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.
What skills do social scientists need to adapt to generative AI, and how should educators approach teaching them? A narrow focus on training everyone in technical skills is misguided – what’s needed is authorial voice, leadership and management skills, and the critical force of the social sciences.
Does the ranking of a UK university in league tables affect its reputation and name recognition? Using a novel data source from an online quiz, this research explores the complex relationship between league table rank and identifiability of universities.
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.
