Even before the launch of ChatGPT in November 2022, many employers placed a high value on skills and experience in artificial intelligence (AI). In fact, 80% of employers in the education sector told the QS Global Employer Survey in 2022 that they felt that AI was important to their organisation.
This article explores the growth in the use of machine learning – across a range of sectors and industries including how universities are using the technology to convert student recruitment enquiries and offers more effectively.
What is machine learning?
Machine learning (ML) – a subfield of artificial intelligence (AI) – is a technology that allows machines, such as robots or computers, to learn, adapt, perceive and make inferences using statistical algorithms, geometry and probability theory.
The general use of machine learning is used to increase automation and efficiency, enabling data-driven decision-making and greater personalisation of products and services.
As McKinsey’s Global Survey in 2022 reveals, the adoption of AI technologies more generally is continuing to rise – with more companies than ever before leveraging AI models with ML functionality, such as ChatGPT.
According to McKinsey, 56% of all organisations surveyed in 2021 reported AI adoption in at least one function – up from 50% in 2020 – with this increase seen most in emerging economies such as China, the Middle East and North Africa. In fact, adoption of AI has more than doubled since 2017.
The increased interest in and usage of AI models that rely on ML in recent years can be attributed to a range of factors. This includes the availability of large amounts of data and powerful computer resources, such as Graphics Processing Units (GPU), which have led to very powerful AI functionality that helps humans navigate everyday life more efficiently.
How is machine learning used today?
While the use of ML increases, so does its demand in a wide range of industries:
Healthcare: ML is being used in the medical industry to help ease its pressure on increasingly in-demand helplines and administrative services by leveraging algorithms to better manage patient records or schedule appointments. ML is also utilised to find signs that indicate a specific disease through medical imaging (such as X-rays or MRI scans).
According to Dr Roman Bauer, lecturer in Computer Science at the Centre for Mathematical and Computational Biology. University of Surrey: “AI will soon revolutionise medicine by rendering the diagnosis and monitoring of patients’ diseases more efficient, reliable and fast, ultimately helping decrease the increasing burden on healthcare systems all over the world.”
Finance: Some of the most widely adopted applications of machine learning in finance include fraud detection, risk management, process automation (such as email and browser automation), data analytics (to predict market trends), customer support and algorithmic trading (such as for reading current market prices or to reduce trading expenses).
Marketing: ML is used in marketing to predict the behaviour of customers by finding patterns in their online journeys. It can automatically identify objects, people and even emotions in images and videos, allowing marketers to deliver more relevant tags, captions, adverts and recommendations.
“In the case of marketing, targeted publicity is now very common in many social networks and we expect to see more of this type of application in the near future,” explains Dr Victor Sanchez, Associate Professor of Computer Science at University of Warwick. He cautions: “However, it’s important to always remember that these technologies should be used in an ethical and unbiased manner.”
Education: ML can be used to create personalised learning pathways for individual students – increasing the quality and efficiency of teaching methods in accordance with each individual’s learning style. Using an AI and learning platform Century Tech, for example, students begin by completing diagnostic assessments, which reveal learning gaps and areas for improvement. With AI-powered features, Century recommends topics that students need the most help with, while reintroducing content at regular intervals to prevent students from forgetting what they’ve already learned.
How can machine learning help institutions convert student enquiries, applications and offers more effectively?
To better understand how ML works in the context of student recruitment, let’s look at an ordinary analogy. If an individual regularly searches for innovative cooking recipes on a social media platform such as Pinterest, that platform will provide more recommendations of recipes to try beyond initial results. These recommendations are essentially predictions that have been informed by previous user behaviour from across the platform and form the core of a user’s ‘Picked for you’ category.
In a similar way, ML models can utilise historical and current institutional data, on the behaviour and demographic of student applicants and prospects, to identify the most significant factors that influence the journey along the student pipeline. This information helps institutions understand where and how to put their efforts when it comes to converting enquiries to applications and offers to enrolments.
Although machine learning cannot perfectly predict every outcome, it certainly helps institutions make more informed and intelligent choices in their student recruitment efforts – something QS has witnessed firsthand through the deployment of its bespoke machine learning functionality.
Speaking on the information used by the QS model, Ha Ho Hai, QS Director of Business Intelligence, explains: “We use a combination of demographic (age, country, course), communication preferences (engagement with email or phone calls) and behavioural (levels of satisfaction and engagement with communications). Models may look at up to 200 data points about individual enquirers or students and they are refined throughout the recruitment cycle.”
