Using AI to deliver scaled pedagogy is HE’s only available play

The global cost pressures imposed by sector expansion oblige universities to embrace technology that is finally fulfilling the hype, says Anthony Finkelstein

十一月 4, 2024
A robot delivers a virtual lecture
Source: PhonlamaiPhoto/iStock

I cannot claim great powers of foresight, much though I would wish to.

I first encountered artificial intelligence (AI) in the late 1970s, at the very end of the first “AI winter” of low funding and widespread scepticism about the field’s prospects for success. That winter was initiated in the UK by the Lighthill Report of 1973, which gave a very negative assessment of AI’s ability to live up to the early hype and prompted the government to cut most of its public funding.

I was interested in the field but not more than that. The programming tools, specifically Lisp (and later Prolog), did get my attention, though. I could see the relevance of knowledge-based approaches and so-called expert systems. So, eventually, I became an “Alvey baby”, funded to pursue postdoctoral research as part of the UK government’s Alvey programme to deliver an AI-led fifth generation of computing technologies. This gave rise to a longstanding concern with logic and symbolic reasoning that shaped a good part of my later work in software engineering.   

But I was certainly never very engaged with the philosophical and other debates that swirled around AI, as they, of course, do now. Although I appreciate abstraction, I have little appetite for speculation. This is probably a personal shortcoming but one that I am unlikely to be able to shed at this stage.  

I recall my first use of GPT. I was profoundly shocked at the behaviour of the system – at what it could do. Indeed, despite the fact I “knew” how it worked in some reasonable detail, I could not comprehend it. I did not understand what sheer scale (amplified by some neat engineering) would yield. I had to repeat to myself that I was not seeing search but, rather, the results of a statistical process giving rise to predictions at the level of words and text fragments. I still do. This shock is important to recognise and to hold onto. We have crossed a frontier.  

The impacts of technology and the ways in which innovations are applied have been much studied. For advanced technologies, the translation from lab to broader uptake generally takes an extended period. Although experienced by users or consumers as rapid and disruptive, technology shifts are often, when viewed at a distance and in context, relatively slow. There are, obviously, inflection points and network effects that come into play, but generally applications emerge incrementally and there are lengthy gaps between the early adopters, early majority, late majority and laggards.

This is not what is happening with AI. Take-up is progressing with extraordinary rapidity, productivity opportunities and applications are proliferating, the leverage that can be secured from integration with existing platforms and data resources is evidently very large and more are emerging on an almost daily basis. This is so much the case that in enterprises, impatient with even this accelerating pace of deployment, individual AI use for routine tasks has become commonplace. It is impossible to say with any precision where this might lead, not least because the models themselves continue to develop at extraordinary speed.  

So much for reflection. These changes certainly mean substantial large-scale transformation for higher education. We now have the capability to give our students highly personalised educational experiences, individual feedback and sophisticated analysis and problem-solving assistance. Institutionally, we can streamline our business processes to be more responsive and efficient. And our research will benefit from novel forms of scholarly exchange and exploitation of knowledge.

This is not speculation; this is a straightforward account of what is happening. Given also the global expansion of higher education and the associated cost pressures, reflected in the UK’s current financial sustainability crunch, using AI to deliver scaled pedagogy is simply our only available play.

I am not generally a “hyper” of technology but in this case I am content to be mistaken for one. There is no stand-back option: failing to engage with AI is straightforwardly to neglect the mission of higher education.

Sir Anthony Finkelstein is president of City St George’s, University of London. This is an edited version of one of a collection of essays published today by London Higher.

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Reader's comments (7)

The article lacks one crucial feature. Any reflection on what HE is and how,where,and why "AI" fits in that picture. There is more to He than 'delivering pedagogy' and 'administration' Although th search and word shuffling of AI might work for some aspects of admin, even that is not so clear. The people doing that work bring wwith them human insight and empathy that is Personalised tuition was the dream of the CAL community in the 80s it never amounted to much. Given the choice between sitting in full classes with fellow students and expert staff or CAL labs students voted with their feet and CAL labs were soon reconverted to classrooms. not demonstrable in AI, and any cost savings or scale might be lost if failure in AI performance leads tosystemic failures, loss of reputation and suits. As the author is well aware, software does not come with guararntees or warranties and that includes AI. A long standig foundation of universities is that the pedagogy that is taught is the fruit of reseach and the close link between research andteaching is what provides the university with its legitimacy. Although AI might be used as an itntellectual crutch by some reserarchers, it has not demonstrated theability to supplant researchers in terms of critical reasoning let alone creativity. Therefore AI cannot play a full role in university pedagogy. Attempts to shoehorn it into this will lead to two tier solution. courses ad institutes that use it and those that do not. Students and funding will move accordingly and any benefit of cost savings will be lost. Rather than a life buoy bobbing in the sea of drowning institutions, AI is a siren luring institutions towards the rocks of irrelevance. Some will founder leaving the sea clearer for others to find safe passage. The challenges of low funding will not be solved by wishful thinking.
This is sort of a second rate puff-piece. I reckon AI could do a better job.
This is sort of a second rate puff-piece. I reckon AI could do a better job.
This is sort of a second rate puff-piece. I reckon AI could do a better job.
Here is what AI generated something much better.....?? You judge Artificial Intelligence (AI) is transforming higher education in profound ways, both in the present and with exciting potential for the future. Here’s a look at how AI is currently being used and what we might expect moving forward: Present Use of AI in Higher Education 1. Personalized Learning: AI-driven platforms like adaptive learning systems tailor educational content to individual student needs, helping them learn at their own pace. Tools such as Coursera and Khan Academy use AI to recommend courses and resources based on a student’s progress and performance. 2. Administrative Efficiency: AI is streamlining administrative tasks, from admissions to grading. Chatbots like Ivy.ai assist with student inquiries, while automated grading systems save educators time and provide instant feedback to students. 3. Enhanced Accessibility: AI technologies are making education more accessible. For instance, speech-to-text and text-to-speech applications help students with disabilities. Tools like Otter.ai transcribe lectures in real-time, aiding students who are deaf or hard of hearing. 4. Data Analytics: Universities are leveraging AI to analyze vast amounts of data to improve student outcomes. Predictive analytics can identify students at risk of dropping out and suggest interventions to keep them on track. Future Prospects of AI in Higher Education 1. Intelligent Tutoring Systems: Future AI systems could provide even more sophisticated tutoring, offering personalized feedback and support in real-time. These systems might use natural language processing to understand and respond to student queries more effectively. 2. Virtual Reality (VR) and Augmented Reality (AR): AI combined with VR and AR could create immersive learning environments. Imagine virtual labs where students can conduct experiments without physical constraints or AR applications that bring historical events to life in a classroom setting. 3. Lifelong Learning Companions: AI could evolve into lifelong learning companions, guiding individuals through their educational and career journeys. These AI mentors could help with everything from skill development to career planning, adapting to the learner’s evolving needs. 4. Ethical and Inclusive AI: As AI becomes more integrated into education, there will be a greater focus on ensuring these technologies are ethical and inclusive. This includes addressing biases in AI algorithms and ensuring that AI tools are accessible to all students, regardless of their background or abilities. Conclusion AI is already making significant strides in higher education, enhancing learning experiences, improving administrative efficiency, and making education more accessible. Looking ahead, the potential for AI to further revolutionize education is immense, promising more personalized, immersive, and inclusive learning environments. As we continue to innovate, it’s crucial to ensure that these advancements are used ethically and equitably, benefiting all students.
I find myself torn. On the one hand, I was long a skeptic or even a cynic about Ai. That was changed by three recent developments. Firstly the latest AIs can write convincing outlines of answers to exam questions if long smugly thought required to much critical thinking to be susceptible to AI. Secondly, I recently put my latest research paper into an AI and got it to generate a podcast from it. The results were astounding. Far better than most science podcasts I listen to. Thirdly I tenderly spent a day trying to underside a concept I've been telling my self I needed to understand. After reading the foundational work, I asked an AI questions on the bits in struggled with. Was it airways correct? No. But I knew enough to be able to take its explanations back to the original work and work out of it made sense or not, and question the AI further when it was clear it was wrong. What was interesting is that I needed to be an active participant in this process - I couldn't just sit back and absorb what either the ai or the article in question said. If anything I see this as the downfall of AI - is only good where the student is a more active participant b than today's students are in my view.
AI is a major challenge for the sector, and not sure it is being well-handled in the UK.
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