Artificial Intelligence (AI) is already used in industries like healthcare, energy, and finance. Machine learning - the process by which a computer can use data and past experiences to alter (i.e. improve) its problem-solving - is the technology behind self-driving cars, personalised user experiences, and facial recognition. Its potential in the field of education is only just beginning to be explored.
AI as it is currently used in the education sector shares much of its focus with other industries. A study conducted last year found that the majority of existing literature on the combined topics of ‘machine learning’ and ‘education’ is oriented around increasing efficiency and decreasing costs for universities. However, aspirations for the impact that AI could have on teaching and learning in higher education go much deeper, and, as Google manager Lukman Ramsey explains, are closer than ever to being realised.
AI’s ability to recognise and predict patterns makes it a brilliant tool for matching prospective students with universities and courses that are best suited to their abilities and interests. Automated systems could develop personalised, targeted interactions with students who are in the process of selecting a university course, taking into account any number of factors to help them work out which one would be the best fit. As well as the acceptance rate, typical grade offers, and average living costs associated with individual courses, AI systems can inform their users on student satisfaction, degree outcomes, and career prospects based on data from current students and graduates of specific courses.
This process can be further personalised to illustrate what the prospective student’s individual experience would be like, through the use of Big Data and personality profiles. AI can then be used to inform school teachers on how to personalise and effectively prioritise the support they give each student, consolidating the weak areas of their application and developing their strengths in the lead up to deadlines, interviews, and tests. Nathaniel, Founder & Director of Simply Learning Tuition, knows that a personal approach such as using a private tutor has been shown to improve a student’s chances of getting accepted to their top-choice course at University.
Universities, meanwhile, can use AI systems to initiate interactions with students that would thrive in their educational environment. If developed appropriately, this kind of approach could see vast improvement in access to higher education for students who might otherwise struggle to get the kind of information and educational support that would lead them to completing a successful application.
Once at university, AI can be used to help students adjust to university life and thrive both academically and personally. Universities in Europe and the USA have introduced chatbots to answer students’ ‘frequently asked questions’. They are highly accurate in their answers, and provide students with information on campus layout, course details, and a host of other topics at any time of day or night.
The use of chatbots has benefitted university communities in ways that go beyond simply providing round-the-clock access to basic information. The University of Murcia in Spain found that their chatbot was helping to improve student motivation - an increasingly important outcome given the high drop-out and disengagement rate amongst today’s university students. The data from which these systems ‘learn’ takes the form of the questions that the students ask. This data pool can be analysed to inform university administrators what the pertinent concerns and queries of the students are, indicated by the frequency with which certain questions are asked. All of this comes hand-in-hand with relieving pressure on teachers, meaning they can put more of their energy into teaching matters and research. As this Harvard Business Review article suggests, introducing AI is not a means of replacing teachers, but rather a way of allowing them to specialise in the areas where they are most needed.
Schools and universities already use a number of automated systems to help process and grade their students’ work. Turnitin, an AI system that assesses plagiarism, is used by over 2500 educational institutions worldwide. Many universities also integrate AI to streamline tasks like attendance monitoring and assignment processing. With access to students’ academic records, machine learning would enable teachers and university tutors to tailor content to each student’s ability and interests. Low attendance could be an indicator of an overwhelmed student struggling to cope, or of an under-stimulated student losing interest in the course content. AI systems can identify the difference, and help educators respond appropriately.
In much the same way that AI can match school students with university courses, it can also be used to direct graduates towards a suitable industry after university. Yet educators at the forefront of the AI debate hope that its use in higher education can have an even broader positive impact. Using AI to support mental health is not a new concept: the first ever computerised therapy was trialed in the 1960s. More recently, the development of tools such as Woebot help students cope effectively with mental stressors and learn resilience in the face of new and challenging environments. The technology exists, but the implementation requires careful human monitoring to ensure that computerised systems are giving appropriate and effective help. Knowing how to deal with emotional challenges is a lifelong skill that will help graduates thrive in a new work environment.
As institutes of learning, universities also have a responsibility to prepare their student body for the future. As we touched upon earlier, AI already is and continues to be integrated into industries that impact every aspect of our daily lives. Digitally literate graduates that are familiar with the strengths and limitations of machine learning from a user’s perspective will be an asset to their employer. More importantly, universities are a safe environment in which young adults can learn about the mechanisms of AI, including the ethical considerations of data use and the importance of maintaining human input and intervention. Consequently, they will be better prepared to protect themselves and to develop employable and valuable skills as they enter the world of work.