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Although the growing use of generative artificial intelligence (GAI) in higher education research offers exciting potential for enhancing innovation and efficiency, it is also troubled by significant ethical challenges. This review systematically explores critical concerns that border on risks that are associated with data privacy, algorithmic bias, transparency deficiencies, accountability gaps, and threats to academic integrity. This was done by providing a literature-based assessment of why the use GAI in higher education continues to be problematic. This assessment yielded informative insights that were deciphered from 31 papers which were systematically selected from a sampling universe of 119 publications. Findings highlight the necessity of striking a balance between embracing AI-driven advancements and upholding ethical standards in research, in a manner that does not compromise human capacities to mitigate the unexpected eventualities and their consequences. A set of actionable guidelines that are designed to enhance the responsible integration of GAI in higher education without side-lining the importance of observing critical ethical issues that must be considered and adhered to in academic research is given. These guidelines are useful because they form the foundation of a robust framework which gui, Generative, Higher Education, Researchdes the way GAI is used in higher education research without forgetting that this technology is not an all-round paragon.

This paper critically examined the impact of over-dependence on Artificial Intelligence (AI) by Information Technology (IT) Professionals, exploring the impact on cognitive functions such as critical thinking, decision-making, and problem-solving, and how these affect the autonomy of IT Professionals. Using quantitative research methodology, the study surveyed 180 IT Professionals to gauge their usage of and dependence on AI-powered tools, as well as their perceptions about AI technologies and the effects these might have on their cognitive abilities. The findings showed a very significant integration of AI by the respondents in both their personal and professional capacities, as well as a substantial dependence on AI-powered tools, particularly for decision-making purposes. Furthermore, there appeared to be a possible underestimation of the effects that AI usage has on cognitive abilities, as expressed through the paradoxical survey results. Moreover, several ethical issues were identified such as bias, privacy and the lagging behind of laws and regulations, further serving to complicate the effects of over-dependence on AI. Based on its findings this paper makes recommendations for the development of clear guidelines for the use of AI, continuous learning and development programs, focusing on AI advancements and risks, as well as the implementation of frequent impact and security assessments. These recommendations aim to assist organisations and IT Professionals to benefit from the advantages of AI while maintaining critical skills, human oversight and intuition.

Artificial intelligence (AI) is fundamentally transforming accounting practice by displacing traditional data-processing tasks, augmenting auditing functions, and reshaping the professional competency requirements of financial reporting. While these technological developments generate substantial efficiency gains, they simultaneously introduce unprecedented ethical challenges, including algorithmic opacity, accountability diffusion, bias propagation in automated judgment, and the erosion of professional scepticism — that threaten the integrity of financial reporting and the public trust upon which the accounting profession's social licence to operate ultimately depends. This paper develops a rigorous, theoretically grounded analysis of the intersection between artificial intelligence and accounting ethics, drawing on institutional theory, agency theory, virtue ethics, and the stakeholder trust framework to diagnose how AI adoption reconfigures the ethical architecture of financial reporting and to prescribe the governance, professional, and regulatory responses required to safeguard ethical standards and trust in this transformed environment. The paper makes three original contributions: first, it develops a multi-dimensional taxonomy of AI-related ethical risks in accounting, distinguishing technical, professional, and systemic risk categories; second, it proposes the Ethical AI Accounting Governance Framework (EAAGF), an original implementation model that integrates algorithmic governance, professional ethics renewal, regulatory adaptation, and stakeholder assurance mechanisms; and third, it situates these contributions within a comparative empirical analysis of AI adoption experiences across major accounting contexts, integrating evidence from surveys, regulatory reports, and case illustrations. The paper argues that the central challenge is not whether AI will transform accounting — this is already occurring — but whether the profession, regulators, and organisations can collectively construct the ethical governance infrastructure required to ensure that transformation advances rather than undermines the accountability, transparency, and trust that constitute the profession's foundational social purpose.

Artificial intelligence (AI) is fundamentally transforming accounting practice by displacing traditional data-processing tasks, augmenting auditing functions, and reshaping the professional competency requirements of financial reporting. While these technological developments generate substantial efficiency gains, they simultaneously introduce unprecedented ethical challenges, including algorithmic opacity, accountability diffusion, bias propagation in automated judgment, and the erosion of professional scepticism — that threaten the integrity of financial reporting and the public trust upon which the accounting profession's social licence to operate ultimately depends. This paper develops a rigorous, theoretically grounded analysis of the intersection between artificial intelligence and accounting ethics, drawing on institutional theory, agency theory, virtue ethics, and the stakeholder trust framework to diagnose how AI adoption reconfigures the ethical architecture of financial reporting and to prescribe the governance, professional, and regulatory responses required to safeguard ethical standards and trust in this transformed environment. The paper makes three original contributions: first, it develops a multi-dimensional taxonomy of AI-related ethical risks in accounting, distinguishing technical, professional, and systemic risk categories; second, it proposes the Ethical AI Accounting Governance Framework (EAAGF), an original implementation model that integrates algorithmic governance, professional ethics renewal, regulatory adaptation, and stakeholder assurance mechanisms; and third, it situates these contributions within a comparative empirical analysis of AI adoption experiences across major accounting contexts, integrating evidence from surveys, regulatory reports, and case illustrations. The paper argues that the central challenge is not whether AI will transform accounting — this is already occurring — but whether the profession, regulators, and organisations can collectively construct the ethical governance infrastructure required to ensure that transformation advances rather than undermines the accountability, transparency, and trust that constitute the profession's foundational social purpose.

Although the growing use of generative artificial intelligence (GAI) in higher education research offers exciting potential for enhancing innovation and efficiency, it is also troubled by significant ethical challenges. This review systematically explores critical concerns that border on risks that are associated with data privacy, algorithmic bias, transparency deficiencies, accountability gaps, and threats to academic integrity. This was done by providing a literature-based assessment of why the use GAI in higher education continues to be problematic. This assessment yielded informative insights that were deciphered from 31 papers which were systematically selected from a sampling universe of 119 publications. Findings highlight the necessity of striking a balance between embracing AI-driven advancements and upholding ethical standards in research, in a manner that does not compromise human capacities to mitigate the unexpected eventualities and their consequences. A set of actionable guidelines that are designed to enhance the responsible integration of GAI in higher education without side-lining the importance of observing critical ethical issues that must be considered and adhered to in academic research is given. These guidelines are useful because they form the foundation of a robust framework which gui, Generative, Higher Education, Researchdes the way GAI is used in higher education research without forgetting that this technology is not an all-round paragon.

This paper critically examined the impact of over-dependence on Artificial Intelligence (AI) by Information Technology (IT) Professionals, exploring the impact on cognitive functions such as critical thinking, decision-making, and problem-solving, and how these affect the autonomy of IT Professionals. Using quantitative research methodology, the study surveyed 180 IT Professionals to gauge their usage of and dependence on AI-powered tools, as well as their perceptions about AI technologies and the effects these might have on their cognitive abilities. The findings showed a very significant integration of AI by the respondents in both their personal and professional capacities, as well as a substantial dependence on AI-powered tools, particularly for decision-making purposes. Furthermore, there appeared to be a possible underestimation of the effects that AI usage has on cognitive abilities, as expressed through the paradoxical survey results. Moreover, several ethical issues were identified such as bias, privacy and the lagging behind of laws and regulations, further serving to complicate the effects of over-dependence on AI. Based on its findings this paper makes recommendations for the development of clear guidelines for the use of AI, continuous learning and development programs, focusing on AI advancements and risks, as well as the implementation of frequent impact and security assessments. These recommendations aim to assist organisations and IT Professionals to benefit from the advantages of AI while maintaining critical skills, human oversight and intuition.