Instructing Higher Education in the Era of Generative AI: Implications for Managerial Decision-Making, Business Ethics, and Workforce Readiness

Corresponding Author Email: ramya.dvr@gmail.com

DOI : https://doi.org/10.51470/BITS.2026.05.01.76

Abstract

Download this article as:

1. Introduction

The emergence of GenAI in the last couple of years has revolutionized how learning happens in higher education institutions. Previous talks about artificial intelligence in higher education (AIHE) were mainly geared towards discussing how AIHE could enable personalization of education, teacher assistance, and increase efficiency in the process by utilizing intelligent systems [1]. The advent of the integration of AIHE in the field of education led to a change in focus from experimental research about AI technology itself to scholarly deliberation about pedagogies, student engagement, and educator involvement [2]. This change has been expedited by the arrival of platforms such as ChatGPT that provide learners and educators alike with readily available solutions for creating written content through AI writing, summaries, idea generation, assessments, code generation, and research assistance. Therefore, the use of GenAI is not only limited to being a learning tool for learners but rather an essential component of the entire process of learning itself.

As evidenced through current scholarship, HEIs continue to grapple with opportunities and risks brought about by GenAI. AI tools can assist in learning flexibility, academic productivity, personalized feedback, and information exploration. However, these tools raise concerns about authenticity, dependence, unequal access, and the decline of the independent intellectual effort. The field as a whole indicates that AI is now mainstreaming into the education context in higher education; however, there is still variation in policies, pedagogies, and ethics at the institution level [3]. Students themselves acknowledge the dual nature of GenAI; they find it helpful for efficiency, for developing ideas, and for supporting learning, but have concerns about accuracy, being treated fairly, academic integrity, and a lack of critical engagement [4].

While it is important for GenAI in the context of academic learning, it also plays a significant role in the preparation and development of future managers, entrepreneurs, administrators, policymakers and organizational decision makers within higher education. Business and management science is directly involved with the development of analytical skills, ethical judgment, communication skills and decision-making competence among individuals. While all of these capabilities can be aided by large language models, they need to be used critically, instead of passively, by the learners for them to be of value [5]. Specifically, there are implications for management education, human capital development, business ethics, and workforce readiness.

While it holds tremendous promise, GenAI poses several unsolved problems for higher education and a business focus on learning. These include an impact on critical thinking, lack of clarity regarding authorship and academic integrity, ethical issues of using AI in the academic context, access disparities to digital resources, skills deficits and uneven preparedness for AI-driven workplaces. As the use of GenAI becomes more commonplace in education, the importance of guiding this technology through transparency, accountability, inclusion, and human oversight is gaining greater attention in policy discussions, which is more than just a matter of technology [6]. Recent studies also indicate that evidence regarding the impact of ChatGPT and the like on learning quality, assessment practices, institutional policies, and learning outcomes has not been fully conclusive [7].

This review article analyses the impact of GenAI on managerial decision-making as well as on the ethics of business and on the preparedness of the workforce in higher education. It maintains that proper use of GenAI can enhance analytical thinking, ethical consciousness, and professional development, but the improper and unequal use of GenAI can widen inequalities in analytical, ethical, and employability skills.

2. Conceptual Foundation and Analytical Framework

The study defines generative artificial intelligence (GenAI) as a technology that can be used in education and as a technology that affects human capital, managerial cognition, ethical behavior, and organizational readiness in the future. In an era where AI is reshaping job and role requirements, higher education is increasingly playing a pivotal part in cultivating tomorrow’s workforce for roles that are AI-driven and professional or managerial [8]. In this context, GenAI refers to AI systems that are able to generate content, explanations, summarizations, ideas, analyses, and recommendations. Higher education is universities and professional learning institutions that produce higher orders of knowledge and work-readiness related skills. Managerial decision-making is the process of examining information, defining a problem, considering alternatives, estimating risks, and choosing appropriate action in organizational settings. Business ethics is defined as transparency, accountability, fairness, responsibility and integrity in business practices. Workforce readiness is defined as graduates’ preparedness to work in today’s environments by being digitally, analytically, ethically, communicatively, and adaptively prepared to do so. Organizational readiness is the capacity of organizations and institutions to embrace, manage, and profit from new technologies with knowledge and ethics, and with a management structure that is capable of adaptation and innovation [9].

The analytical approach of this article is provided in Figure 1. It suggests that GenAI in higher education will revolutionize the production, interpretation and evaluation of knowledge. This change has an impact on the evolution of critical thinking, ethical decision-making and digital literacy. The competencies then feed into managerial decision-making, business ethics and workforce readiness, which collectively support organisational readiness. The framework also acknowledges the automation-augmentation paradox, which is defined as the ability of AI to take over cognitive tasks that can be automated, but also by augmenting human judgment, reasoning and managerial problem solving when done responsibly [10].

There are four key dimensions of the framework. First, AI-mediated knowledge development outlines the role of GenAI in knowledge access, content creation, explanation, and the efficiency of learning, and highlights potential issues of superficial learning and dependency. Second, managerial decision-making skills underscore the need for students to critically analyse the AI- produced content, to understand bias and to consider sources of evidence and make decisions in complex business scenarios. Third, ethical and governance readiness is linked to responsible use of AI in HE institutions and future business ethics and transparency, accountability and good corporate governance. Fourth, workforce and human capital readiness focus on ensuring graduates are equipped with the skills of AI literacy, adaptability, analytical skills and ethical awareness to help organizations perform and add value to the business [11]. However, the benefits of GenAI can be uneven when students have varying access and institutional support, digital resources, and learning environments, which can exacerbate educational inequality to workforce-readiness inequality [12].

3. Generative AI and the Transformation of Higher Education

The advent of generative AI has reshaped the landscape and experience of higher education, offering new avenues to learn, create, and share knowledge. GenAI systems offer more advantages than previous digital tools that primarily facilitated information retrieval or online communication, and can produce explanations, draft written text, summarise complex information, facilitate brainstorming, and assist in research-related tasks. This has shifted the engagement process from being human-centred to being AI-centred and given students the opportunity to engage with the machine-generated responses as part of their learning. The use of ChatGPT and similar technologies reflects the advantages and disadvantages of this shift from the opportunity for personalized content generation and the speed of information access to issues such as concerns for accuracy, overreliance, ownership, and academic integrity [13]. AI in education reflects not an advancement but a transformation in the way students write, create ideas, study, and learn.

GenAI’s impact extends to academic writing and research assistance, transforming the higher education landscape. Students are increasingly using AI to create outlines, enhance their arguments, summarize texts, convert concepts into formal academic writing, and prepare for evaluations. These practices will increase the learning efficiency and confidence of students, especially the ones requiring more academic assistance. Given the quick adoption of ChatGPT in education, however, its pedagogical utility is contingent on how educators use it, the students’ critical thinking regarding outputs, and the institutions’ clear guidance in the responsible use of ChatGPT [14]. The introduction of GenAI provides the dual opportunity of it being a learning partner to guide exploration and reflection, or a shortcut that undermines independent thinking and meaningful participation in academic activities.

One of the key factors in this change is the role of faculty. Teachers are no longer in a position to be a mere content provider, or final product evaluator. With AI being increasingly used in education it is expected for teachers to now create learning environments that are AI-aware, critically engage with AI and support the use of AI in learning responsibly. Faculty must restructure course assignments, assessment methods and activities to prompt students to question, validate and ethically use AI-generated content. This involves changing a focus on product evaluation towards process-focused learning, wherein students show how they think, decide and support their ideas and their thinking. This will inevitably require pedagogical approaches to effectively incorporate AI tools into the educational context, as well as AI tools themselves, to shape the future of education [15].

Of all areas of higher education, assessment is one that is most impacted. Traditional writing assignments are now easily generated by AI and it is hard to know if the assignments submitted are genuine student understanding. This has sparked considerable discussion on the potential of ChatGPT and others to spur innovation in education or to pose a challenge to traditional academic assessment [16]. To meet this requirement, institutions need to reimagine the design of assessment, applying a variety of formats such as oral defenses, reflections, writing, analysis of cases, applied projects, in-class work and critique of AI-produced work. This can in fact, be an effective strategy to ensure the continuity of academic rigor while also recognizing the presence of GenAI in today’s learning and professional life.

Universities must craft policies and train faculty for AI use, establish ethical guidelines, and incorporate these changes into curricula to ensure a harmonious relationship between innovation and academic accountability. The challenges and opportunities of GenAI in higher education highlight the need for a shifting of focus from reacting to restrictions to developing a structured approach to its use, to ensure it is transparent, accountable, and inclusive [17]. This encompasses defining what acceptable use of AI is, the need for transparency when it applies, digital literacy and awareness of the use of AI outputs. The experience of the implementation of digital learning during the COVID-19 period also demonstrates that the acceptance of educational technologies by students is not only related to the perceived usefulness of the educational technologies, ease of use, institutional support, and the learning conditions, but these are also relevant for the adoption of GenAI by students.

The revolution of higher education due to GenAI must be seen from a business and management perspective as a part of the development of human capital. The purpose of higher education is to equip graduates for success in academic settings, as well as in the professional, organizational and managerial arenas. With the growing adoption of AI-driven systems in the workplace, graduates need to have the ability to work with digital tools, critically evaluate information, communicate effectively, and make ethical decisions. With the proper educational context, GenAI can enhance these skills, including through the inclusion of AI literacy, responsible AI usage, analytical thinking, and learning in a work environment. Without the creation of coherent policies and pedagogical practices, however, the integration of AI can lead to differential results at the country, institutional and student level. Higher education systems are making varying responses to these challenges, and there is evidence of variations in preparedness, governance and educational practice across the globe in relation to institutional GenAI policies [18]. In doing so, GenAI is changing the landscape of higher education from a knowledge delivery system to a more intricate arena where graduates are equipped with the skills and knowledge to navigate the complexities of the future business and organizational environment while remaining AI-literate and ethically responsible. Table 1 summarizes the key shifts enabled by GenAI in the context of higher education, and how these relate to the learning of business and management.

Bottom of Form

4. Knowledge Transformation and Management Learning

Generative AI is reshaping knowledge production in higher education in ways that impact the creation, interpretation, communication and evaluation of information. The process of knowledge production is not limited to human reading, thinking, writing, talking, and discussing anymore—it now involves engaging with AI which can explain, synthesize, draft, compare, and organize complex ideas. This transition is part of a major and more general social change where AI is increasingly shaping the processes of economy, education and organizations, and thus how people interact with knowledge and use it in real-world settings [19]. In higher education, this implies that students are not just learning about a subject, but also how to use and understand information created by AI. The central educational question is no longer simply whether GenAI should be used, but how it should be used to train learners to critically and ethically assess its outputs professionally.

The change is particularly important in the field of management learning as academic knowledge acquired at universities is frequently the basis of managers’ judgment, business analysis, strategic thinking and problem solving within the organization. In the business world, knowledge is valuable if it is used to diagnose issues, evaluate options, interpret information, convey information, and make decisions in the face of uncertainty. AI is already impacting business practice through analysis, prediction, automation and decision-making but there are many issues to address in moving from a vision to a reality [20]. Addressing this gap is one possible way that higher education can contribute and can be done through the creation of graduates who not only possess the knowledge of AI tools but possess the knowledge and skills to use them in an intelligent, thoughtful, and meaningful way within an organizational context. Knowledge needs to be more than theoretical knowledge; however, for future managers, it needs to be transformed into actionable knowledge that can help them make responsible decisions.

There are substantial dangers in AI-driven knowledge. Students may not engage critically, especially if using GenAI for summaries, explanations or written documents and idea generation. This can hinder originality and strengthen dependency and a dependence on machine-based outputs, whereas originality and autonomous thought might otherwise be nurtured. Furthermore, there is also uncritical consumption of responses, whereby an AI generated response is presented and accepted without students questioning the underlying evidence, assumptions, context or bias of the given response. Trust is hence a key aspect in AI-supported knowledge environments. It is important to recognize that AI systems are neither neutral nor definitive sources of truth and that they need to be verified, be transparent and need human judgment [21]. This is especially critical in the field of management education, where decisions made based on incorrect or misleading AI-generated information can impact organizational performance, ethical practices, and business decisions.

The recent educational debates indicate that the use of GenAI is shifting the way learning is expected, researched, and used in the classroom, while also impacting policies, practices, and the way learning is taught. Artificial Intelligence tools like ChatGPT and Midjourney have presented a new frontier for students’ creativity and productivity in the academic world, while simultaneously demanding new levels of digital literacy and institutional oversight [22]. It is in this context that knowledge transformation needs to be seen as an opportunity and a responsibility. Students should be taught to challenge AI-based content, cross-check it against reliable information, recognise its scope of limitations, and apply it to particular academic or business issues. In the absence of such guidance, GenAI can be an efficient tool without any deep learning.

Meanwhile, GenAI brings in a world of opportunities for management learning. It can be used to assist business case analysis, allowing students to consider various viewpoints, consider variables and compare possible options. It can help with decision-support learning, allowing students to consider alternative possibilities, test out hypotheses, and look at potential ramifications. It can also be used to improve scenario planning, data interpretation and professional communication for learners by providing them with the opportunity to act out workplace situations, write business reports, make presentations and construct managerial arguments. In education, collective reflections indicate that the potential of GenAI for the future is linked to its application as a tool that facilitates expanded inquiry, creativity and critical engagement in education, rather than merely automating educational tasks [23].

GenAI can be especially beneficial in business and higher education, especially when embedded in real-world, problem-based, and work-based learning. Evidence from the focus group with business higher education suggests that GenAI can facilitate various learning activities, including case discussion, business writing, research support, and decision-making. This will be accompanied by the need to be mindful of ethics, accuracy, and dependence on the system by students [24]. So, the goal of knowledge transformation in the GenAI era must be to focus on management learning, both technically and ethically. It is not about taking the place of managerial thinking with AI-generated content, but about training the new generation of professionals who can analyze, communicate and make responsible decisions with the support of AI.Top of Form

Bottom of Form

5. Critical Thinking and Managerial Decision-Making

5.1 Critical Thinking as a Management Competency

The ability to think critically is essential for managers since they need to analyze evidence, interpret complicated information, manage risks, and make decisions in situations characterized by uncertainty. In light of the growing use of AI in decision-making, managers need to understand how AI creates such insights, identify the limitations of AI, and find ways to interpret them. Although the use of AI may be useful in analyzing data, making forecasts, and processing information, its effectiveness depends on human ability to analyze results [25].

5.2 GenAI and Managerial Cognition

GenAI has an impact on managerial cognition such as problem framing, information processing, bias identification, and assessment of alternative solutions. It helps trainees develop explanations, determine causality, compare strategic options, and think from alternate viewpoints. However, decision-making within organizations requires a combination of human-AI collaboration where AI brings its computing power and pattern recognition skills, and humans bring their experience, ethics, context, and responsibility [26].

5.3 Benefits for Decision-Making Skills

In higher education, GenAI will have the capacity to improve decision-making skills using methods like brainstorming, scenario planning, evaluation of alternatives, business communication, and problem-solving techniques. In business management courses, students could use GenAI to analyze different aspects of business cases, evaluate options, identify risks, and develop managerially sound arguments. This approach could improve strategic thinking, as it would force students to provide rationales for their decisions instead of simply accepting AI-generated suggestions. The hybrid approach to solving problems allows AI to complement the analytical capability of humans, but leaves the latter in charge of interpreting results and making decisions [27]. The same applies to GenAI, as it stimulates critical thinking when students evaluate its validity, assumptions, limitations, and possible biases [28]. The collaborative application of GenAI can also encourage higher-level learning through the exchange and evaluation of AI’s insights [29].

5.4 Risks to Independent Judgment

However, despite its various benefits, GenAI can become a threat to independent decision-making when students opt for a passive reliance on AI-generated material. Such an approach may weaken the capability of the student to think analytically and creatively, take personal responsibility, and trust their reasoning skills. Algorithmic bias and insufficient information about GenAI can also pose problems regarding the assessment of the validity of the recommendations provided by artificial intelligence. The successful implementation of AI-assisted decision-making systems requires a proper equilibrium between technological capabilities, human abilities, organizational readiness, and governance that ensures accountability [30]. Therefore, the current higher education system needs to provide students with the skills and competences to use GenAI as an auxiliary decision-making tool without delegating the decision itself. Table 2 outlines key GenAI competences required for managers.

6. Academic Integrity, Business Ethics, and Responsible AI Use

With the emergence of Generative AI, academic integrity has become more complex, as it has introduced new concepts that have raised questions about plagiarism, authorship, originality, and acceptable academic support. Students can use GenAI to develop ideas, draft text, summarize sources, revise language, or complete an assignment, which can make it difficult to determine legitimate assistance from misrepresented authorship. Academic integrity in the GenAI era needs to be more than just plagiarism detection, and should involve disclosure, transparency, ethical judgement and acceptable usage guidance from the institution [31].

Higher education institutions’ responsibility in promoting AI use is intertwined with business ethics, as ethical practices developed during education influence the ethical practices of business professionals in the future. When students are taught to use AI tools ethically, they become responsible for the content they produce, the sources they cite, and the accuracy of the information they use. Business reporting, managerial communication, strategic analysis and organisational decision-making, however, may be negatively impacted by the use of normalized undisclosed AI or unverified AI-generated content in the future [32].

Academic integrity and ethics are also linked to corporate governance and ethics when it comes to AI. AI usage needs to be transparent, accountable, compliant, fair, and subject to human oversight. The discussions around AI ethics internationally highlight that AI is to aid in the promotion of dignity, responsibility, inclusiveness and social benefit [33]. There are also other ethical principles that are emphasized: the principles of beneficence, non-maleficence, autonomy, justice, and explicability are principles for responsible use of technology [34].

The use of GenAI must be transparent and explainable. Principles are key to trustworthy AI, and are not only used to inform individual action, but also institutional decision-making and control, such as accountability, fairness, and responsible human control [35]. At the college level, this implies supporting students in acknowledging the use of AI, fact-checking information, differentiating their thinking from AI-provided advice, and documenting the use of AI.

However, ethical guidelines alone are insufficient. Many principles outline general guidelines for AI ethics, but few provide specific guidance for implementation, monitoring, or accountability [36]. Practical approaches that can be adopted in institutions to foster responsible use of AI tools include disclosure statements, source verification, exercises to reflect on the use of AI, writing to explain reasoning, giving students feedback on fairness, bias, accountability, and professional responsibility, and engaging in classroom discussion. Academic integrity concerns also highlight the importance of the universities’ response involving clear policies and transparency, educating students, changing assessment, and ensuring shared responsibility for academic integrity between the student and teacher [37]. Therefore, higher education institutions must train students to steer clear of problematic use of AI, but also ensure they are able to use AI ethically in business and organizational settings.

7. Digital Inequality, Human Capital, and Workforce Readiness

Generative AI use in higher education raises significant concerns about digital inequality as access to AI tools relies on institutional resources, regional development, socio-economic background, infrastructure, language and digital literacy. Students in well-resourced universities may have better internet connectivity, advanced AI tools, institutional training, faculty support, and informal learning opportunities than students in under-resourced contexts. Therefore, digital inequality is not only about access to the Internet, but also about differences in access to skills, quality of access and how this access is translated into education and career results [38].

The digital divide is no longer limited to physical access; it also includes inequalities in material resources, skills, and meaningful use. Internet connectivity does not ensure that every student has access to optimal devices, software, operating systems, or institutional assistance, or feels comfortable with using digital tools. The advanced use of AI tools, which demands a stable Internet connection, English language knowledge, paid subscriptions and critical thinking, further restricts the ability of disadvantaged students to acquire skills connected to AI in the GenAI era [39].

The COVID-19 pandemic demonstrated the role that digital dependency can play in exacerbating educational inequities, particularly for students who lack access to a reliable device, connectivity, study environment, or digital support [40]. This may play out in the same way with GenAI use: those students who have better access to and guidance on the tools learn more about digital skills, analytical skills, professional self-confidence, and work readiness than those who do not have access to and guidance on the tools.

The new requirement for workforce readiness is literacy in AI. It contains knowledge of artificial intelligence capabilities and limitations as well as the use and misuse of tools, assessment of outputs, the identification of bias and the use of human judgment in tasks supported by technology [41]. In the higher education sector, these skills have strong connections with training the next generation of business, administration, communication, financial, analytical, and human resource management professionals for work with AI. Alongside this, learners should possess cognitive, affective and sociocultural capacities for meaningful engagement with AI technologies [42].

Imbalances in AI readiness can impact organizations in multiple ways, such as recruitment challenges, increased training expenses, and disparities in innovation potential. Firms that have employees who are AI-ready can benefit from improved productivity, decision support and adaptability, while those who can’t have reskilling challenges. Labor research worldwide indicates that the impact of GenAI is likely to be multifaceted, affecting not only the number but also the nature of jobs, the geographical distribution of employment opportunities, and the skills of the workforce [43]. The future of work demands analytical thinking, technological literacy, resilience, creativity, leadership and lifelong learning abilities, which all depend on equitable access to GenAI for employability and human capital development [44].

8. Practical Implications for Higher Education, Firms, and Policymakers

The adoption of generative artificial intelligence technology in the higher education landscape has several practical implications for universities, business schools, organizations, managers and policymakers. Higher education organizations must redefine their curriculum to ensure that students acquire skills related to AI knowledge, critical thinking, ethics, and digital know-how during their graduate training programs. Policies on the responsible use of AI need to outline what is permissible and forbidden. Evaluation must shift towards oral evaluations, reflections on AI output, problem-solving tasks based on cases, and evaluation of reasoning processes rather than just written output. Faculty development becomes a necessity since teachers need to be trained in handling AI ethically, redesigning teaching techniques, and helping their students learn with ethical AI support. Such a shift reflects the growing need for reliable AI based on transparency, accountability, and ethics [45].

GenAI can be integrated within management studies in business schools and management educators through the use of AI-driven case study analyses, business decisions, simulations, ethical discussions of AI, and other workplace evaluations. The intention behind this is to ready students for future environments where artificial intelligence is more often part of communication, analysis, strategic planning, and operations in their work. Ethical considerations also require that the integration of AI and data within learning environments ensure learner protection and proper AI usage [46]. Firms and their managers need to consider recruiting and training those who can work with AI technology, which requires skills, judgment, and accountability. The organization needs to train its employees, ensure accountable decision-making support, adopt responsible AI governance, and foster ethical AI behavior as the labor market evolves due to automation and digitalization [47; 48].

The use of AI in education should not be allowed to increase the already existing inequality gap. Some of the policies that should be adopted include promoting digital inclusion, providing accessible means for educational technology, formulating organizational policies for the use of AI, capacity-building, and the creation of nationwide AI literacy campaigns. Technology should support inclusive education rather than perpetuating the divide [49]. Policies on responsible AI use in organizations need to foster decent work, social justice, and equal opportunities in AI-based economies [50]. The main challenges that arise from integrating GenAI, the impact on business and management education, and the proposed solutions are provided in Table 3.

9. Future Research Directions

In future research, the role of GenAI in shaping managerial decision-making capabilities among college students must be explored, especially with respect to the development of critical analytical skills, problem-solving, sound judgment, and strategic thinking. Studies may test whether GenAI helps students make better comparisons, weigh evidence, and take informed business decisions, or whether the use of GenAI promotes dependence on automatically generated information. Research may also focus on ways that GenAI could be better leveraged through assessment techniques like case analysis, reflective writing, viva voce, and problem solving [51]. Another area worth investigating is business ethics education with the inclusion of GenAI. Research may explore the effects of transparency in AI usage, ethics education, and source verification. Future studies could investigate the understanding of students regarding their perception of authorship, accountability, transparency, and fairness as they use GenAI to accomplish academic and professional assignments, in addition to investigating the approaches by which universities could strike a balance between innovation and academic integrity, not to mention the authenticity of the educational process [52]. It would also be valuable to study the relationship between AI literacy and employability, specifically in terms of how improved levels of AI literacy relate to improved readiness for work and professional management capabilities [53]. Future research would benefit from looking at comparative analyses to study how differences in the availability of GenAI tools impact human capital formation. Furthermore, researchers need to explore the area of faculty preparedness and curricula reform, including teacher efforts to change their teaching practices, assessment procedures, and policy-making regarding the use of AI. Measurement of student AI literacy would facilitate such a research agenda [54].

10. Conclusion

The impact of Generative AI on higher education is not just about education but on preparing students for business and management as well. This review has stated that GenAI should not be seen as just a writing aid or a summarization tool, but as a potent transformative tool that has an impact on knowledge creation, critical thinking, ethical thinking, digital literacy, and job readiness. The responsible integration of GenAI is crucial as higher education institutions train the next generation of managers, entrepreneurs, administrators, policymakers, and business professionals for an AI-enabled world. The article emphasizes the value of GenAI for management learning in generating ideas, analyzing business cases, planning scenarios, helping in decision-support learning, and learning about professional communication. But the benefits are only there if students can be trained to use AI critically and ethically. Overreliance on GenAI or being passive can diminish critical thinking, originality, attribution, and AI-generated confidence. Thus, there is a need for higher education institutions to develop curricula, assessments, and policies to reinforce and not replace human judgment. Academic honesty, business ethics, and responsible use of AI are interrelated, which is also highlighted in the review. The ethical practices gained in the university will have an impact on the conduct of the professions, corporate governance, transparency, and accountability in decision-making in the future. Likewise, the unequal access to GenAI can further exacerbate human capital deficits and workforce inequality in the event that some students gain deeper AI skills and others get left behind in digital poverty. Overall, GenAI in higher education is both an educational and management concern. It can improve managerial decision-making, ethical leadership, employability, organisational preparedness when used responsibly, and cognitive, ethical and workforce-related difficulties when used irresponsibly.

Bottom of Form

References

1. Popenici, S. A., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1), 22.
2. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education–where are the educators?. International journal of educational technology in higher education, 16(1), 39.
3. Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: the state of the field. International Journal of Educational Technology in Higher Education, 20(1), 1-22.
4. Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1), 43.
5. Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., … & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and individual differences, 103, 102274.
6. Holmes, W., & Miao, F. (2023). Guidance for generative AI in education and research. UNESCO Publishing.
7. Baig, M. I., & Yadegaridehkordi, E. (2024). ChatGPT in the higher education: A systematic literature review and research challenges. International Journal of Educational Research, 127, 102411.
8. Bessen, J. (2018). Artificial intelligence and jobs: The role of demand. In The Economics of Artificial Intelligence: An agenda (pp. 291-307). University of Chicago Press.
9. Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., … & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994.
10. Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192-210.
11. Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2022). Artificial intelligence and business value: A literature review. Information Systems Frontiers, 24(5), 1709-1734.
12. Kyere, E., Hong, S., & Gentle-Genitty, C. S. (2023). Mediational effect of teacher-based discrimination on academic performance: An intersectional analysis of race, gender, and income/class. Education Sciences, 13(4), 387.
13. Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), 15.
14. Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), 410.
15. Grassini, S. (2023). Shaping the future of education: Exploring the potential and consequences of AI and ChatGPT in educational settings. Education Sciences, 13(7), 692.
16. Rudolph, J., Tan, S., & Tan, S. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?. Journal of Applied Learning & Teaching, 6(1), 342-363.
17. Michel-Villarreal, R., Vilalta-Perdomo, E., Salinas-Navarro, D. E., Thierry-Aguilera, R., & Gerardou, F. S. (2023). Challenges and opportunities of generative AI for higher education as explained by ChatGPT. Education sciences, 13(9), 856.
18. Jin, Y., Yan, L., Echeverria, V., Gašević, D., & Martinez-Maldonado, R. (2025). Generative AI in higher education: A global perspective of institutional adoption policies and guidelines. Computers and Education: Artificial Intelligence, 8, 100348.
19. OECD. (2019). Artificial intelligence in society. OECD Publishing.
20. Ransbotham, S., Kiron, D., Gerbert, P., & Reeves, M. (2017). Reshaping business with artificial intelligence: Closing the gap between ambition and action. MIT Sloan Management Review, 59(1).
21. Siau, K., & Wang, W. (2018). Building trust in artificial intelligence, machine learning, and robotics. Cutter Business Technology Journal, 31(2), 47.
22. Chiu, T. K. (2024). The impact of Generative AI (GenAI) on practices, policies and research direction in education: A case of ChatGPT and Midjourney. Interactive Learning Environments, 32(10), 6187-6203.
23. Bozkurt, A., Xiao, J., Lambert, S., Pazurek, A., Crompton, H., Koseoglu, S., … & Jandrić, P. (2023). Speculative futures on ChatGPT and generative artificial intelligence (AI): A collective reflection from the educational landscape. Asian Journal of Distance Education, 18(1).
24. Huo, X., & Siau, K. L. (2024). Generative artificial intelligence in business higher education: A focus group study. Journal of Global Information Management (JGIM), 32(1), 1-21.
25. Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International Journal of Information Management, 48, 63-71.
26. Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577-586.
27. Raisch, S., & Fomina, K. (2025). Combining human and artificial intelligence: Hybrid problem-solving in organizations. Academy of Management Review, 50(2), 441-464.
28. Lee, C. C., & Low, M. Y. H. (2024). Using genAI in education: The case for critical thinking. Frontiers in Artificial Intelligence, 7, 1452131.
29. Ruiz-Rojas, L. I., Salvador-Ullauri, L., & Acosta-Vargas, P. (2024). Collaborative working and critical thinking: Adoption of generative artificial intelligence tools in higher education. Sustainability, 16(13), 5367.
30. Marocco, S., Barbieri, B., & Talamo, A. (2024). Exploring facilitators and barriers to managers’ adoption of AI-based systems in decision making: A systematic review. AI, 5(4), 2538-2567.
31. Cotton, D. R., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228-239.
32. Bin-Nashwan, S. A., Sadallah, M., & Bouteraa, M. (2023). Use of ChatGPT in academia: Academic integrity hangs in the balance. Technology in Society, 75, 102370.
33. UNESCO. (2022). Recommendation on the ethics of artificial intelligence. UNESCO.
34. Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., … & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689-707.
35. Floridi, L., & Cowls, J. (2022). A unified framework of five principles for AI in society. Machine learning and the city: Applications in architecture and urban design, 535-545.
36. Hagendorff, T. (2019). The ethics of AI ethics: An evaluation of guidelines. arXiv preprint arXiv:1903.03425.
37. Perkins, M. (2023). Academic integrity considerations of AI large language models in the post-pandemic era: ChatGPT and beyond. Journal of University Teaching and Learning Practice, 20(2), 1-24.
38. Scheerder, A., Van Deursen, A., & Van Dijk, J. (2017). Determinants of Internet skills, uses and outcomes. A systematic review of the second-and third-level digital divide. Telematics and Informatics, 34(8), 1607-1624.
39. Van Deursen, A. J., & Van Dijk, J. A. (2019). The first-level digital divide shifts from inequalities in physical access to inequalities in material access. New Media & Society, 21(2), 354-375.
40. Beaunoyer, E., Dupéré, S., & Guitton, M. J. (2020). COVID-19 and digital inequalities: Reciprocal impacts and mitigation strategies. Computers in Human Behavior, 111, 106424.
41. Long, D., & Magerko, B. (2020, April). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI conference on human factors in computing systems (pp. 1-16).
42. Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041.
43. Gmyrek, P., Berg, J., & Bescond, D. (2023). Generative AI and jobs: A global analysis of potential effects on job quantity and quality. ILO Working Paper 96.
44. World Economic Forum. (2023). The future of jobs report 2023. World Economic Forum.
45. European Commission. (2019). Ethics guidelines for trustworthy AI. High-Level Expert Group on Artificial Intelligence.
46. European Commission. (2022). Ethical guidelines on the use of artificial intelligence and data in teaching and learning for educators. Publications Office of the European Union.
47. World Economic Forum. (2020). The future of jobs report 2020. World Economic Forum.
48. OECD. (2023). OECD employment outlook 2023: Artificial intelligence and the labour market. OECD Publishing.
49. UNESCO. (2023). Global education monitoring report 2023: Technology in education: A tool on whose terms? UNESCO.
50. International Labour Organization. (2024). Artificial intelligence and the world of work: Advancing decent work and social justice. ILO.
51. Lye, C. Y., & Lim, L. (2024). Generative artificial intelligence in tertiary education: Assessment redesign principles and considerations. Education Sciences, 14(6), 569.
52. Xia, Q., Weng, X., Ouyang, F., Lin, T. J., & Chiu, T. K. (2024). A scoping review on how generative artificial intelligence transforms assessment in higher education. International Journal of Educational Technology in Higher Education, 21(1), 40.
53. Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022). Artificial intelligence literacy in higher and adult education: A scoping literature review. Computers and Education: Artificial Intelligence, 3, 100101.
54. Carolus, A., Koch, M. J., Straka, S., Latoschik, M. E., & Wienrich, C. (2023). MAILS-Meta AI literacy scale: Development and testing of an AI literacy questionnaire based on well-founded competency models and psychological change-and meta-competencies. Computers in Human Behavior: Artificial Humans, 1(2), 100014.