The integration of AI in education is no longer a matter of choice, but a reality that we must embrace. Rather than shying away, we should focus on leveraging the opportunities it presents.
There has been much debate about the future of AI including calls for its development to be paused in the hope that regulatory architecture can catch up with developments in the technology. This piece isn’t about that debate, though we need to be mindful that this is the backdrop to the conversation within the higher education sector. But whether the adoption of AI-driven technology advances slowly or quickly, it’s clear that it is shaping our present and will continue to shape our future.
It is now over 25 years ago that IBMs DeepBlue beat chess grandmaster Garry Kasparov. Since then, technology, smart devices, AI, and big data have become increasingly sophisticated and pervasive in both our homes and workplaces.
AI represents a significant development in how we research, teach and learn, yet much of the debate within universities has focused on the present threat of AI to maintaining academic standards in assessment. Though these concerns are genuine, the focus of the debate needs to move to the longer term, macro issues of what AI means for universities and academic practice.
Our students will need to be prepared with the skills and attributes they need to thrive in an AI-mediated professional world – so we need to embrace AI in our learning and teaching strategies and our research practice. AI has the potential to reshape academic roles and identities across teaching, research and knowledge transfer activities. Yet the nature of that reshaping will not be found in the technology alone. Instead we need to think about how AI fits into our landscapes. We need to think about the role of the human and the integration and, dare we say it, co-creation, with AI.
Academics are not in competition with machines
Tasks that involve repetition and monotony are prime candidates for technological advancements. For instance, in the field of legal services, where extensive paperwork once required hours of labour from paralegals, AI can now swiftly analyse and summarise documents, answer specific research queries, and drastically reduce the time involved, freeing up time to focus on the human-centric/people side of things, consequently changing the role of the legal professional and the associated skill set required.
Similarly, AI-powered diagnostic systems enable early and efficient patient care, analysing medical scans more accurately and in a fraction of the time a human would take, while robotic surgical procedures simplify complex operations, ushering in undeniable changes to the healthcare sector. Embracing a collaborative relationship with technology to deliver high-quality services is the path to the future.
To ensure our own adaptability, it is essential to enhance our collective emotional intelligence (EQ). Competing directly against data-driven, hyper-efficient computers is impractical. Instead, in future we will need to learn how to work in synergy with technology to deliver quality services. We should focus on leveraging our unique human abilities, such as providing imaginative and innovative solutions, tailored to individuals and their specific needs.
The convergence of EQ-powered humans and AI-powered technology has the potential to revolutionise our lives and work. Amplifying our emotional intelligence means nurturing people skills, including effective communication, persuasive abilities, negotiation tactics, building networks, resilience, curiosity, creativity, autonomy, relationship management, and conflict resolution. These skills have often been referred to as “soft skills” and this needs to change.
At Teesside University our Future Facing Learning approach embeds these human skills across our curriculum. As Europe’s first Adobe Creative Campus, we’re committed to creative, flexible, and inclusive learning, teaching and assessment and we’re excited to see the evolving role of AI in enabling our students’ creativity to flourish. By focusing on enhancing our people skills, we can thrive in an era where human interaction and emotional connection remain indispensable, even in the presence of advanced technology.
Critical thinkers, not followers
We are in no doubt that AI will have profound implications for the sector, but our response needs to be measured. AI may disrupt some aspects of academic life, and we need to make some significant changes to embrace it, but it won’t replace the university anytime soon.
If we look to the recent past, technologies like Wikipedia were to render universities obsolete, and before that, it was the internet itself that was predicted to bring about the demise. Even the scientific calculator had its fair share of vocal detractors. However, none of these advancements spelled the end for education. Instead, we have learned to coexist with technology and embrace it (particularly during and since the pandemic). AI has the capability to accomplish certain tasks, but that does not mean we should refrain from teaching or having students learn about it – AI is just another tool. We did not stop teaching maths due to the advent of the calculator.
Students must become critical thinkers who can assess the outputs of AI systems and evaluate the nature of knowledge and truth. As academics, for years we’ve been educating our students about Wikipedia for example – that it’s not a reliable source of evidence, of accuracy, or of unbiased truth. That it is important to compare and critically analyse multiple sources to inform our arguments, theories, and work. Given that some AI applications work by sweeping the internet, there is inherent bias in any output. The message remains – this is not a reliable source of evidence, of accuracy, or of unbiased truth.
While access to knowledge and information has become democratised, the ability to utilise it intelligently has become the essence of expertise and intellectual competence. The learning experience should empower students to explore AI, understand its limitations, and consider its potential applications within the context of their respective disciplines. Proficiency in the effective use of AI has become a literacy in its own right. Therefore, it is imperative that we collaborate with students to develop a shared understanding of the impact of AI on knowledge management.
To enable students to effectively work with AI, they must possess the essential skills of asking the right questions, critically analysing information, and applying the responses to maximise their learning gains. AI in education is beyond ChatGPT. Understanding the limitations of a breadth of AI tools and fluently utilising them in conjunction with other sources to ensure credibility and reliability are crucial academic abilities.
The utmost value of universities lies in their role as intellectual hubs, fostering unhampered and open inquiry, constant questioning, and rigorous testing. They are environments of questioning rather than deference, analysis rather than ideology, intellectual openness rather than comfortable exclusion. Universities teach students to think, not merely follow. Our goal is not to have students compile resources and regurgitate conclusions in a structured and robotic manner. “Information” – not knowledge – is readily available and can be accessed and retrieved on demand.
Ironically, as a result of AI, often you don’t even need to go looking for information – it finds you. It is therefore more important than ever that we strive to create environments where students are critical: where they can cultivate confidence in their thinking and apply their thoughts, fostering opportunities for imagination, creativity, and curiosity, turning information into knowledge. The value of knowledge lies in asking questions, fostering an insatiable hunger for understanding, and actively engaging in problem finding and problem solving.
Learning that is deeper and more personal
If we shift our focus from relentlessly searching for plagiarism to rewarding originality, we can design assessments that provide students with authentic learning experiences. These assessments immerse students in real-world tasks and practices, enabling them to apply critical thought and take action within a meaningful context.
By doing so, students develop self-reflection skills and have the opportunity to cultivate their personal and professional attributes. They can explore their aspirations, values, and social contributions, discovering their holistic selves through their learning journeys.
Embracing portfolio-based assessments that align with real-world work activities holds significant potential, particularly in partnering with industry to design and deliver learning experiences. Such assessments not only better prepare students for their future endeavours but also foster interconnected and cross-disciplinary learning, mirroring the complexities of real-world challenges.
In order to do this however, we need to rebalance our focus on assessments. Students are assessment driven and AI has the capability to end assessment focused learning. If the sum of the module is the assessment, AI can create this. If, however, the focus is on the educational journey, including the production of a piece of work, this is much harder to replicate within technology alone. We need to strive to focus on the human/computer interaction. We are, after all, social beings.
AI presents opportunities to offer students personalised developmental pathways. To enable a course to adapt to an individual student, it requires students to develop self-awareness of areas they thrive in and areas for growth. It can help students to personalise their student journey and navigate large and complex institutions, technological infrastructures and procedures. Additionally, self-awareness encompasses the ability for students to collaborate with educators in designing curriculum and learning activities, leveraging their expertise and experiences.
To fully harness our students’ emerging expertise, we must also design flexible and inclusive assignments that encourage them to integrate experiential knowledge as a scholarly resource. The unique experiences students bring to the classroom, regardless of their academic level, enrich the knowledge landscape and enable them to consider the social and ethical implications of their work. This is an aspect that AI tools cannot replicate. Students’ personal experiences and lived understandings enable them to develop visions of equity and justice grounded in reality, potentially transforming scholarly conversations.
It is imperative that we create more opportunities for students at all levels to engage in original research, participate in fieldwork, co-create with peers, conduct interviews, collect data, and leverage their insights and experiences to advance society. These are endeavours that AI tools cannot accomplish or replace, but could be leveraged to assist.
AI literacy for all
Disciplines and professions are different, and so we may need to approach AI in different ways. AI literacies within subject disciplines should be actively built into our curricula and assessment practices – and fast. Preparing students for the world of work, encouraging the use of all digital tools at their disposal (including AI) and designing assessments that encourage the creative use of such tools will deliver revolutionary and disruptive change that is much needed as sectors outside of higher education are embracing the changes and affordances from AI in their practices.
AI literacies refer to the knowledge, skills, and understanding necessary for individuals to effectively engage with and make informed decisions about artificial intelligence technologies. Developing AI literacies involves gaining a deeper understanding of the capabilities, limitations, and ethical considerations associated with AI systems.
Technical understanding: Familiarity with the basic principles, algorithms, and techniques used in AI, such as machine learning, neural networks, and natural language processing. This includes understanding how AI systems are trained, the types of data they require, and the underlying mathematics and statistics involved.
Data literacy: Knowledge of data collection, processing, and analysis methods. This includes understanding data formats, data quality, biases, and privacy issues. Being able to interpret and critically evaluate data used to train AI models is crucial.
Ethical awareness: Understanding the ethical implications and societal impact of AI. This includes awareness of potential biases, discrimination, privacy concerns (think AI-generated imagery and deepfakes), and ethical dilemmas arising from AI deployment. It also involves considering issues like fairness, transparency, accountability, and the potential impact on employment and social structures.
Critical thinking: Developing the ability to evaluate and question AI applications critically. This involves understanding the strengths and limitations of AI systems, recognising their potential risks and biases, and being able to assess the reliability and credibility of AI-generated information.
Human-centred design: Recognising the importance of designing AI systems that align with human values, needs, and preferences. This includes understanding user experience (UX) design principles, considering accessibility and inclusivity, and involving diverse perspectives in the development and deployment of AI technologies.
Collaboration and communication: The ability to work effectively with interdisciplinary teams and communicate AI concepts and implications to a wide range of audiences. This includes being able to explain complex AI concepts in a clear and understandable manner, facilitating discussions, and collaborating on AI-related projects.
Continuous learning: Given the rapid advancements in AI technologies, having a mindset of continuous learning is essential. This involves staying updated with the latest developments, exploring new AI applications, and understanding evolving ethical and legal frameworks.
Ubiquitous AI: AI is already prevalent in many professions – earlier we mentioned the legal and healthcare sectors, but there are many more examples where our graduates will step into roles where AI is unavoidable or where anyone lacking AI literacy within the context of their role/profession will be severely disadvantaged in terms of effectiveness, efficiency, self-development and career progression when compared to their AI literate peers. Consider the role of a researcher or data analyst/scientist (which can span many sectors and professions); essentially roles that collect, analyse and interpret data. AI as a companion to the human is able to carry out a high volume of complex tasks or calculations quickly and accurately, perform visual simulations or predictions based on interpretation of data or a set of parameters, and so on.
Developing these AI literacies helps individuals navigate the increasingly AI-driven world and make informed decisions about AI adoption, while also contributing to responsible and ethical AI development and deployment.
Discussion of AI should not fall into the “digital first” trap. Technology should not be forced into places it does not belong or shoehorned in to satisfy future skills agenda. AI, as with all digital tools, should be embraced, but needs careful consideration as to how this fits into the broader landscape, starting with our greatest assets, our staff.