Four Myths about Generative AI in Education

I outline four common misconceptions about the use of Generative AI which are widespread in Higher Education debates about the use of these tools: that it is possible and practical to detect the use of AI in writing, that text produced by GenAI is bland, repetitive or predictable, that GenAI tools struggle to cite sources accurately, and that more creative or reflective assessments are harder to complete using AI.

Minding the Gaps: Statistical Misrepresentation in Attainment Gap Research

Political interests configure the stories we tell with data. Closing the gap in attainment between disadvantaged students and their advantaged contemporaries is pivotal to an agenda to use education as a positive social force. But both the measurement and representation of this gap is politicised, skewed and open to manipulation. This paper shows how two organisations with inverse aims represent—and misrepresent—their measure of the attainment gap to portray diametric trajectories in the pursuit of equal attainment.