Understanding AI in Education

AI outside of education 

In this blog we explore the use of AI across a few very different sectors – Museums & Galleries, Recruitment, Air travel, and Banking. The purpose is to see how other sectors, each at their own stages of AI maturity, are innovating with the same types of AI tools which we are exploring the use of in education. These might serve as inspiration for your work in education, or they may just be an interesting window into how other sectors are using AI tools day to day.   

Museums & Galleries 

Museums & Galleries have increasingly embedded technology to enhance the visitor experience, and institutions are keenly exploring how AI can provide even deeper insights into visitor behaviour.  

In 2021 the Istituzione Bologna Musei began a pilot project which placed a series of cameras and sensors around its galleries to collect data on visitor behaviour. This allowed the museum to track visitor’s pathways through the gallery, seeing which pieces they stopped at and measuring their engagement with the artwork based on facial expression and posture. 

The information gathered might provide curators a better understanding of how viewers interact with the paintings on display. Wider museum issues can be brought to light too – for instance when analysing visitor behaviour, the museum also tracked how well visitors were adhering to covid-19 restrictions. 

AI has also been featured as a component of the exhibitions themselves. The award winning Recognition exhibition at Tate Britain utilised visual AI tools (including object and facial recognition) in combination with Natural Language Processing (NLP) to relate collection items to modern day images. The connections made by the AI programme helped visitors look at the collection in new ways and gain an understanding of the AI itself.   

 A recent project from Carnegie Mellon University added a virtual assistant to a hands-on exhibit for children. The gorilla themed assistant, NoRilla, replaced the frequently ignored museum signage, guiding the children through the experience and challenging them further with questions. The results were that children engaged with and learned more from the interaction with NoRilla compared to a standard hands-on exhibit.  



Hiring and recruitment is a field that has experienced increasing adoption of AI tools, the 2022 Littler European Employer Survey Report found that 28% of organisations surveyed were already using AI to support hiring and a further 19% were intending to do in the next year. There are AI tools available at almost every stage in a hiring journey too – from hosting a virtual careers fair, using a chatbot to speak with potential candidates, to holding the interview virtually.  

Notably, generative AI tools have made it possible to create informative job descriptions quickly by prompting the tool with the key aspects of the role and allowing the generator to produce the full description. Services like then go a step further and provide writing support which directs the writer to more accessible, neutral language in an effort to reduce unconscious bias in job postings. 

Bias is a key concern in the use of AI for recruitment and notably some providers have had to make changes to prevent their services from perpetuating harmful biases. In 2021, Hire Vue, a company that offers automated, AI-assisted video interviewing services, withdrew the service’s visual analysis feature, which employed facial recognition to examine candidates’ expressions. The change was made following criticisms of bias in Hire Vue’s system as well as research conducted by Hire Vue itself which indicated that visual analysis did not correlate well enough with strong job performance.  


Air Travel  

If you’ve used a search engine to find the best price for a flight, then AI has likely already been involved to help you make optimal travel plans. Travel technology company Amadeus have identified though that these searches take considerable computing power and generate high carbon emissions. To combat this, they are also using AI to make their flight search more energy efficient. Their model is trained on previous customer searches to predict which flight combinations are most likely to be good value for the searcher, allowing them to optimise their search engine and save over 43 tons in CO2 emissions a year.   

At the airport there are ambitious, organisation wide implementations like Schipol airport’s aim to utilise AI in order to be a completely autonomous airport by 2050. The AI driven airport already utilises a range of AI tools across its operations today, including in the ‘turnaround insights’ project which uses deep learning to analyse live aircraft footage in order to optimise the many vital processes involved in receiving an incoming flight and preparing it to depart again safely. While lost luggage is handled by the Lost and Found service which cleverly uses a combination of NLP and image recognition (including identifying colours, brands and serial numbers) to match lost luggage with its owner.   



The banking industry has been embracing AI for customer service with chatbots and virtual assistants now commonplace for most major banks. These have evolved too and allow customers to not only ask questions but perform actions like transferring funds and setting card limits. HSBC notably has even brought digital assistants in branch by deploying the Pepper robot, developed by SoftBank Robotics and equipped with sentiment analysis by Affectiva, in some of its U.S. branches to interact with customers in person (in robot?).  

Increasingly AI tools are improving banking customers ability to self-serve which reduces customer wait times and increases capacity. Over the last five years many major banks have begun to allow new customers to open accounts directly through their mobile apps. The use of biometrics and facial recognition allows new customers to open bank accounts securely by recording short videos or taking selfies on their phones. This takes the time to open an account down from days to minutes and improves accessibility for customers who cannot visit branches in person. Notably NatWest found that fraudulent account applications significantly decreased after launching their in-app account opening process. 

 AI is playing an important role in fraud prevention behind the scenes too. Fraud prevention relies on being able to identify unusual activity, as such predictive models can be hugely helpful at improving fraud detection systems. With AI, banks can parse through massive volumes of customer data to build more accurate profiles of customer transactions so that any abnormal activity can be quickly flagged. Digital bank Monzo have excelled at using machine learning to combat financial crime and their fraud prevention model was recognised at the 2021 Tackling Economic Crime Awards (TECAs). 

Find out more by visiting our National centre for AI page to view publications and resources, join us for events and discover what AI has to offer through our range of interactive online demos.

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