Education

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Software that monitors students during tests perpetuates inequality and violates their privacy

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.

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Beyond gadgets: EdTech to help close the attainment gap

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.

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Speech recognition in education: The powers and perils

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.

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AI teachers may be the solution to our education crisis

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.

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Can computers ever replace the classroom?

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.

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AI is coming to schools, and if we are not careful, so will it’s biases

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

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Flawed Algorithms are Grading Millions of Students Essays

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

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Student Predictions & Protections: Algorithm Bias in AI-Based Learning

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.

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How will artificial intelligence change admissions?

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.

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