Education

This research paper gives arguments for how AI can benefit our education system. It argues that AI can support teachers in giving children the best education whilst not taking away from the humanity of it. AI can be beneficial in aspects such as online tutoring, collaborative learning, and tackling achievement gaps. While it does not […]

In an opinion piece by a University Librarian, he claims that millions of algorithmically proctored (invigilated) tests are happening every month around the world, increasing exponentially during the pandemic. In his experience algorithmic ‘proctoring’ reinforces white supremacy, sexism, ableism, and transphobia, invades students’ privacy and is often a civil rights violation.

An opinion piece in which examples are given of students who have been highly disadvantaged by exam software, including a muslim woman forced to remove her hijab by software, in order to prove she is not hiding anything behind it.

News article which argues that whilst AI facial recognition during exams might be fair, it is both an invasion of privacy and is at risk of bringing unwarranted biases.

A video overview of a report advocating for the use of edtech, or education technology, which includes many AI solutions, in order to close the “Opportunity Gap” between marginalised and “mainstream” pupils.

Weighing up the huge potential of voice recognition technology to gain insights into children’s language and reading development, against a difference of 16% in misidentified words between white and black voices.

This article looks at the global shortage of teachers and how AI might be used to supplement and provide lacking education, and argues that it could be less biased than teachers, thereby resolving inequity.

This article considers the various ways AI can be used during the pandemic to boost virtual learning, focusing on Chinese company Squirrel AI who are reporting good results with computer tutors and personalised learning, and weighing up the risks, such as surveillance of Muslim Uighurs in Xinjiang.

Facial recognition AI, combined with other AI assessment, is used to spot how children are performing and boost their performance. However, there is concern that it may not work so well for students with non-Chinese ethnicities who were not part of the training data.

This article looks at what issues may arise for children from minority and underprivileged communities from replacing teachers with AI.

Automated essay grading in the US has been shown to mark down African American students and those from other countries.

This white paper takes a deeper dive into the data and algorithm used to underestimate the pass rate of students of certain nationalities, looking at how data and modelling can lead to bias.

This short article gives an example of how predictive algorithms can penalise underrepresented groups of people. In this example, students from Guam had their pass rate underestimated versus other nationalities, because of the low number of students in the data set used to build the prediction model, resulting in insufficient accuracy.

This article details the algorithm used to inform A Level results for students who could not take exams due to the 2020 pandemic. The algorithm took into account the postcode of the student, which meant that students from lower income areas were more likely to have their grade reduced whilst students in high-income areas were […]

An outcry over alleged algorithmic bias against pupils from more disadvantaged backgrounds has now left teenagers and experts alike calling for greater scrutiny of the technology.

Case study explaining algorithm bias inherent in grade prediction for A Level students. Demonstrates the physical impact AI can have, if not scrutinised for bias.

This news example gives an example of bias present in an algorithm governing the first round of admissions into a medical university. The data used to define the algorithms output showed bias against both females and people with non-European-looking names.

An article detailing how AI might change admissions in terms of the process, the consequences and how students from some countries could be at risk of bias.

A good introductory video to the use of AI in college admissions. Questioning at what point it is acceptable to completely remove the human oversight in admissions.

Technology to protect remote exam taking against fraud has quickly become necessary as exams move online. Combinations of machine learning algorithms, facial recognition technology and eye tracking software are used to make sure that the person taking the exam is who they say they are and identify cheating.

There are many types of different exciting AI applications being used to enhance learning in the class room, from automated ‘smart tutors’ who can assess pupil performance more accurately tailor learning interventions than humans, to facial recognition cameras which can assess pupils’ understanding through analysing their facial expressions.

A range of different ways to make decisions about admissions to universities and schools are being used, including algorithms which use data pulled from students social media channels. There is concern that the data used will be biased against ethnic minorities due to the smaller amounts of data available and human bias used in creating the models.

In 2020 the use of algorithms to replace exams to determine results hit the headlines when it was shown that they penalised students from state schools and low inclome postcodes.