Keywords

7.1 Introduction

In the preceding six chapters, we first set out the general case for application of generative AI (GenAI) for teaching and learning in Higher Education (HE). From there, we discussed specific use cases across a wide range of teaching and learning activities, including lesson preparation and content development, key considerations in developing personalised learning, and applications in formative and summative learning and assessment. In this chapter, we offer reflections on the three areas that will underpin future successful applications of GenAI in the higher education sector and some key risks that stakeholders must consider while integrating GenAI into higher education.

7.2 Key Areas for Future GenAI Success in Higher Education

7.2.1 Educators Must Learn to Live with Generative AI

The first point to re-iterate is that GenAI will not go away. Educators in HE must embrace the challenge and harness the opportunities it offers to strengthen their practice for improved outcomes for learners. There are three major reasons for this imperative.

The first is an obvious one: GenAI is now ubiquitous, and students are active users regardless of whether they are advised to use it or not use it. In a sense, GenAI is a something of an unstoppable moving train, but inherently a value-free tool that can nevertheless be deployed in a right or wrong way, for good or ill. Imposing an outright ban is a fool's errand, in the circumstances. Educators can influence how it is used, but only if they engage with it themselves. In this book, we have highlighted a number of positive use cases, but these are by no means exhaustive. The burgeoning field of AI lends itself to a wide variety of innovative applications. It is a fertile field for engaged creative minds. Educators who refuse to engage with AI are unlikely to be of help to learners using it and seeking guidance on how to use it correctly and appropriately to enrich their learning experience.

The second reason is that AI is already shaping the world of work. The last edition of the Future of Jobs Report 2023 indicates that majority of organisations are adopting AI, and most are investing resources to train and re-train their workers to use AI in their operations. More and more roles in the future will require some competence in artificial intelligence, as well as other frontier digital technologies. Educators, who are training learners for the twenty-first-century job market, cannot afford to be left behind. If Industry demands it, educators must be prepared to supply it. Furthermore, curriculum design and teaching delivery in HE is increasingly collaborative, and industry partners are increasingly sought to make inputs to new module designs and content delivery. Individual educators should therefore have requisite competence to engage meaningfully with industry partners and provide effective support for learners.

The third reason is that frontier technologies, such as GenAI's, will inevitably shape educators’ practice, as teachers and researchers. This is the case with previous iterations of technologies that are now considered mainstream in the HE sectors. One example is VLE technologies, now a mainstay in HE sectors. More recently, following the outbreak of COVID-19 pandemic, collaborative software like Zoom and MS Teams have become indispensable tools for universities and colleges, facilitating seamless transition to online and hybrid learning models, with synchronous and asynchronous contents (Imran et al., 2023; Turnbull et al., 2021). Like these technologies, GenAI is poised to play increasingly prominent, and ultimately central, role in the higher education sector. Educators should prepare early for that “inevitability” by developing competences on the use of AI both for their own research and teaching practice. As we have highlighted in this book, the use of AI can drive efficiency in various teaching activities including content preparation, development of engaging tasks, and activities for learners. GenAI can also be used to support research in areas such as language editing, and time-intensive activities like data cleaning and processing, presentation of results, management of international collaboration, and public communication of research via multiple channels, including social media.

We now turn our attention to competency. In order for educators to “live” with GenAI, they must develop requisite competencies and skills. They do not have to be top-of-the-range proficiency, but just enough to set themselves for lifelong learning and evolution. Below, we highlight some practical suggestions for educators to develop these AI skills:

  • Set up a free account: The first suggested action point is to set up a free account for a GenAI product. Following Open AI's pioneering launch in November 2022, other major actors have stepped in with similar products, including Google's Gemini (formerly Bard) and Microsoft Co-Pilot, among others. Setting up an account provides you with the immediate opportunity to explore and learn more about GenAI. You can make it a fun start by introducing yourself and ask your GenAI to advise where best to start as a new educator signing up for the first time (see Prompt 7.1 below).

Prompt 7.1: Educator—Making a Start with ChatGPT

ChatGPT Prompt

Hello. I am a Professor in Entrepreneurship at a UK university trying to find out what ChatGPT is all about and if it can be of significant use for me as an educator. I just signed up to this platform and wondering where to start, or whether I should start, here. Do you have any suggestions?

ChatGPT Response

Hello! Welcome to the platform, and it's great to hear that you're interested in exploring how ChatGPT can support your work as an educator in entrepreneurship. There are indeed many ways in which ChatGPT can be of use to you:

  • Research Assistance: ChatGPT can help you with research tasks by providing information on various topics related to entrepreneurship, business trends, case studies, and more.

  • Student Support: You can leverage ChatGPT to provide additional support to your students. It can assist them with homework, clarifying concepts, generating ideas, or even simulating business scenarios.

  • Content Creation: ChatGPT can aid in generating content for lectures, assignments, or course materials. It can assist in writing explanations, creating examples, or even generating discussion prompts.

  • Q&A Sessions: You can use ChatGPT during Q&A sessions to provide instant answers to student queries, thereby enhancing engagement and interaction.

  • Feedback Generation: ChatGPT can help in generating feedback for student assignments, providing constructive criticism, and suggesting areas of improvement.

    To get started, you can try engaging with ChatGPT in various ways:

  • Ask Questions: Feel free to ask any questions you have about entrepreneurship, education, or any other topic relevant to your interests.

  • Test Responses: Experiment with different types of questions or prompts to see how ChatGPT responds.

  • Explore Features: Familiarize yourself with the platform's features, such as document summarization, text completion, and conversation mode.

  • Join Communities: Engage with other educators or professionals on the platform to share experiences, tips, and best practices.

If you have any specific questions or tasks in mind, feel free to ask, and I'll do my best to assist you!

ChatGPT Screen

A screenshot of the Chat G P T page. It displays a response by Chat G P T to a question about the purpose and significance of Chat G P T for an educator. Some of the listed responses for its use include research assistance, student support, and content creation.

  • Join fora of fellow practitioners: The next recommendation, and ChatGPT also suggests this by the way, is to join groups of fellow generative AI users. There are all-purpose groups like ChatGPT suggests above, where you can interact with professionals from a broad spectrum of sectors including but not limited to HE. This can help you to learn about other creative use cases outside of higher education. There are also groups dedicated specifically to exploring applications of AI in higher education. This can help you to dig deeper into different creative use cases and new opportunities to deploy AI to strengthen curricular and enrich the learning experience of students. If there is no such group in your university, you can start one. It is always useful to have a local group of GenAI practitioners with whom you can share and explore new ideas. There are also global platforms, like AI-Ed (AI Education) that focuses on the intersection of artificial intelligence and education. In addition, platforms like LinkedIn and Reddit often host discussions on cutting-edge applications of generative AI in the higher education contexts. We believe this school of thought is influencing the recently announced ChatGPT Edu by OpenAI, an attempt to encapsulate all use cases into a digital product for accessibility, effectiveness, and productivity.

  • Attend seminars and workshops regularly: Regular participation in online platforms and groups can be complemented with attendance at seminars and workshops. Workshops tend to be more focused on specific sub-topics and use cases, and you can select the ones that match your particular interest at a time. Seminars are also a good way to keep abreast of the latest developments, especially those relating to policy at national and sector levels. Typically, seminars feature more proficient and expert users sharing their knowledge and experience and responding to specific questions and issues participants want to explore. Workshops and seminars also provide excellent networking opportunities for HE practitioners.

The overall message here is that there are many opportunities out there for beginners in the HE sectors to enter and explore the world of generative AI. It offers benefits for them as educators to enrich their practice in curriculum development and learning engagement. It also offers opportunities for them as researchers looking to enhance their effectiveness and productivity as knowledge producers. In other words, generative AI enables HE practitioners to raise their productivity in the mutually reinforcing domains of research and teaching, for the overall benefit of both themselves and their learners.

7.2.2 Users Must Take Personal Responsibility for the Use of Generative AI

So far, the use cases and prompts explored in this book have re-established a well-known fact: GenAI stands out for its remarkable ability to generate new, previously unseen outputs based on the data it has been trained on. This capability can revolutionise higher education by enhancing creativity, automating content generation, and providing personalised learning experiences. These benefits are undeniable, yet the use of this technology especially by individual users requires a cautious approach to prevent misuse and unintended consequences. While its potential to transform learning is unquestionable, its power necessitates a parallel emphasis on responsible and ethical use. We argue that, in the hands of students and educators alike, GenAI must be wielded with a profound sense of ethical responsibility to maintain academic integrity, foster critical thinking, and ensure a fair and equitable learning environment. GenAI is a double-edged sword that can be exploited for malicious purposes, such as creating deepfakes, spreading misinformation, or the production of illegal content. Beyond these surface-level concerns, deeper ethical issues relate to intellectual property (IP) rights and academic integrity.

As AI-generated content becomes increasingly indistinguishable from human-created content, distinguishing between the two and determining ownership becomes challenging. Users must respect existing IP rights and consider the implications of using AI to generate derivative works. Establishing clear guidelines and legal frameworks concerning the ownership of AI-generated content is crucial to protect creators’ rights and encourage innovation within ethical boundaries.

The World Intellectual Property Organization (WIPO), a global forum for intellectual property policy, services, information, and cooperation, has publicly acknowledged these concerns. They have noted that: GenAI presents many IP touchpoints and uncertainties. While it is impossible to completely mitigate these IP risks, the following considerations may be useful for users navigating IP considerations in this evolving domain. Employers (and students) of HE institutions using generative AI tools may inadvertently reveal trade secrets or waive confidentiality in commercially sensitive information if such information is used for prompting GenAI tools. To prevent this, they should consider leveraging a combination of technical, legal, and practical safeguards (WIPO, 2024).

For instance, a use case that is relatable to this context is in Chapter 3, where we developed the circular plastic economy GPT, which was customised and “trained” with a sixteen-chapter textbook which is an output from a UKRI-sponsored research project. In this case, the book that was used in an open access book available for use by all. However, educators looking to implement similar model in the future need to pay close attention to intellectual property rights, and ensure they fully acknowledge their sources. Additionally, there is significant legal uncertainty regarding whether the use of GenAI tools, their training, and outputs constitute IP infringements, with answers varying by jurisdiction. Users should mitigate risks by seeking indemnities where possible, vetting datasets that are used for building GPTs, and implementing technical and other practical measures moderated by policies to minimise the likelihood of infringement. According to WIPO (2024), there are pending litigations worldwide which aim to determine whether training AI with IP-protected items, the use of such trained AI models, and the outputs they generate amount to IP infringements.

7.2.3 HE Institutions Must Adapt and Keep Pace

In today’s teaching and learning environment as well as the broader labour market, the demarcation between uniquely human capabilities and routine automatable tasks is fluid. In this context, HE institutions should seek to help learners develop skills that are complemented by, not contrasted with, AI including creativity, complex problem-solving, articulate communication, teamwork, flexibility, and ethical discernment. Broadly speaking, it is futile to resist the potential impact of GenAI tools on teaching and learning processes. Rather, institutions should proactively engage with these tools in order to tackle emerging challenges and devise strategies for their innovative and responsible application. The proactive engagement that we advocate here leaves no room for hypocrisy where universities ban the use of GenAI and yet seek to attract research funding on the application of AI technology in education. It also precludes the one-sided paternalistic approach where education practitioners and stakeholders only seek to regulate what others, including students, do with the technology but fail to improve themselves. Rather, we advocate that HE institutions adapt and keep pace with the technology. This is important because of the intrinsic nature of knowledge and learning, the two phenomena that underlie all technologies and their applications.

Firstly, knowledge is both dynamic and transient; in other words, it constantly evolves to the point of becoming obsolete over time. This characteristic of knowledge necessitates a continuous commitment to learning and adaptation. Secondly, knowledge is cumulative, that is, new knowledge builds upon existing knowledge such that learning and adaptation become easier over time. These two features of knowledge suggest that it leads to greater benefits over time but offers only short windows of opportunity to take advantage of those benefits. For example, decades ago educators would be celebrated for expertise in teaching the use of slide rules to solve problems in logarithms. Today, that knowledge has very limited value.

The challenge for HE institutions is to position themselves to capitalise on the opportunities that GenAI offers before the technology goes beyond reach. In the rapidly evolving knowledge landscape, resistance, hesitation, or delay in adoption and adaptation spells obsolescence. By the time laggards recognise and react to the emerging trends, early adopters will have already established a formidable knowledge advantage that could perpetuate or widen the gap between them and the rest. Therefore, we recommend that every institution should seek to secure a position close to the top of the summit before it becomes insurmountably steep. Institutions that fail to respond could be left behind—it is as simple.

This recommendation is even more important given the rapid rate at which GenAI tools emerge and evolve. For example, over the space of two weeks during May 2024, OpenAI released a new flagship model (GPT-4o), made more capabilities (including custom GPTs, data analysis, and web surfing, among others) available for free in ChatGPT, and announced ChatGPT Edu. ChatGPT Edu was specifically tailored for universities and offers enhanced AI capabilities, ranging from advanced data analytics to support for a wide spectrum of administrative and academic tasks. For instance, custom GPT models can be readily developed within the ChatGPT Edu environment to facilitate language learning, grant application processes, personalised academic support, and many more. This is a reflection of how the future might look like. The HE institution of the future will need to take decisive action today by investing in capacity building, ICT infrastructure, collaboration, and risk management.

7.3 Key Risks of GenAI Integration into Higher Education

In this section, we highlight a few important risks that HE institutions and stakeholders need to consider when developing strategies for integrating GenAI into higher education.

7.3.1 Risk of Plagiarism and Academic Dishonesty

The ability of GenAIs to produce text indistinguishable from human output introduces profound concerns regarding plagiarism and the erosion of academic integrity. Imagine a student facing a looming deadline for a research paper. Exhausted and overwhelmed, they turn to a GenAI tool, feeding it a topic and letting it produce a seemingly complete essay. While this scenario might seem tempting, it poses a significant threat to academic integrity. HE institutions bear the fundamental responsibility of upholding rigorous standards, and the indiscriminate use of GenAI undermines the essence of education: fostering independent thought, research skills, and critical analysis. Although plagiarism detection tools have evolved, current AI text detection tools have not been consistently effective in identifying AI-generated texts. In fact, some studies have shown that texts written by non-native English speakers have been incorrectly identified as AI-generated. Universities will, without doubt, implement measures to detect AI-generated content, and the consequences of such discoveries can be severe, potentially leading to failing grades or even expulsion.

The true value of higher education lies not only in acquiring knowledge but also in cultivating the ability to think critically and ask probing questions. Generative AI, by providing ready-made essays or code, risks becoming a crutch that impedes the development of these crucial skills. Students who rely solely on AI-generated outputs may find it challenging to grasp the underlying concepts, analyse arguments, or form their own well-reasoned opinions. Engaging with a problem, exploring different perspectives, and constructing a coherent argument are integral to the learning process. Circumventing this process through GenAI not only detracts from the educational experience but may also inadequately prepares graduates for the complexities of the real world.

Indeed, the risk of students delegating their academic duties to AI and thereby circumventing genuine learning opportunities is widely acknowledged. This risk is compounded by the limitations of traditional plagiarism detection tools like Turnitin in identifying AI-generated content. The recommended response is a paradigm shift in assessment towards evaluating critical thinking and original contributions. The focus must pivot from rote learning to the demonstration of analytical abilities, creative synthesis, and application of information in novel contexts. The paradigm needs to shift away from formats susceptible to AI replication (essays, for instance) towards innovative assessment forms, such as open-ended exams, scenario-based tasks, portfolios, and projects that test individual insight.

No doubt, GenAI is a powerful tool with the potential to enhance learning. However, it is just that—a tool, not a substitute for the irreplaceable role of educators and the dedication required from students. By using GenAI responsibly and ethically, we can minimise the associated risks and strike a balance that upholds the standard and integrity of the academic experience. In reality, the future of higher education depends on striking this delicate balance, ensuring that technology acts as a springboard for learning rather than a shortcut that undermines its foundational principles.

7.3.2 Risks of Biases and Inequality

By design, GenAI is trained on extensive datasets that inadvertently ingrain biases, mirror societal disparities, and amplify historical inequities. Thus, adopting these models without caution could perpetuate stereotypes, foster discrimination, disseminate misinformation, exacerbate social inequities and contravene the foundational principles of HE. These challenges are well known and should be accounted for in the response strategy of HE institutions. For example, clear guidelines should be established for the ethical use of AI tools, including rigorous vetting processes to identify and mitigate biases before integrating them into educational tools. As vendors of virtual learning environments (VLEs) and educational software-as-a service (SaaS) solutions continue to embed AI functionalities within their products, HE institutions need to interrogate the fairness, transparency, inclusivity, and transparency of these functionalities. Specifically, HE institutions need to insist on algorithmic transparency and accountability. HE institutions may also implement procurement protocols that mandate comprehensive impact assessments for AI tools.

Moreover, there are wide disparities in access to cutting-edge AI tools and the requisite computing resources across different contexts. This threatens to create a socioeconomic divide or widen it within educational settings. Consequently, the education system could become a bifurcated one where access to AI tools becomes a symbol of privilege. HE institutions need to actively cooperate to overcome this challenge that could undermine the egalitarian ethos of HE. For example, HE institutions can develop partnerships with technology companies and non-profit organisations to provide wider access to AI tools and necessary computing resources. Such partnerships can implement initiatives like shared cloud computing access, donation of AI software licenses, and establishment of AI resource hubs. Moreover, training and education programmes for both students and faculty in underserved communities can empower them to use AI tools effectively.

GenAI has the potential to deepen existing inequities in educational access and outcomes. An illustrative example involves AI tools used for language learning, which might be calibrated to standard dialects, thereby disadvantaging students who speak in regional accents or dialects not recognised by the AI. Similarly, historical data used in GenAI algorithms could reflect societal biases, thereby skewing learning content away from a diverse and inclusive curriculum. Therefore, the development and deployment of GenAI-driven pedagogies must undergo continuous auditing for biases that might disadvantage minority communities or reinforce stereotypes. For example, AI-enhanced content for history lessons should be vetted to include diverse perspectives and contributions from various cultures, avoiding Eurocentric or other biased viewpoints. In striving for equity, it is crucial for educators and technology developers to collaborate on creating AI tools that are adaptable to different learning styles and needs. Such adaptable innovations will support all students in reaching their full potential.

7.3.3 Risks of AI Illiteracy and Mental Redundancy

There is a pressing AI literacy imperative. The challenges associated with the use of AI in education can only be effectively confronted by competent administrators, educators, and other practitioners. This creates a pressing need to enhance the AI literacy of HE stakeholders. Before they can engage responsibly and innovatively with them, both students and educators need to develop an astute understanding of AI tools—encompassing their operational mechanisms, limitations, biases, and ethical considerations. For instance, training for administrators, teachers, and students on the potential pitfalls and ethical considerations of using GenAIs is practically non-negotiable. Moreover, training programmes are needed to equip educators with strategies for using AI as a supplement rather than a substitute for traditional teaching methods. Furthermore, HE institutions should actively participate in ongoing research and discussions aimed at advancing knowledge of these technologies. After all, it is extremely difficult, if not impossible, to manage what is not understood.

Overreliance on AI could erode critical thinking. If students and teachers rely too much on AI for academic tasks, a dependency syndrome could emerge where the cultivation of critical analytical skills and independent thought is impaired. Thus, HE institutions need frameworks and protocols that help to balance AI-assisted teaching and learning with activities that promote autonomous reasoning and creativity. In this regard, blended learning models are a promising alternative because they integrate AI tools for specific tasks while reserving significant portions of the curriculum for problem-based learning, discussions, and projects that require independent analysis and creative thought. Additionally, assessment criteria could be adjusted to reward critical thinking and originality as way to incentivise students to develop and demonstrate real learning.

HE institutions need to be proactive, inclusive, and future-oriented in policy formulation. The responsibility of HE institutions in dealing with the implications of AI extends to the formulation of explicit, transparent policies governing AI use in education. The policies should provide guidelines to curb misuse, set parameters for ethical usage, and define the specifications for integrating AI into teaching and learning. At the same time, the policies should not create a constraint to experimentation and capacity building. Clearly, the task of formulating the right policies is not trivial. This is why it is important for HE institutions to be proactive by anticipating areas of policy needs and responding on time, rather than waiting to use policies as a reactive tool. Moreover, it is essential to have open dialogue within and beyond the academic community in order to foster a collective ethos of responsible AI utilisation. For instance, the perspective of students is instrumental to creating inclusive AI policies. In addition, today’s HE institutions need to understand their role as preparing learners not only for today’s labour market but also for the future where existing competencies may become irrelevant and new skills may be in demand. Policies around the use of AI in HE should reflect this future orientation.

7.4 Towards a Responsible AI-Enhanced Higher Education Ecosystem

The responsible use of generative AI in higher education should be an important concern for all relevant stakeholders. As already discussed in Chapter 6, responsible GenAI use can be built upon the Responsible Research and Innovation (RRI) framework. The framework emphasises anticipatory, reflective, inclusive, and responsive approaches to innovation such that technological advancements align with societal values and needs. This has several implications for the wider HE ecosystem.

First and foremost, HE institutions must develop clear guidelines for the responsible use of GenAI tools. These guidelines should specify acceptable applications, such as utilising GenAI for brainstorming sessions or generating preliminary outlines, and unequivocally ban its use for producing plagiarised work. For example, a university could implement a policy where GenAI-generated content must be clearly labelled and used solely for ideation, a draft or starting point for teaching, learning, or research and development.

Secondly, educators play a crucial role in integrating discussions about AI ethics within their curriculum. This can take the form of case studies on AI misuse, debates on the ethical use of AI in academic settings, or analysis of real-world scenarios where AI's impact on society is examined. For instance, a course could include a module where students debate the ethics of using AI in creating art or literature, critically examining questions of originality and creativity.

Promoting a culture of academic integrity is essential. By valuing original thought, critical inquiry, and honest effort above all, educators can foster an environment where students are motivated to excel through their intellectual pursuits. An example action could be rewarding students who demonstrate innovative approaches to combining AI-generated content with their unique insights, thereby encouraging responsible AI use that complements human creativity. Educators wearing their researcher hats should also model ethical behaviour in their use of GenAIs, for example in manuscript preparation. Most academic publishers now acknowledge that researchers use GenAI in some form in the process of undertaking research and disseminating output. Publishers distinguish between copy writing, which requires original content creation and creative thinking; and copy-editing, which involves reviewing and correcting already written material to improve accuracy and readability. As such, most journal guidelines now include warnings that use of GenAI for copy writing is unacceptable and unethical but use for copy-editing is permissible. Emerald Publishing (2023) observes, this is because use of GenAI tool “mirrors standard tools already employed to improve spelling and grammar, and uses existing author-created material, rather than generating wholly new content, while the authors(s) remain responsible for the original work”.

The responsible and ethical use of GenAI transcends mere recommendation; it is a necessity as the technology landscape continues to evolve. Today’s decisions and indecisions will significantly influence how GenAI impacts teaching and learning, and by extension the society. Key action points include:

  • Understanding generative AI: Educators and students should be well informed about the opportunities, risks, and limitations associated with generative AI. Workshops or seminars could be conducted to disseminate knowledge on these topics.

  • Blending human creativity with AI outputs: Encouraging the integration of human input and creativity with AI outputs ensures that the ownership of final works remains clear. Collaborative projects could exemplify how AI can augment rather than replacing human creativity.

  • Respecting intellectual property: Avoiding the use of third-party intellectual property in AI prompts can minimise the risk of generating infringing content. Legal workshops could help clarify what constitutes fair use and how to navigate copyright considerations.

  • Safeguarding confidential information: It is crucial to avoid using confidential or sensitive information in AI prompts. Training sessions on data privacy could help staff and students recognise and protect personal and institutional data.

  • Providing clear and transparent documentation: Staff and students should be encouraged to label GenAI-generated outputs and maintain records of AI prompts used. This could be part of a broader policy on academic honesty and transparency.

  • Steering clear of deepfake technologies: The use of deepfake or other potentially deceptive AI technologies should be explicitly prohibited. Ethical guidelines should address the implications of creating and distributing such content.

  • Communicating legal risk: Clearly communicating the legal risks associated with generative AI use helps align practices with the institution’s risk appetite. Legal teams could provide regular updates on evolving laws and regulations affecting AI use.

7.5 Concluding Thoughts

We have, in this final chapter, highlighted a number of key issues relating to the future of generative AI, in relation to HE practitioners, general users, and at organisational and institutional levels. This underlines the multi-layered and multi-dimensional future of generative AI, and why stakeholders must work together to harness GenAI's true potentials in higher education. In effect, responsibility for ethical use of GenAI is shared, and each stakeholder must play their part. Educators need to keep updated and stay at the cutting edge of the technology, in order to be of better value to learners, but also for themselves as researchers and knowledge producers. General users should be aware of their responsibilities to use AI tools ethically, and their liabilities for unethical and malicious use. Stakeholders, including HE institutions, must be proactive in creating clear guidelines and policies for the responsible and ethical use of GenAI, while also supporting its use through continuous training of staff. Additionally, it is crucial to provide an enabling environment to maximise opportunities and mitigate risks associated with GenAI.

The future of AI is as promising as it is fraught with risks. Both the opportunities and challenges are inevitable, and a “Luddite” resistance to the technology is ultimately a fool’s errand. HE practitioners should embrace the exciting opportunity to shape the use of AI and other frontier digital technologies. An open-minded approach will not only help to enrich the learning experience of students, but it will also enhance the productivity of HE practitioners as knowledge producers.