Prof. Emily Nordmann

@emilynordmann.bsky.social

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Associate Dean of Learning and Teaching @uofglasgow.bsky.social, PFHEA, psychologist, #rstats, learning & teaching in higher education, scholarship focused, in particular student and staff approaches to lecture recordings. 🏳️‍🌈

Red sword by Bora Chung front cover
Private Rites by Julia Armfield front cover
Front cover of Surface Detail by Iain Banks
Front cover of Frontier by Grace Curtis
Spark by Naoki Matayoshi front cover
A cat on a blanket next to an Iain Banks book and a rainbow cushion.
Before introducing existing assessment frameworks, it is important to clarify our scope. As weknow, assessment serves multiple purposes (Boud 2000). Assessment for learning is important(Carless 2017). However, in this paper we are concerned with moments of assessment of learningwhere there is a desire to in some way control student use of GenAI to enable judgements tobe made about student capability. These moments are essential for assessment in higher educa-tion to deliver on its promise of ensuring that graduates can do what their university says theycan do. With this focus established, we now turn to examine three of the most dominant kindsof AI focused assessment frameworks.
Abstract Generative AI (GenAI) challenges assessment validity by enabling students to complete tasks without demonstrating genuine capability. In response to this challenge, institutions have developed and implemented various approaches that aim to communicate permissible AI use to students. Familiar examples include the ‘traffic light’ approach now commonly found within institutional policy. While well-intentioned, these approaches share a common limitation: They focus primarily on communicating rules rather than redesigning assessment mechanics. To clarify why such approaches fail, and to guide more effective responses, this paper introduces a novel conceptual distinction between discursive changes to assessment (modifications relying solely on instructions students remain free to ignore) and structural changes (modifications that reshape the underlying mechanics of assessment tasks themselves). Through a critical analysis of prominent frameworks, we demonstrate that current approaches predominantly rely on discursive changes that create what we term an ‘enforcement illusion’. We find that educational frameworks frequently borrow the language of socially familiar structural systems (like vehicular traffic lights) while lacking their actual enforcement capabilities, creating an illusion of assessment security. In place of this, we argue for a shift towards structural assessment redesign that builds validity into assessment architecture rather than attempting to impose it through unenforceable rules.
Front cover of Animal Farm by George Orwell
Brian is a seismologist at MIT Oh well that's perfect. Brian being a seismologist and you having so many faults.
From cover of the change by Kirsten Miller
How do higher education staff understand the terms hybrid, hyflex and blended learning? Choice, modality and uncertainty Emily Nordmanna*, Barbora Hronskaa and Jill R.D. MacKayb aSchool of Psychology and Neuroscience, University of Glasgow, Glasgow, Scotland; bRoyal (Dick) Veterinary School, University of Edinburgh, Edinburgh, Scotland Received: 19 September 2024; Revised: 13 February 2025; Accepted: 31 March 2025; Published: 15 May 2025 Many universities implemented blended and hybrid delivery for the first time during the COVID-19 pandemic, and as such, the use of terms that relate to various manifestations and implementations of blended learning has increased significantly by all higher education stakeholders. However, the meaning ascribed to these terms is often inconsistent and can lead to confusion, making it difficult to set expectations clearly for both staff and students. This study aimed to investigate how higher education staff understand and use these terms and to identify sources of confusion and barriers to adopting standardised definitions. We surveyed 152 higher education staff and asked them to provide definitions of each term as well as completing a categorisation task. An applied thematic analysis identified two factors that were present across definitions: choice (no choice, student choice and choice not specified) and modality (mixed but separate, dual delivery and mixed not otherwise specified). Our findings reveal significant discrepancies in understanding, particularly regarding hybrid learning, which was often conflated with other modalities and involved definitions where neither choice nor modality was clearly specified. Blended learning was most consistently defined and identified as involving separate online and in-person components with no student choice as to the modality in which they could engage with each component. Hyflex learning, despite being less familiar to many participants, was accurately associated with dual delivery and …
ABSTRACT Generative AI (GenAI) summarisers, tools that provide concise summaries of digital texts, are increasingly marketed to higher education students as time-saving aids for engaging with complex academic materials. As their prevalence grows, so does their potential impact on students’ reading practices and learning outcomes. Through an exploratory literature review, we demonstrate a significant gap in scholarly discussion and empirical research examining the extent and effects of AI summariser use among students. We explore the marketed promises of AI summarisers, hypothesise about their likely use cases, and consider concerns surrounding their impact on students’ engagement with academic texts. Our analysis is grounded in contemporary higher education literature on digital reading practices, scaffolded learning, and the development of critical thinking skills. This paper informs policy and practice by proposing a critical research agenda that addresses key questions about the prevalence, impact, and implications of GenAI summarisers in higher education. By stimulating scholarly discussion on this emerging technology, we aim to provide a foundation for evidence-based recommendations on the responsible integration of AI summarisers in academic settings, relevant to an international audience of educators, researchers, and policymakers in higher education.