning, educational engineering, and computer-aided instruction, alongside technologies like ChatGPT, virtual reality, big data, and data mining. Conceptual analysis dissects critical ideas such as posthumanism, transhumanism, and their intersection with AI in education. These paradigms are reshaping education by challenging its traditional roles as a vehicle for human development and societal progress. AI's role in facilitating personalized learning experiences underscores its potential for enhancing efficiency and adaptability. However, it also raises pressing questions about its alignment with broader educational objectives. Beyond knowledge transfer, education fosters character development, critical thinking, and social skills areas potentially undermined by overreliance on AI technologies. The hermeneutic method provides a deeper interpretation of philosophical texts and literature, emphasizing the ethical and societal implications of integrating AI into education. Posthumanism and transhumanism, which advocate for merging human capabilities with technological advancements, pose existential challenges to education's foundational values. Tools like ChatGPT, while powerful, risk eroding academic integrity and critical skill development. For instance, reliance on AI for solving exam problems or answering complex queries undermines the principles of effort and intellectual growth, essential components of education. The research underscores the dual-edged nature of AI in education. On one hand, it enables adaptive and efficient learning systems. On the other hand, it disrupts traditional educational practices, raising ethical dilemmas and questions about the future of human-centric learning. Philosophical perspectives are crucial in navigating these disruptions, advocating for an education system that balances technological integration with the preservation of core academic values. This involves addressing the ethical considerations surrounding AI applications, particularly the potential misuse of tools like ChatGPT, which are byproducts of posthumanist and transhumanist ideologies. Education, as a transformative process, must evolve to integrate AI responsibly, ensuring that it complements rather than replaces human intellectual efforts. This requires reconstructing educational systems to mitigate the unintended consequences of AI technologies. Philosophical inquiry into the purpose of education must guide the realignment of its goals, emphasizing how knowledge is acquired and skills are nurtured in a rapidly evolving digital landscape. By fostering awareness of the ethical and practical implications of AI, educational institutions can reclaim their role in cultivating well-rounded individuals capable of critical thought and ethical decision-making. In conclusion, this study highlights the need for a balanced approach to integrating AI into education. While technological advancements offer unprecedented opportunities for innovation, they must not overshadow the humanistic values at the core of education. By addressing the challenges posed by posthumanism and transhumanism, the educational sector can harness AI's potential while safeguarding its mission to develop responsible, ethical, and capable individuals for the 21st century.

Editorial

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

The differential effects of artificial intelligence (AI) on critical thinking in postgraduate education, particularly in the context of academic integrity, remain underexplored. This cross-sectional study addressed this gap by surveying a convenience sample of 555 postgraduate students from a public university in Amman, Jordan. Results indicated that AI usage positively supports lower-order cognitive skills (remembering, understanding, applying), whereas its impact on higher-order skills (analyzing, evaluating, creating) was weaker and contingent on students’ academic integrity. Structural equation modeling demonstrated that academic integrity moderates the relationship between AI usage and critical thinking: students with high integrity effectively leveraged AI to enhance higher-order skills through deep engagement, critical evaluation, and idea generation, while students with lower integrity showed minimal gains in these skills, often relying superficially on AI outputs. For lower-order skills, all students benefited, though those with higher integrity achieved more meaningful improvements. These findings highlight that responsible and ethically guided AI use, reinforced by strong academic integrity, is essential for fostering both foundational and advanced critical thinking in postgraduate education. The study offers theoretical and practical insights for educators, policymakers, and researchers seeking to integrate AI effectively and ethically in higher education.

The integration of Artificial Intelligence (AI) technologies in education has gained significant attention, particularly in the context of higher education, in recent years. Despite concerns about academic integrity, academics recognise the opportunity for AI to foster critical thinking and prepare students for real-world scenarios. However, its integration into courses requires careful consideration of course objectives and ethical implications. This study explores the utilisation of AI in higher education settings, focusing on its role as a learning tool. The study systematically reviewed 87 empirical studies from databases between 2014 and 2024 to investigate the benefits, challenges, and implications of incorporating AI into higher education. Additionally, it examines the potential impact of AI on teaching methodologies, student outcomes, and the overall learning experience. The findings of this study underscore the significant influence of AI integration in higher education on teaching methodologies. This integration promotes personalised and adaptive instruction, enhancing student engagement, performance, satisfaction, and overall learning experiences. However, the adoption of AI in higher education raises significant ethical concerns that demand careful consideration. These concerns include data privacy, algorithmic bias, intellectual property rights, and academic integrity. Academics' perspectives on AI adoption vary based on technological proficiency, pedagogical beliefs, and institutional support. Successful AI integration necessitates alignment with pedagogical theories such as constructivism, connectivism, and self-directed learning, ensuring a robust technical infrastructure and addressing ethical considerations to maximise benefits while minimising risks.

The integration of artificial intelligence (AI) tools in postgraduate supervision has the potential to transform research processes by enhancing the quality of feedback, improving the efficiency of supervision, and addressing persistent challenges such as time constraints and student engagement within higher education. Despite this promise, the readiness of supervisors to adopt AI remains uneven, necessitating an exploration of their preparedness to integrate such tools. This study employs a constructivist paradigm and a qualitative research approach, guided by a generic qualitative research design. It draws on semi-structured interviews with 15 postgraduate supervisors from diverse disciplines to examine their perspectives on the integration of AI tools in postgraduate supervision. Through thematic analysis, four central themes emerged: technological literacy, institutional support, perceptions of AI, and ethical considerations, revealing the complex interplay between individual competence and institutional context. Supervisors with prior experience in digital technologies or from technology-intensive fields demonstrated higher readiness, while those from non-technical backgrounds encountered challenges due to limited digital exposure and perceived complexity. Institutional factors such as digital infrastructure, supportive policies, and professional development opportunities further influenced readiness levels. However, concerns surrounding academic rigour, ethical accountability, and workload pressures continue to constrain adoption. The chapter concludes by recommending targeted capacity-building programmes, institutional policy reforms, interdisciplinary collaboration, and enhanced supervisor–student partnerships to ensure ethical and effective AI use. Ultimately, while AI tools hold significant potential to enhance supervision efficiency and personalised support, their successful implementation requires tailored strategies responsive to diverse supervisory contexts, offering valuable insights for higher education institutions seeking to promote responsible AI integration in postgraduate supervision.

This qualitative study examines the complex attitudes, ethical considerations, and practical implications of integrating artificial intelligence (AI) in academic writing across key stakeholder groups, including university professors and students. Using semi-structured interviews with 40 participants (20 students and 20 faculty members) from diverse disciplines and institutional contexts, the research reveals divergent perspectives on AI’s role in academia. Faculty respondents expressed significant concerns about academic integrity, erosion of critical thinking, and the limitations of AI detection tools, which frequently misidentify human-written text as AI-generated. Conversely, students viewed AI as an essential productivity tool for overcoming writer’s block, refining ideas, and managing workload, though they acknowledged ethical ambiguities in its deployment. A critical tension emerged between AI’s perceived benefits—enhanced efficiency, personalized feedback, and accessibility—and its risks, including algorithmic bias, surveillance culture, and threats to student agency. Stakeholders agreed that institutional policies lag behind technological adoption, with current frameworks inadequately addressing transparency, data privacy, or equitable implementation. The study also identifies disciplinary variances: STEM educators favored AI for technical drafting, while humanities faculty emphasized its threat to authentic voice development. The findings advocate for a collaborative, multi-stakeholder approach to AI governance, emphasizing pedagogical redesign, ethical guidelines for explainable AI, and professional development to bridge digital literacy gaps. This research underscores the urgency of reimagining academic writing in the AI era, balancing innovation with the preservation of core educational values.

This scholarly investigation delves into the transformative impact of Artificial Intelligence (AI) on enhancing customer experience in the business realm. The study's purpose was to meticulously examine the integration, evolution, and strategic implications of AI in business operations, particularly in customer engagement. A comprehensive literature review and detailed case study analysis constituted the core methodology, focusing on peer-reviewed articles and practical examples from diverse business sectors. This approach facilitated a multi-dimensional exploration, capturing both the technological advancements in AI and the associated implementation challenges within various business contexts. Central findings from this research underscore AI's evolution from an emerging technological tool to a fundamental component in customer-centric business strategies. AI's capabilities in personalizing customer interactions, automating support systems, and leveraging predictive analytics have revolutionized business-customer dynamics. However, this evolution is not without its challenges, including data privacy concerns, ethical considerations, and the need for skilled AI expertise. The study concludes that AI is a strategic asset, necessitating thoughtful integration into business models. It emphasizes the importance of a collaborative approach, where AI specialists and industry experts work synergistically to tailor AI solutions to specific business needs. Ethical considerations and maintaining customer trust are highlighted as pivotal in AI deployment strategies. The study recommends continuous innovation, investment in AI infrastructure and talent, and adherence to ethical AI practices. These measures are essential for businesses to enhance customer experiences and drive sustainable growth in the digital age
 Keywords: Artificial Intelligence, Customer Experience, Business Strategy, AI Integration, Ethical Considerations.