Strategies for Integrating Generative AI into Higher Education: Navigating Challenges and Leveraging Opportunities

Abstract

The recent emergence of generative AI (GenAI) tools such as ChatGPT, Midjourney, and Gemini have introduced revolutionary capabilities that are predicted to transform numerous facets of society fundamentally. In higher education (HE), the advent of GenAI presents a pivotal moment that may profoundly alter learning and teaching practices in aspects such as inaccuracy, bias, overreliance on technology and algorithms, and limited access to educational AI resources that require in-depth investigation. To evaluate the implications of adopting GenAI in HE, a team of academics and field experts have co-authored this paper, which analyzes the potential for the responsible integration of GenAI into HE and provides recommendations about this integration. This paper recommends strategies for integrating GenAI into HE to create the following positive outcomes: raise awareness about disruptive change, train faculty, change teaching and assessment practices, partner with students, impart AI learning literacies, bridge the digital divide, and conduct applied research. Finally, we propose four preliminary scale levels of a GenAI adoption for faculty. At each level, we suggest courses of action to facilitate progress to the next stage in the adoption of GenAI. This study offers a valuable set of recommendations to decision-makers and faculty, enabling them to prepare for the responsible and judicious integration of GenAI into HE.

1. Introduction and Aim of the Paper

The breakthroughs in the development of generative artificial intelligence (generative AI; GenAI) platforms such as ChatGPT, Midjourney, and Gemini are causing far-reaching changes in all aspects of our daily lives, including aspects associated with the labor market, leisure industry, and education. GenAI poses unprecedented challenges to academic learning and teaching methods [1,2]. Recent advancements in the field of GenAI coupled with its unprecedented accessibility have made many consider it a “game-changer” that society and industry should prepare for [3]. A historical examination reveals that the initial fear that new technologies would disrupt fundamental concepts and routines of everyday life is not new [4]. On the other hand, this innovative development can represent a transformative catalyst, allowing higher education (HE) systems to improve their relevance and sustainability [5,6].

It seems that GenAI technologies are here to stay. Consequently, decision-makers and faculty members in higher education institutions (HEIs) must critically examine the integration of these technologies into academic teaching and learning processes. Therefore, a team of researchers from the Faculty of Instructional Technologies at Holon Institute of Technology in Israel consolidated their efforts and formulated this paper to examine the latest developments of GenAI and the possibilities of leveraging it to benefit academic teaching, thus promoting the development of GenAI and ensuring that it becomes innovative and trustworthy and “respects human rights and democratic values” [7] (A preliminary version of the current paper focusing on higher education in Israel can be found through this link: https://facultyprojects.telem-hit.net/HigherEducationGenAI/En/, accessed on 1 May 2024).

2. What Is GenAI?

GenAI applications facilitate a sophisticated interaction between humans and computational systems, enabling these systems to perform a variety of complex tasks. These tasks include providing intelligent and articulate responses to queries, critiquing texts and ideas, generating images, and orchestrating intricate programming solutions [7,8]. GenAI is a subfield of artificial intelligence (AI) that focuses on creating machines that can generate new content, such as images, music, and text. Unlike AI systems designed to recognize patterns and make predictions based on existing data, GenAI systems are designed to create new data, mediated by prompting, a process in which users write a short text to describe their desired outcomes [7].

Since the end of 2022, there has been a tremendous increase in awareness, familiarity, and use of AI tools, especially those tools considered to be GenAI: ChatGPT, Microsoft Copilot, Midjourney, Synthesia, DALL-E, et cetera, among users without prior knowledge or training in AI. For example, according to OpenAI, ChatGPT reached 1 million users in just 5 days after its launch and currently has over 180.5 million monthly users and 100 million weekly active users [9]. Further, Midjourney, an AI text-to-image generator, has accumulated over 16 million total users as of November 2023, with between 1.2 and 2.5 million daily active users [10,11]. It is believed that the breakthrough in GenAI tools will lead to far-reaching changes in everyday human life [12].

The widespread adoption of GenAI applications is attributed to several factors. Firstly, advancements in computing infrastructure, cloud computing, processing power, the availability of powerful algorithms, data harvesting, and storage have enabled the fast and mature handling of complex real-time AI models [13]. Additionally, the removal of technological barriers and the availability of simple language interfaces have made GenAI applications accessible and user-friendly [14]. The influence of technology-savvy student populations and the impact of social networks and globalization have also contributed to the readiness of individual users to explore GenAI applications [10]. However, there are concerns associated with the use of GenAI in education, particularly about the impact of GenAI on core skill development and the need for educators to guide students’ interactions with GenAI to preserve these skills [15]. Lastly, the potential of AI-generated content to overcome limitations in traditional recommender systems has been highlighted, and there is a proposal to create a new generative recommender paradigm [16].

3. GenAI in HE

Along with research, HE systems are dynamic ecosystems that foster not only the transfer but also the co-creation of knowledge through a multitude of pedagogical practices. The educational journey is characterized by an intricate interplay between teaching and learning, where faculty members are not merely knowledge transmitters but also facilitators of a learning environment that encourages critical thinking, problem-solving, and the development of competencies relevant to the present and future professional fields. These pedagogical strategies underscore the importance of active student engagement and self-education, leading to deep and meaningful learning outcomes [17]. For students, learning is a process of acquiring knowledge that will lead to a change in professional abilities in the present and/or in the future [18,19]. Academic learning is usually based on clear procedures—including lectures, reading, assignments, and tests—designed to promote understanding, broad learning, and critical thinking among students [20].

Before the GenAI breakthrough, a range of studies explored the opportunities, challenges, and future research recommendations related to AI in education. For instance, Tahiru [21] and Zawacki-Richter [22] highlighted the potential role of AI in education, particularly in academic support and institutional and administrative services. However, they noted the challenges and risks associated with AI, such as the lack of critical reflection and the need to explore ethical and educational approaches. Further, Ali [23] and Okonkwo [24] delved into specific applications of AI in HE, including intelligent tutoring systems and chatbots. They emphasized the need for additional exploration of pedagogical, ethical, social, cultural, and economic dimensions and the potential benefits and challenges of these applications.

The intelligent capabilities of GenAI applications, such as ChatGPT, and their ease of use allow many opportunities for learning and diverse uses, offering great pedagogical potential. Therefore, it is believed that teaching, learning, and academic research will be greatly impacted by the introduction of GenAI [1,2,25]. The empirical research on adopting GenAI in HE is in its infancy, as the first research studies published by the end of 2022. The search for research on GenAI in HE covered only open-access peer-reviewed journal articles published in English. The search queries “GenAI” OR “generative artificial intelligence” AND “higher education” were used to include papers with these terms in the titles, keywords, or abstracts published from 1 December 2022 (a few days after ChatGPT was released for the general public) to 28 February 2024. The search was executed mainly on Google Scholar. Our review specifically aimed to include only empirical research studies that provide direct evidence on the adoption of Generative AI in higher education settings. We excluded theoretical, review articles or editorial commentaries, which did not meet our criteria for empirical data. Our goal was to base our analysis on studies that presented primary research and tangible findings specific to the implementation of GenAI in HE. We excluded studies that primarily focused on the technological aspects of Generative AI, such as algorithmic development or GenAI system design, as our interest was in the application and impact of these technologies within HE practices. We included thirty-three empirical case studies, excluding papers with a focus on the technology. In this brief timeframe, preliminary research endeavors have begun assessing the incorporation of these tools in learning and teaching processes. It is suggested that while there is a significant interest in exploring the role of GenAI in enhancing learning and teaching processes, the precise focus on GenAI is in case studies specifically targeting student and faculty attitudes and perceptions towards the integration of GenAI in academic learning and teaching processes see [26,27,28,29,30,31,32].

In a national research survey, Malmström et al. examined Swedish students’ attitudes toward AI use in academic learning in Sweden [33]. In the survey, nearly 2000 students answered an online questionnaire in April and May 2023. The findings indicate students’ familiarity with and use of ChatGPT and their positive perceptions towards using AI tools in learning. Almost all the respondents were familiar with ChatGPT, and more than a third used it regularly. Most students had a positive feeling about using chatbots and other AI-language tools in education; notably, many claimed that AI made them effective learners. On the other hand, most students did not know if their institutions had rules or guidelines regarding the responsible use of AI. In a national research survey of Israeli students’ views on using GenAI for learning purposes as well as the students’ perceptions and ethical dilemmas, similar findings were found: Nearly 700 Israeli students testified that GenAI applications in general and ChatGPT in particular have become integral to their academic study routines. Furthermore, students reported making positive use of ChatGPT, including employing it as a personal tutor, as an instrumental tool that enabled a search for academic information, and as a source of inspiration. However, an analysis of the findings also revealed negative uses of ChatGPT, mainly in that students employed it to complete their assignments. Notably, most students were aware of the ethical aspects of using GenAI for learning purposes and expressed a desire for guidance to be urgently provided, especially regarding the consequences of being caught cheating [34]. Johri et al. further explored student perceptions and the potential impact of GenAI on education, underscoring the importance of addressing concerns such as privacy, accuracy, and ethical implications [35]. These studies collectively show the need for a balanced approach that harnesses the potential of GenAI while mitigating its risks. A challenge facing academic faculty is how to leverage these new tools and adopt the capabilities they offer.

The literature shows that applications such as ChatGPT can be used to broaden knowledge and learning processes on diverse subjects [36,37], promote personalized learning experiences [38], and facilitate the writing process [39,40], thus reducing the pressure of writing papers [39] and cognitive load [36] while giving learners autonomy that will increase their sense of control over learning processes and even strengthen the sense of their ability [41] and allow them to receive constant and real-time feedback on their performance [42]. All these benefits may improve students’ learning experiences [1,43,44]. Consequently, GenAI increases the involvement of students in the learning process as well as their ability to evaluate information and ask questions, thus strengthening their critical learning [45,46]. ChatGPT may also have social consequences because it can be harnessed to create dialogue with learners, thereby reducing the loneliness some students feel during their academic studies [1,43,45].

Although GenAI holds great promise for HE, GenAI is associated with some concerns. First, integrating GenAI into learning poses ethical challenges that require an in-depth investigation. For example, GenAI applications make it possible to create content that is almost indistinguishable from human-created work [2,47,48]. Further, Sullivan et al. found that most references to AI ethics in academia were related to cheating, academic dishonesty, or misuse of information among students [49]. These behaviors are reflected in the description of the negative consequences of using GenAI applications in academia, including in creative works, encompassing visualization, sound, and text. One concern is the possibility of using these applications without proper attribution, which may undermine essential academic skills such as critical thinking, problem-solving, creativity, research abilities, and interpersonal communication [50,51]. Another concern relates to the significant amount of data needed for GenAI to function, which creates the potential for security breaches and violations of privacy [52], for example, using GenAI models to craft convincing phishing emails that will trick users into disclosing sensitive information [53]; using GenAI to invade individual privacy through mass surveillance or data mining [54,55]; utilizing GenAI to develop sophisticated malware that is difficult to detect using security software [53,56], and utilizing GenAI capabilities to identify and exploit security vulnerabilities, allowing attackers to gain unauthorized access to systems [53,56]. Such weaknesses within models such as ChatGPT can be exploited for malicious purposes [53].

A major concern associated with GenAI is inaccuracy and bias because AI inherently reflects imperfections in its training data. Generative models can fabricate information, plagiarize sources, exhibit prejudiced associations, et cetera. Therefore, extensive oversight is necessary to validate quality and identify errors. Relying entirely on AI for rote content delivery could also undermine the development of critical thinking skills. Human educators remain vital in fostering high-level analysis, creativity, and interpersonal growth [57]. Additionally, sometimes there are concerns related to reliance on technology and algorithms when users experience insufficient self-trust [58,59,60], which raises concerns about the human ability to understand, manage, and responsibly control AI [61] besides limited access to educational resources, disputes over intellectual property rights, and alterations in educational values [55].

4. Responsible Integration of GenAI in HE: Experts’ Recommendations

As described in the previous sections of this paper, GenAI is an ecosystem of algorithms, tools, solutions, systems, applications, and a variety of products that pose challenges such as bias, inequality, non-transparency, and a lack of reliability. In the future, professionals must develop and implement intelligent solutions that take into consideration a responsible approach to the effects of AI and GenAI technologies on the environment, society, governance, privacy, et cetera.

Responsible AI refers to the practice of designing, developing, and deploying AI systems in a way that is ethical, fair, transparent, and accountable. Responsible AI encompasses a broad range of considerations, including but not limited to ensuring AI systems do not perpetuate or amplify biases, protecting the privacy and security of individuals, ensuring the reliability and safety of AI systems, and making AI decisions explainable to humans. The goal of responsible AI is to create AI technologies that serve humanity positively, align with societal values and norms, and contribute to sustainable development [62].

Based on a literature review of the current state of integration of GenAI in HE and the expertise of our research team in implementing innovative technologies in academic teaching and given responsible AI principles, we present the following recommendations to decision-makers, including academic staff, preparing for the era of GenAI:

• Awareness of the coming disruptive change—This is not hype or a minor Information Communication Technology (ICT) change. Moreover, some have posited that humanity stands on the precipice of what could be considered the most significant technological upheaval in recorded history. Considering this perspective, it becomes imperative for the management and faculty of academic institutions to cultivate a profound understanding of the transformative potential that GenAI applications present to pedagogical methodologies, learning paradigms, and scholarly research. This burgeoning awareness will necessitate a careful formulation of a comprehensive institutional strategy, which specifically entails the development of a policy framework for integrating GenAI applications into the academic milieu along with a recalibrated design of academic programs. Such strategic planning should also allocate necessary resources to support the seamless adoption and integration of GenAI technologies into educational and research activities.
• Training faculty—As with any ICT change, the onus of incorporating GenAI into the academic curriculum rests squarely upon the shoulders of teaching staff, underscoring the critical need to enhance a faculty‘s proficiency in these emergent technologies. However, the changes GenAI tools provide are never seen before, so it is essential to cultivate a comprehensive understanding among educators of the capabilities and limitations of GenAI tools. Therefore, faculty members should be encouraged to actively immerse themselves in AI technology, thereby expanding and enriching their domain-specific knowledge and pedagogical strategies. Efforts to augment a faculty’s expertise in GenAI can be directed through various professional development initiatives. These initiatives may include the organization of workshops designed to foster practical skills and theoretical knowledge, the formation of discussion groups that facilitate peer-to-peer learning and exchange of insights, personalized training sessions tailored to individual learning needs, and the distribution of instructional materials that guide the effective integration of GenAI in teaching. Additionally, sharing successful case studies and experiences can serve as a valuable resource, illustrating the transformative potential of GenAI in enhancing educational outcomes. Such multifaceted approaches to faculty development not only equip educators with the necessary tools to navigate the complexities of GenAI but also ensure that they are well-positioned to lead their students through the evolving landscape of digital and AI literacy.
• Changing teaching and assessment practices—Within the realm of HE, the lecture-based model of instruction continues to predominate. This model is characterized by the dissemination of knowledge to large cohorts of students who typically assume the role of passive recipients tasked with the absorption of presented information. Despite its longstanding presence as a foundational element of academic instruction, the effectiveness of this pedagogical approach is increasingly being called into question, particularly when contrasted with methodologies that promote active engagement and facilitate the development of higher-order cognitive skills. With the advent of the GenAI tools, a continuous pedagogical strategy shift is needed. A strategy that places students at the center of the educational experience, engaging them in authentic problem-solving activities, data analysis, decision-making processes, collaborative endeavors with peers, and producing concrete outputs that encapsulate their learning journey is required. In the domain of student assessment, many educational institutions continue to favor conventional testing mechanisms, which predominantly assess memorization and recall abilities, often at the expense of evaluating the practical application of knowledge and skills. Educational stakeholders have an imperative to ensure that assessment methodologies are congruent with the articulated learning objectives. They should advocate for a diversification of evaluation practices. Such an approach does not merely encourage the acquisition of knowledge through memorization but also fosters deep understanding, critical analysis, and the ability to apply learning in varied contexts. It is necessary to acknowledge that the call for these transformative approaches in teaching and assessment practices is not a direct consequence of the emergence of GenAI. Nonetheless, the advent of the GenAI era undeniably amplifies the urgency for adopting these educational reforms, highlighting the necessity for HEIs to evolve in response to the changing landscape of knowledge acquisition and application in the 21st century.
• Students as co-partners with faculty—An understanding of the technological transformation of GenAI and its implications for academia should be shared with the younger generation, namely, students. The active involvement of students is not just about familiarizing them with new technologies; it is also about empowering them to be proactive participants in their education and future. Integrating GenAI into academic learning and teaching is not just a matter of keeping pace with technological advancements; instead, it is a strategic imperative for preparing students for the future, enhancing their learning experiences, fostering critical and ethical reasoning, and encouraging innovation and creativity.
• Imparting learning literacies adapted to the GenAI era—In the contemporary education landscape, a robust foundation in AI literacy must be laid for students. This foundation includes the provision of a comprehensive set of knowledge, skills, and competencies essential for navigating the complexities of the AI ecosystem with acumen and discernment. Consequently, there is a necessity for educators not only to elucidate the operational mechanisms of AI applications but also to emphasize the importance of students engaging with these technologies while equipped with a preliminary framework of critical thinking specific to the explored content area. Such a pedagogical strategy will ensure that learners become adept at evaluating the veracity and quality of information obtained through AI tools, thereby cultivating indispensable skills requisite for thriving in a society increasingly interwoven with AI technologies. These skills include but are not limited to critical thinking, enhanced cognitive capabilities, creativity, and the capacity for original thought. Furthermore, it is fallacious to believe that students have the innate power to navigate the ethical quandaries associated with GenAI applications. Consequently, there is a pressing need to integrate ethical reasoning and principles of academic integrity within the educational curriculum. This integration would not only enrich the students’ academic journey but also prepare them to confront and address the moral and ethical challenges that are intrinsic to the deployment and utilization of GenAI technologies in various aspects of life.
• Applied research is needed—Research constitutes the cornerstone of scholarly pursuits, embodying the essence of academic inquiry and advancement. The emergence of GenAI does not detract from the imperative for sustained research within this domain. Nonetheless, given the constrained temporal framework for research currently available, there is a pressing need to prioritize applied research endeavors. Specifically, these endeavors entail a rigorous investigation into the practical application and tangible implementation of GenAI-enhanced pedagogical methodologies juxtaposed against conventional educational practices. Such empirical studies are instrumental in elucidating the substantive contributions of GenAI technologies to the educational field. The insights from this research will play a pivotal role in informing the strategic planning and responsible execution of transformative changes in the design and delivery of learning and teaching processes. Through this analytical lens, the academic community can navigate the integration of GenAI based on a foundation of evidence-based understanding, ensuring that technological advancements are leveraged to thoughtfully and effectively enhance educational outcomes in teaching and assessment practices.

Finally, we propose four preliminary scale levels of a GenAI adoption model for faculty. Each successive level is a more advanced stage of adoption of GenAI than the previous level. Further, at each level, we suggest courses of action to facilitate progress to the next stage in the adoption of GenAI:

• GenAI Novice Stage: This initial adoption phase is characterized by minimal utilization of GenAI tools. During this stage, it is imperative to foster a sense of curiosity among faculty members regarding the potential benefits that can be derived from engaging with GenAI technologies. Examples of activities that can be undertaken to this end include leveraging these technologies for tasks such as composing emails or facilitating brainstorming sessions. Furthermore, there is a pressing need to cultivate an awareness of the urgency of adopting GenAI technologies. This sense of immediacy stems from a recognition that a failure to integrate such advancements may swiftly result in a loss of relevance within the rapidly evolving technological landscape.
• GenAI as a Utility: In the secondary phase of adoption, GenAI assumes the role of a supplementary instrument employed for distinct tasks. During this stage, faculty members will appreciate the advantages offered by tools such as ChatGPT, incorporating them into their daily routines. For instance, GenAI may be harnessed to generate high-quality content or to provide support in project management endeavors, thus facilitating the acquisition of new competencies, such as the power to produce quality academic writing.
• GenAI as a Co-Pilot: At this level, GenAI functions as a strategic tool, guiding decision-making in the teaching process and serving as a support assistant that analyzes data to guide decision-making strategies. This is a significant change from the previous level because users are handing over tasks to AI. At this stage, faculty members should be encouraged to develop a concept of “delegating powers” to GenAI to “free up” their time for tasks where human users have an advantage over AI. Ideally, each faculty member should be required to select a task that can be “delegated” to AI.
• Transformative GenAI: This is the highest level of the GenAI adoption process. At this level of adoption, GenAI is deeply rooted in teaching strategies and methodologies, changing them significantly. For example, research processes can be redefined using chatbots powered by AI, providing immediate and personalized responses. This level of integration challenges users to reimagine their role as instructors. This level opens the way to a future where GenAI changes the world and existing roles and even creates new roles. At this stage, developing a “growth mindset” is necessary, which means looking for opportunities to create values that could not be created without AI and adopting the new values. Faculty members must be encouraged to exploit opportunities to experience the challenges of uncertainty and urgent changes.

5. Discussion

The advent of GenAI in HE heralds a transformative potential to reshape pedagogical frameworks and institutional strategies. This paper has discussed the multifaceted nature of GenAI’s integration into HE, highlighting both its transformative potential and the multifarious challenges it introduces, particularly in the realms of ethical considerations and teaching practices.

As the literature review shows, GenAI offers profound opportunities to improve students’ learning experiences by facilitating learning environments tailored to students’ educational needs. These technologies enable the co-creation of knowledge, fostering an integrative and participative educational ecosystem. GenAI’s ability to provide personalized learning experiences with constant and real-time feedback can significantly enrich educational outcomes and equip students with the necessary skills for future professional challenges. This technology also promises to democratize access to high-quality education by providing scalable solutions that adapt to varied learning styles and paces.

Moreover, GenAI can enhance the capability of HE institutions to analyze large datasets of student learning patterns, enabling educators to optimize teaching strategies and improve educational content dynamically. The potential to use GenAI for predictive analytics in student performance also opens avenues for early interventions, potentially reducing dropout rates and improving student retention.

However, the integration of GenAI also introduces major challenges. The potential for these systems to perpetuate existing biases and infringe upon privacy rights and unethical conduct poses significant concerns. HE institutions must adopt comprehensive strategies to address these challenges, ensuring that deploying GenAI technologies aligns with the foundational principles of academic integrity and ethical standards. To mitigate these risks, it is imperative to develop robust frameworks for data governance, transparency in GenAI decision-making processes, and continuous monitoring of GenAI systems.

While integrating GenAI into HE presents remarkable opportunities for educational development, it also requires a nuanced understanding of its implications and a robust framework for its responsible use. By embracing a comprehensive approach emphasizing ethical integrity, pedagogical effectiveness, and continuous adaptation, HE institutions can navigate the complexities of GenAI integration. This can improve the quality of academic education and ensure that it remains relevant and responsive to the needs of current and future generations of students. Through thoughtful and strategic integration, GenAI can be leveraged to foster an enriched, inclusive, and forward-looking academic educational landscape.

6. Conclusions

The emergence of GenAI marks a pivotal moment for HE. As we have detailed in this paper, GenAI has the potential to change learning and teaching processes fundamentally. The integration and utilization of GenAI presents remarkable prospects for the development of HE. However, the adoption of Gen AI in HE also entails some concerns and challenges. We hope this paper will provide a solid foundation for decision-makers and faculty to prepare for the responsible and careful adoption of GenAI in academic learning. As we navigate the challenging journey toward adopting GenAI, we must underscore the foundational pillars of academic learning: intellectual curiosity and an unwavering dedication to personal and collective growth. Comprehending the ramifications of integrating GenAI within the teaching and the pedagogical landscape is crucial. This understanding will enable faculty and their students to remain relevant within the contemporary GenAI era. Such an endeavor demands a proactive engagement with evolving GenAI technologies and necessitates a thoughtful consideration of how these tools can be harnessed to enhance the educational experience. By fostering an environment that values critical inquiry and adaptability, we can ensure that the academic community remains at the forefront of innovation, prepared to contribute responsibly and meaningfully to the ongoing discourse and development within the GenAI domain.

This paper has limitations that must be acknowledged: First, the empirical case studies presented here may not represent the diversity of global academic contexts. Second, while this paper discusses the integration of GenAI, it needs to delve deeper into the technical specifics and variations of GenAI technologies, which can significantly influence their application and impact in academic settings. Finally, the long-term impacts of GenAI on HE have been projected rather than empirically measured due to the preliminary stage of implementing these technologies in academic settings.

To address these limitations and further the field, future research should:

• Expand empirical bases: Research needs to be conducted across a broader array of HE institutions worldwide to understand the challenges and opportunities GenAI presents.
• Technical deep dives: A more detailed technical analysis of GenAI tools is needed better to understand their capabilities and limitations in educational contexts.
• Longitudinal studies: Long-term studies are needed to assess GenAI’s impact on learning outcomes, student engagement, and career readiness over time.
• Model development: Further develop and empirically test the proposed GenAI adoption model in various HE settings to refine it into a responsible institutional guiding process.
• Policy and ethical considerations: The policy implications and ethical concerns surrounding the use of GenAI in HE need to be further examined.

For years, arguments have been made about the irrelevance of HE as a trusted body for training future generations of workers. Academia, as a research body that has also undertaken the process of training professionals in a variety of fields, is required to constantly update its expertise and learning methods. According to most projections, the proliferation of GenAI will have a dramatic effect on the global job market and on professions that require academic training. For academia, the challenge of remaining relevant will increase as there is the potential for the gap to widen between students’ training and the work environment they will encounter upon transitioning to the labor market. Accordingly, HEIs must take steps today to understand the nature of change in each content area and reflect these changes in their teaching and assessment processes.

In future research, we plan to expand and deepen our understanding of the opportunities and challenges that the integration of GenAI poses to HEIs globally, nationally, and locally. We seek to further develop the four levels of a GenAI adoption model, including empirically examining its application possibilities as a responsible guiding process within HEIs.