Artificial intelligence is disrupting higher education
Traditional contact universities need to adapt faster and find creative ways of exploring and exploiting AI, or lose their dominant position.
Globally, the traditional contact university is facing many challenges. There are some experts who believe that new innovations and social changes are disrupting the role of the traditional contact university.
This is not altogether surprising given our local experience with the “fees must fall” movement and the shift to hybrid and online teaching and learning approaches during the COVID-19 pandemic.
The high number of students without access to higher education remains a major problem, and many traditional and overly teaching-focused universities worldwide are constrained by limited educational capacity offered via brick-and-mortar facilities, by the lack of PhD-trained faculty and inadequate development of scholarly researchers.
These challenges are further compounded by factors such as the global economic downturn, fiscal pressure, reduced budgets, rising tuition, price-sensitive students, and state pressure to admit underprepared students from beleaguered basic education systems.
The emergence of for-profit universities and technical training institutes, though currently dubious in quality and expensive, are increasing competition in the higher education playing field.
Higher education professionals have a responsibility to shape AI as a force for good.
The more agile among the for-profit universities are taking advantage of outcome-oriented accreditation standards and are rapidly increasing their focus on student educational outcomes that emphasise reskilling and future workforce readiness. As a result, they are beginning to offer not just more convenient access but also more marketable skills.
Traditional contact universities that ignore these innovative for-profit universities do so at their peril.
Clayton Christensen’s theory of disruptive innovation predicts that in most sectors, competitors that develop simple and accessible products and services to target the low-end or non-consumption segment of the market will eventually replace the market leaders plagued by higher cost structures and the blind allocation of scarce capital to their outdated business model.
Similarly, in the higher education sector, many traditional contact universities are plagued by costly institutional inertia, monolithic instruction modes, hierarchical interdependencies, curriculum standardisation and the stickiness of past pedagogical practices.
By enabling new forms of multimodal education, the innovative use of AI and new learning technologies by new entrants offering decent education that targets the downstream student market threatens the relevance of traditional analogue higher education offerings.
Types of AI revolutionising higher education
Gartner’s 2023 CIO agenda insights for higher education suggests leaders will be turning to AI to address some of the abovementioned challenges.
The key types of AI that will impact higher education include machine learning (ML), natural language processing (NLP), computer vision and robotics.
ML involves algorithms that can learn from data and make predictions or decisions based on that learning. NLP deals with how computers process and understand human language. Computer vision involves the development of algorithms that allow computers to understand and interpret visual data, such as images and videos. Robotics involves using algorithms to control robots and automate physical tasks.
Microsoft, SAP, SAS, IBM and other large companies have already incorporated these types of AI capabilities into their product offerings, so higher education institutions will have to also incorporate AI into their teaching and research.
AI has even become applicable in the use of office productivity software, such as Excel, ERP applications, business process automation, intelligent process automation, data mining and predictive analytics.
Consequences of an AI-powered classroom
AI has the potential to be both a personal tutor for the student and a personal assistant to the educator. From grammar checking to grading, the AI-enabled classroom is already assisting educators.
Educators all too often burdened by over-assessment and administration can use AI assistants for deriving teaching and lesson plans, student consultations, automatic grading and tracking, and analysing student performance.
Hopefully, educators can now focus more on creative skill development and more immersive student learning rather than superficial learning that assesses student memorisation abilities.
Some of these AI tools that can enable more effective teaching and learning approaches include generative AI chatbots, AI research assistants, AI paraphrasers, text-to-speech converters, adaptive learning courseware, assistive technologies, early warning systems, AI-powered chatbots for administration tasks, AI-powered grading, transcription technologies and enhanced online discussion boards.
Brands such as ChatGPT, Quilbot, Perplexity and Grammarly, to name a few, are already playing a role in higher education and their capabilities are increasing rapidly.
While these tools offer opportunities to personalise learning, develop and elevate critical thinking skills and provide additional support for students outside the classroom, misusing them can result in the loss of reasoning, writing and reading skills, and cheating on assignments and exams.
It may further reinforce the skimming, headlines and soundbite culture that social media is creating and widen the gap between the motivated and unmotivated students. Furthermore, research prestige will remain elusive for overly teaching-focused universities, as elite research universities move first to exploit AI and continue to widen the gap at producing high-quality research published in the fixed number of A-list journals and conferences.
From exaggerated opinions to nuanced discussions
Science fiction writers and Hollywood have always painted a gloomy if not dystopian future about the consequences of AI. And in more recent times, AI experts and entrepreneurs themselves have expressed deep concerns about the impacts of AI.
My own view is that it would perhaps be wiser not to offer any absolutist view about AI.
Over the years, many experts have made predictions about IT that in hindsight turned out to be embarrassingly wrong. For example, the so-called experts were wrong with their estimates about the growth potential of the PC in the household market, the severity of the Y2K problem, and even in recent times the market share potential of the iPhone.
Perhaps we need to focus our attention on shaping the use of AI instead of making dystopian or even overly utopian predictions. Notwithstanding, higher education professionals have a responsibility to shape AI as a force for good.
There are going to be many challenges in incorporating AI into higher education, but there is hope if we educate students to use AI to solve social problems, foster economic growth, and improve decision-making in our personal lives and in our organisations. We also need to address policy, legislative, ethical, moral and societal risks and implications of AI.
Traditional contact universities will need to develop new business models and value propositions for students on account of AI and new learning technologies.
Quality standards of research, plagiarism, fact-checking (fake news, conspiracy theories), algorithm bias and discrimination, regulation and rules (guardrails), intellectual property rights, ethical considerations, and lack of AI literacy skills should be some of the key themes that become part of the discussions and actions in higher education going forward.
Professor in Information Systems (IS) at the Wits School of Business Sciences.
Rennie Naidoo is a professor in Information Systems (IS) at the Wits School of Business Sciences. An established NRF-rated researcher, his focus areas include data science, sustainable IT, artificial intelligence and cyber security. Naidoo earned a Master of Commerce in Information Systems (with Distinction) from the University of the Witwatersrand and a PhD in Information Technology from the University of Pretoria. His research appears in leading scholarly journals, such as the Journal of Strategic Information Systems and the European Journal of Information Systems. He has presented at premier academic conferences, including the International Conference on Information Systems and Hawaii International Conference on System Sciences. Devoted to lifelong learning and scholarship, he mentors students at the Master's and PhD levels at Wits. He actively serves the IS profession as a member of the Association for Information Systems and the South African Institute for Computer Scientists and Information Technologists.
Rennie Naidoo is a professor in Information Systems (IS) at the Wits School of Business Sciences. An established NRF-rated researcher, his focus areas include data science, sustainable IT, artificial intelligence and cyber security.
Naidoo earned a Master of Commerce in Information Systems (with Distinction) from the University of the Witwatersrand and a PhD in Information Technology from the University of Pretoria. His research appears in leading scholarly journals, such as the Journal of Strategic Information Systems and the European Journal of Information Systems.
He has presented at premier academic conferences, including the International Conference on Information Systems and Hawaii International Conference on System Sciences.
Devoted to lifelong learning and scholarship, he mentors students at the Master's and PhD levels at Wits. He actively serves the IS profession as a member of the Association for Information Systems and the South African Institute for Computer Scientists and Information Technologists.