Artificial intelligence is making its way into higher education, with students leveraging it to explore, understand complex ideas and practice new skills, while lecturers are experimenting with it to teach, assess and research. Universities are also looking to benefit from the technology by employing artificial intelligence to help make administrative operations more efficient and offer better support to students.
All of this is happening, and the Ed Tech market is growing at a fast pace too! But not only will this influence the Ed Tech market, but it has a much wider effect in universities in the way it teaches, evaluates, and researches; students should be trained in order to prepare them for their future.
Understanding AI’s Role in Higher Education
AI in higher education goes beyond just generative chatbots. From identifying patterns in data sets, using natural language processing to break down languages, and creating new text, code, images, and much more, machine learning has a wide range of functions.
While some implementations of artificial intelligence are more in the background, such as using different technologies to track and organize information about student engagement or to aid in administrative work, there are several that are more directly used by students and lecturers. For example, generative AI can be used as study assistants and tools to create and grade assignments, as well as find information and generate lesson plans.
Because of its versatility and relative accessibility, generative AI has garnered much attention, as it can be used by those without any technical know-how. While a student can use it to help them understand a complex topic, lecturers can use it to come up with new ideas for classwork.
However, simply having the ability to use AI does not mean that it should be used, or that its use would be beneficial. The OECD’s Digital Education Outlook highlights that implementing generative AI in education in appropriate ways, and not relying on it too much for intellectual tasks, as it could inhibit one’s growth in that field.
The real test is not in what can be done with AI, but rather if what is being done with it is the right thing.
Creating More Personalised Learning
One of the areas in which artificial intelligence can make the most impact is personalised learning.
Courses at universities are filled with heterogeneous groups of students with varying levels of experience, capacity, needs, and aspirations. Individual support to each student is resource-intensive, especially in large tutorials.
AI can provide individualised support within the educational process, for instance, by explaining concepts in multiple ways, offering practice problems, and identifying areas in which a student requires additional support. An AI-driven study assistant can offer help in a range of languages or provide hints and guidance when studying challenging topics in technology and engineering.
Such assistance can be especially valuable outside regular teaching hours. Students often have to engage in self-guided study, and therefore frequently have questions to which they cannot expect a lecturer to answer.
Personalisation, however, should not replace the core learning experience by eliminating direct and interactive learning experiences, such as tutorial sessions, cooperative work, or practical activities. Above all, AI should provide additional valuable support while leaving education as a personal and human-centric experience.
How AI Could Support Lecturers
AI is also changing the way academic staff members complete their day-to-day tasks.
With time-consuming tasks such as preparing teaching resources, designing questions, collating information, and reviewing masses of feedback, AI can help to reduce some of the boring and repetitive parts of these jobs.
For example, an academician can use AI to help design their quiz questions and edit them so that they fit better with their lesson plan.
Another example would be using AI to collate mistakes made by students on assignments. This information is useful as it can show what concepts the majority of students are struggling with, so that teaching plans can be adjusted to reflect this.
Although it could be tempting to rely on these AI resources to collate and create lesson plans and information, human intuition and effort are still required as incorrect information could be presented.
Why Assessment Needs to Change
Assessment is one area where generative AI could have a real impact.
With AI being able to generate essays, summaries, code, and answers to questions, we are left with the question: how do we assess something when technology can produce an acceptable, if not excellent, answer in seconds?
A solution could be to focus more on the process of learning and have students demonstrate their understanding in different ways, such as describing how they formed an argument, discussing how they decided on a project, or verbally justifying their responses.
Hands-on work and supervised work could be used to show one’s skill and understanding as well.
Essays are not going anywhere for the foreseeable future; nevertheless, they are just one of the many professional skills required by many professions and therefore, may offer an opportunity to both test a student’s understanding of what a subject is about but equally how good they convey this.
Machine learning is so advanced it can actually be integrated into an assessment: have students critique an AI-generated answer, or edit an AI-generated piece of writing. It doesn’t actually matter how they do that as long as students can prove that they’ve understood it, and it might well afford opportunities to introduce some of the AI tools another generation of graduates will grapple with in a workplace entertainment sector.
Maintaining Academic Integrity
New technologies have made it a very complex issue, however academic integrity. Generative AI tools can definitely be a fantastic source of information, however these systems sometimes return inaccurate assertions and erroneous references. Use of AI to create materials for submission as original student work and use of these resources to do things not intended by the instructor will cause multiple problems.
Text detection software is not always able to recognize texts written by generative AI, and expecting these programmes to work perfectly brings expectations of errors. Therefore, the academic community must look beyond automated detection and analysis.
There should be direction on how to use AI resources, when such tools may be used, when is the disclosure of their use required and when are they prohibited and what happens if someone uses them anyway. Various requirements can be applied based on the goal of an assignment.
Through the report, titled “Generative AI in Education and Research: Opportunities for a Human-Centric Approach,” UNESCO offers a human-friendly approach to exploit generative AI resources as well as privacy considerations on the use of this technology and institutional policies.
Protecting Privacy and Personal Data
Because AI is data reliant, privacy becomes a very real issue in universities.
Universities hold quite a bit of data and before making the decision to use AI as a service, understanding how this information is obtained, stored and processed is important.
For students and staff who supply external entities with information, this need is manifested even more acutely, thus institutional data-sharing guidelines must be set and the security steps taken by third-parties evaluated.
Finally, we need to make sure we raise awareness about the use of AI and make its application clear so that students and staff will not be led to incorrect assumptions on its integration into the educational process.
Keeping this into consideration, the implementation of AI in universities is clearly necessitated by regulation, education and transparency.
Could AI Widen Educational Inequality?
While AI is more than capable promise for academic assistance, it would not be accessible to every student.
Students have different levels of access to reliable technology options, cutting-edge artificial intelligence, and greater overall competency with technology than others.
This might lead to a disparity in education where students are able to access differing resources depending on who they are.
Another university may also have an advantage in certain resources compared to others.
Instead, better funded universities may have more available security and confidence in the administration for staff. It is similarly essential to glance at how open AI will be for the understudies who are of different needs. Alternative options should also be available to students who are unable to use certain technologies.
Fairness should be taken into consideration before implementing such a program, rather than addressing consequences after the fact.
Preparing Students for an AI-Enabled Workplace
Institutions have a duty too to ensure graduates are equipped for workplace environments increasingly dominated by AI. Inevitably, not all graduates need to learn how to develop and work with AI – but all need to appreciate how it functions, its potential use, and its shortcomings. They must be able to ask about anything AI outputs; they should be able to verify any sourcing; identify bias; handle data securely; and be aware when the use of human judgement and expertise is called for.
As before, a solid grounding in the subject itself is paramount – for anyone able to grasp the intricacies of their discipline will be all the more prepared to identify errors or misinformation, whether that output stems from human or artificial intelligence. The ability to think critically, research, solve problems and communicate skills will always be central to being a responsible technology user.
AI and the Future of University Research
Research also has its own emerging role for AI. Researchers could employ AI for managing the scientific literature, the analysis of big data, coding assistance, and identifying patterns in information. This could alleviate researchers of time-consuming, repetitive tasks and help make exploration of volumes of research manageable.
Generative AI may also help with drafting and editing, but researchers are still ultimately responsible for ensuring that their findings and the information therein are factually correct and research sound.
It is vital to exercise particular caution when using AI on sensitive research data, and considerations such as privacy and ownership, research integrity and reproducibility need to be taken into account before integrating AI into research.
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