In this post, Dr Vassilis Galanos continues his exploration of metrics, arguing that the passive acceptance of a metrics-oriented culture is what feeds, establishes, and normalises hype and high adoption rates of Generative Artificial Intelligence (GenAI) machinery. This post is part 3 of 3, and belongs to the Hot Topic theme: Critical insights into contemporary issues in Higher Education.

In the previous two posts (Higher Education State Critical and Rigorously Established Fear), I argued that, in Marxian terms, the surplus value generated from intellectual labour in academia is enhancing the institution’s reputation and funding. This shift is slowly reversing the map (numerical indices) with the territory (learning and teaching experience), using the latter to navigate the former, instead of the opposite. In this post, I argue that the passive acceptance of this metrics-oriented culture is what feeds, establishes, and normalises hype and high adoption rates of Generative Artificial Intelligence (GenAI) machinery, such as OpenAI’s ChatGPT, Microsoft’s Copilot, or Anthropic’s Claude.

This is a fast-forward historical recap of what preceded the emergence of GenAI. Student grades, attendance, and staff’s citations fuel the academic-industrial complex by fostering connections between businesses ready to absorb the highly-graded students while partnering with high-ranking research initiatives. This relationship turns human intellect into marketable products, since the relationship is mutually parasitical: as much as the industry wishes to present an outwardly-facing, scientifically and moral high-ground with the approval of the academy,  while the latter wishes to benefit by collaborations with the industry that increase revenue, prestige, and can be presented as societal impact.

This process is reminiscent of the commodification trends in industrial capitalism, where labour was quantified and valued based on its contribution to profit. Social media metrics further entrench this commodification, transforming intellectual achievements into social capital. Shoshana Zuboff calls this “behavioural surplus” – a term that might as well fit within the academic landscape. Students and staff self-regulate, ever aware of the metrics that loom over them, dictating their academic behaviour. This is the darker side of the notion of the quantified self the vision in which people would continuously track in order to optimise their performance (and well-being) that fell into decay once the profit-driven motives of self-tracking industries were sufficiently experienced, from health apps to selfie share, in most cases training customised advertisement algorithms and facial recognition software, often used for military and policing purposes.

This self-tracking culture fits perfectly into academia’s metrics obsession. Students monitor their grade point average (GPA) like investors tracking stocks, and researchers obsess over their h-index scores like they’re anxiously awaiting show reviews. While waiting for these longer-term affirmations, theye both gain temporary satisfaction through social media interactions in secret hope one of their outputs (research, business, or otherwise) will become viral – indeed, the algorithm for virality in social media and academia might be very similar.

From what has already been mentioned, GenAI visions uplift this obsession to an extreme level. Initially, it presents itself as capable to produce the boring aspects of a text that everyone wishes to avoid, for example, the opening and concluding sentences, the formal proofreading and grammatical/syntactical corrections, the angle ideation, and short explanations for relatively common-sense knowledge. Supposedly, thus, they save time which, in theory, can be used for leisure (of course, in Academia, the concept of “leisure” is very controversial and means different things to different people – should we prohibit the consumption of an academic text while on holidays? For one, we do not prohibit the academic study of leisure, especially if it attracts big grants).

Upon closer inspection, time is not saved at all, especially for those in precarious temporary contracts, or with student loans, or who need a promotion, or on scholarship deadlines. What Generative AI’s time-efficient output may do, is increase the amount of produced content, but without an unchanged time-table (the contracted hours, or the time prior to entering the job market). Students and teachers become mere data nodes, constantly producing text to feed the technical and the social machinery. This aligns with the historical trajectory of technological advancements that have progressively extended the volume and precision of bureaucratic production and control while intensifying the intellectual labour within the same time interval.

And the final Marx quote for the day:

“The shortening of the hours of labour creates, to begin with, the subjective conditions for the condensation of labour, by enabling the workman [workperson] to exert more strength in a given time. So soon as that shortening becomes compulsory, machinery becomes in the hands of capital the objective means, systematically employed for squeezing out more labour in a given time” (Marx 2013: 285).

Endless self-tracking and performance optimisation, powered by GenAI and sustained by social media metrics culture, thus turns the academic journey into a frantic dash for better numbers. While Generative AI claims to offer personalised feedback and guidance, it amplifies the anxiety around self-improvement and harmonising output within “acceptable” frames. Students and staff focus more on meeting target numbers than engaging deeply with their work (or others’), mirroring the constant self-optimisation driven by social media feedback loops.

If, as academics, we also think of ourselves as activists, not merely observing but influencing the politics of what we study (for some, this is inevitable anyway for we cannot suppress our influence – the question is whether we admit it and what we do with it), we should consider how the infrastructures that oppress social groups we wish to defend are entrenched through the criteria we develop and use to measure success and failure.

Words like “success,” “failure,” “impact,” “assessment,” “measurement,” “mark,” “rank,” or “grade” carry legacies of phallogocentrism (the internet is still complete with videos of males measuring their manhood in toilets), imperialism and colonialism, military hierarchy and operationalism, and nonhuman and human enslavement (marked on the flesh by branding iron until today). In this metrics-driven landscape (where “data-driven” is but a euphemism), academia risks becoming a parody of itself. Here, surveillance, commodification, and self-quantification dominate, supported by a broader culture of social media views and reactions that enables as reductionist a thinking as the nine emotions featured in the recent film, Inside-Out 2.

Generative AI, the latest instalment in the history of automated education, intensifies these trends, aiming to squeeze more surplus profit out of education and research, which in turn exacerbates an aesthetic of the safe and acceptable writing we have already established in academic circles. This, in turn, normalises a degenerative culture of unimaginative repetition. Hence, I prefer to call it ‘Degenerative AI’.

My rant is over. I am leaving you with the following song about numbers from the 1969 season of Sesame Street, composed by Denny Zeitlin and featuring vocals by Grace Slick of Jefferson Airplane: https://www.youtube.com/watch?v=G5stWhPNyec

References for Part 1, 2 and 3

Andreski, S. (1973). Social sciences as sorcery. New York: St. Martin’s Press, May.

Archer, M. (2024). Unsustainable: Measurement, Reporting, and the Limits of Corporate Sustainability. NYU Press.

Cixous, H. (1974). Prénoms de personne. Paris: Seuil.

Cixous, H. (1994). The Hélène Cixous Reader. (Susan Sellers, Ed.). Routledge.

Derrida, J. (1979). Spurs: Nietzsche’s styles. University of Chicago Press.

Marx, K. (2013). Capital: A critical analysis of capitalist production (S. Moore, E. Aveling, & E. Untermann, Trans.). Wordsworth.

Zuboff, S. (2022). Surveillance capitalism or democracy? The death match of institutional orders and the politics of knowledge in our information civilization. Organization Theory, 3(3).

Vasileios Galanos

Dr Vassilis Galanos, SFHEA is a visitor at the Edinburgh College of Art and works as Lecturer in Digital Work at the University of Stirling. Vassilis investigates historico-sociological underpinnings of AI and internet technologies, and how expertise and expectations are negotiated in these domains. Recent collaborations involved the history of AI at Edinburgh, interrogations of generative AI in journalism (BRAID UK), artist-data scientist interactions (The New Real), and community-led regeneration interfacing with data-driven innovation (Data Civics). Vassilis has co-founded the AI Ethics & Society research group and the History and Philosophy of Computing’s (HaPoC) Working Group on Data Sharing, also acting as Associate Editor of Technology Analysis and Strategic Management.

In this post, Dr Vassilis Galanos continues his exploration of metrics, its place in Higher Education, and the impact of the Research Excellence Framework on our work practices. This post is part 2 of 3, and belongs to the Hot Topic theme: Critical insights into contemporary issues in Higher Education.

In a previous post with Teaching Matters, I have written about how academic excellence evaluations such as the UK’s Research Excellence Framework (REF), claiming to measure research quality with some kind of objective precision, can foreground the development of digital machinery (such as Generative AI) that is adjustable to the REF’s objective (or better: objectifying) metrics. In this post, continuing the thread from part 1, I will connect the REF to the context of broader student and faculty numerical rankings. REF, that for many academics also stands for “Rigorously Established Fear”, often ends up fostering a competitive environment where volume trumps substance and impact is staged in wording but often not grounded in practice. As an example of this, as part of the Edinburgh Futures Institute’s Data Civics Observatory, I encountered the frustration of local communities in Edinburgh who complained about researchers using their underdeveloped neighbourhoods to justify their grant allocation, but disappeared upon the project’s end.

Niche or curiosity-driven disciplinary-questioning endeavours get side-lined while churned-out, quota-meeting research takes centre stage, especially in the context of academic-industry collaboration. Such collaboration is initially phrased as an attempt to open-up the world of Academia into the real world, but, in practice, it transforms Academia itself into a peculiar type of industry. This mirrors the rise of performance indicators in corporate bureaucracies, which seek to optimise efficiency at the expense of innovation and creativity.

This obsession with optimisation and efficiency further increases the distance between metric-driven reporting as just a symbol and as practical social change (as Matthew Archer recently showed in his 2024 book ‘Unsustainable: Measurement, Reporting, and the Limits of Corporate Sustainability,’ or, as Stanislav Andreski beautifully put it in 1970, “evasion in the guise of objectivity”; “quantification as camouflage’; and ‘techno-totemism and creeping crypto-totalitarianism”).

As an individual progresses up the academic ladder from student to staff, the REF exercise takes the emotional place occupied by the marker’s assessment and staff mentor’s supervision as the higher and sufficiently invisible entity of surveillance. This mirrors Marx’s description of a factory, which, in our case, is the university (my additions in square brackets):

“The technical subordination of the workman [read: worker, but also student, lecturer, professor, etc] to the uniform motion of the instruments of labour [including marking schemes, impact assessments, article production, grant allocation mechanisms], and the peculiar composition of the body of workpeople, consisting as it does of individuals of both sexes and of all ages, give rise to a barrack discipline, which is elaborated into a complete system in the factory [and academia], and which fully develops the before mentioned labour of overlooking, thereby dividing the workpeople into operatives and overlookers, into private soldiers and sergeants of an industrial army. […] The place of the slave-driver’s lash is taken by the overlooker’s book of penalties [including late submission penalties, resits, redundancy of academics who did not produce REFable outcomes, and more]” (Marx 2013: 293).

In the next, and final, post of this three-part series, I will conclude this conversation by situating the emergence of Generative Artificial Intelligence (GenAI) within the afore-described process of metrics-oriented culture.

Vasileios Galanos

Dr Vassilis Galanos, SFHEA is a visitor at the Edinburgh College of Art and works as Lecturer in Digital Work at the University of Stirling. Vassilis investigates historico-sociological underpinnings of AI and internet technologies, and how expertise and expectations are negotiated in these domains. Recent collaborations involved the history of AI at Edinburgh, interrogations of generative AI in journalism (BRAID UK), artist-data scientist interactions (The New Real), and community-led regeneration interfacing with data-driven innovation (Data Civics). Vassilis has co-founded the AI Ethics & Society research group and the History and Philosophy of Computing’s (HaPoC) Working Group on Data Sharing, also acting as Associate Editor of Technology Analysis and Strategic Management.

In this post, Dr Vassilis Galanos dissects what metrics really mean for students, educators, and researchers in the wider academy. This post is part 1 of 3, and belongs to the Hot Topic theme: Critical insights into contemporary issues in Higher Education.

As the heading suggests, it’s not some Matrix-like virtual reality conspiracy controlling all things academic – it’s the metrics. For about 20 years now, from undergraduate student to Lecturer, I’ve experienced numbers like student grades, attendance monitoring points, seminar participation marks, journal rankings, research excellence frameworks (REF), and citation scores as structural elements we increasingly have to face, understand, and be assessed against. Yet, at the same time, we find ourselves being less outspoken about these metrics and what they mean for our daily lives.

Following a long legacy of bureaucratic solutionism, they’re supposed to streamline and improve academic management and recognition, but often end-up reducing the – supposedly – rich, varied experience of academia to a dry set of spreadsheets, impact factor badges, and transcript competitions.

As a person who studies the history of the internet in parallel with artificial intelligence (AI) (and an avid social media user myself, turning my life into an open experiment), I’ve seen the rise of social media metrics like ‘likes’, ‘follows’, and ‘faves’ being established as a “free-for-all” venue for numerical recognition. I have also seen how they further normalise our obsession with numbers, converging with the proliferation of AI and algorithmic technologies to amplify and entrench this metric-driven culture. When you add Generative AI into the mix, the metrics game shifts into hyper-drive with an efficiency that an Orwell-Huxley hybrid couldn’t have predicted.

For the past six months, I’ve spent time with Karl Marx’s The Capital, volume 1, so I decided to dissect what these metrics really mean, using insights from surveillance studies, Marxian economics, and the quantified self, with a nod to the history of numerical classifications from mathematics to economics. To complete the pun: from the Matrix, to metrics, to Marx.

Grades as assessment

Grades are the old standby for assessing students, neatly categorising their efforts and even identities into A, B, C, and “better luck next time.” Or, to use a term co-constructed by Hélène Cixous (1975, 1994: 29) and Jacques Derrida (1979: 97), they encapsulate the education of a phallogocentric system – one that is at the same time serving a masculine (phallocentric) ideal of military rankings and the dominion of rationality (reasoned logic as Logos, that is, logocentric). This creates a linear trajectory in which there is less space for winners and those in higher ranks.

Grading turns the wonderfully messy process of learning into bite-sized numbers, much like fast food turns diverse cuisines into generic meals – always with the opportunity to pay a bit more in order to have access to luxurious gastronomy. This simplification often strangles creativity and critical thinking. For the imaginative and divergent thinkers, it’s like being shoved into a production line where only uniformity gets rewarded.

The politics of such numerical simplification finds its roots back to the early applications of mathematics in standardising measurements for trade and commerce as well as military precision. Here’s Marx:

“The division of labour, as carried out in Manufacture, not only simplifies and multiplies the qualitatively different parts of the social collective labourer, but also creates a fixed mathematical relation or ratio which regulates the quantitative extent of those parts […]. It develops, along with the qualitative sub-division of the social labour-process, a quantitative rule and proportionality for that process” (Marx 2013: 241).

The presentation of presence

Attendance records act as the school’s hall monitor, ensuring students physically show up. Digital systems like biometric scans offer precise tracking but also inch dangerously close to a Big Brother type of oversight. This constant scrutiny is more than just checking who’s present – it’s a subtle method of enforcing compliance and cultivating a culture of stress and control. The evolution of such monitoring systems can be linked to the development of bureaucratic systems in the 19th century, which relied on statistical data to manage and control populations. Interestingly, this enforcement of being present in fear that attendance is being monitored, is transformed within social media environments into “fear of missing out” (FOMO).

The presentation of presence as something to compete for is an interesting parallel between (a) attendance monitoring as part of one’s entertainment/leisure lifestyle, and (b) the joy of education as an enforced evil that is effected only by attendance supervision. Marx again:

“An industrial army of workmen, under the command of a capitalist, requires, like a real army, officers (managers), and sergeants (foremen, overlookers), who, while the work is being done, command in the name of the capitalist. The work of supervision becomes their established and exclusive function” (Marx 2013: 230

(Keep in mind that the French word “surveillance” literally translates into “supervision” or “overseeing” – worth considering every time you have a “supervision meeting” with your dissertation supervisor or your line manager).

The power of citations

For faculty, journal rankings and citation metrics are the currency of the academic marketplace (as it is very precisely put in everyday vocabularies). Top-tier publications and a heap of citations bring career benefits like tenure and grants. But navigating this numbers game often means playing it safe, avoiding the unconventional or interdisciplinary work that might not score high on the metrics scale. This focus on numeric evaluation echoes the econometric models that gained prominence in the 20th century, emphasising quantifiable data over qualitative insights. As an extension of econometrics, the 20th century saw the evolution of bibliometrics, scientometrics, and infometrics, as a quantifiable measure of impact of research.

Compounding the issue, social media metrics like ‘likes’ and ‘followers’ further normalise academics’ predisposition towards popular, mainstream topics that satisfy the instantaneity of a present-oriented appreciation of science. This is often at the expense of deeper, more substantive inquiries, which extend into the past and future. Indeed, the academic culture behind creating ‘tweetable’ abstracts of abstracts (“threads”) after an attention-grabbing title that is meant to be retweeted indicates the time pressure under which scholarly content is produced, disseminated, and consumed – “content” in the recent social media flavour of the word.

In the next part of this Teaching Matters contribution, I will relate the question concerning metrics to the Research Excellence Framework (REF) exercise.

Vasileios Galanos

Dr Vassilis Galanos, SFHEA is a visitor at the Edinburgh College of Art and works as Lecturer in Digital Work at the University of Stirling. Vassilis investigates historico-sociological underpinnings of AI and internet technologies, and how expertise and expectations are negotiated in these domains. Recent collaborations involved the history of AI at Edinburgh, interrogations of generative AI in journalism (BRAID UK), artist-data scientist interactions (The New Real), and community-led regeneration interfacing with data-driven innovation (Data Civics). Vassilis has co-founded the AI Ethics & Society research group and the History and Philosophy of Computing’s (HaPoC) Working Group on Data Sharing, also acting as Associate Editor of Technology Analysis and Strategic Management.

In this post, Dr Charlotte Desvages and Dr Itamar Kastner reflect on the notion of academic standards and its relationship with assessment and feedback, drawing on the events from the 2023 Marking and Assessment Boycott. Charlotte is a teaching Lecturer in Mathematical Computing, and Itamar is a Senior Lecturer in Linguistics and English Language in the School of Philosophy, Psychology and Language Sciences. This post  belongs to the Hot Topic theme: Critical insights into contemporary issues in Higher Education.

On 20 April 2023, in the context of a years-long dispute on pensions, pay, and working conditions, academic staff across the UK started a Marking and Assessment Boycott (MAB) called by the University and College Union (UCU). The MAB lasted until September 2023, directly impacting assessment culminating in the May and August exam diets. As often is the way when systems ‘breakdown’ or suddenly require large-scale workarounds, a huge amount of learning and reflection emerges. In this post, we draw on our experience and reflections of the MAB as an entry point to unpick how regulatory ‘academic standards’ influence our assessment and feedback practices. We argue that a focus on pedagogically-informed course and programme design, where marks are dissociated from feedback, would be more beneficial to students’ learning than a focus on an aggregated numerical grade.

Background to the MAB and academic standards in relation to assessment and feedback

In our University, assessment is regulated by each School’s Boards of Examiners according to the Taught Assessment Regulations, which in turn are decided by the Senate’s Academic Policy and Regulations Committee. Edinburgh saw strong participation in the MAB, prompting Senate to change the regulations temporarily in order to “mitigate against the impact of significant disruption to students, without compromising academic standards” [1]. Boards of Examiners were asked to make an academic judgement on whether they had sufficient information on students’ performance to decide on progression or degree awards; to what extent was the Board confident that a student had achieved learning outcomes and was ready to progress or graduate?

Our proxy for this decision is usually an average of numerical marks. With many of these unavailable due to the MAB, many Boards of Examiners decided that they were not competent to make an informed decision, leading to a large number of students affected by delayed results despite the relaxed regulations – including 30% of students [2] graduating in July either without an award, or with an unclassified degree. Although the immediate impact on students was regrettable, this cautious approach likely contributed favourably to the very small number of misclassifications [3]. Even under a narrow understanding of “academic standards” defined by graduates obtaining the appropriate degree classification, it is difficult to see how Boards applying the full extent of available mitigations would have maintained academic standards.

Perspectives on academic standards

Often, and certainly in the context of the MAB, the institutional understanding of “academic standards” is about the regulatory framework of Quality Assurance, an interlocking system of regulations set by Scottish ministers (through the Quality Assurance Agency), the University’s regulations, and each School’s Board of Examiners. The “quality” in question relates to the quantitative outcomes produced by Boards of Examiners (a course mark, a progression decision, a degree classification). Quality Assurance is how institutions demonstrate robustness and reliability of those outcomes to external stakeholders. In the process, all the learning and growth a student has experienced are distilled into one final grade.

This is an administrative process, happening after all learning and assessment are complete; by necessity, it’s based on shortcuts. For example, the 40% grade boundary provides a standardised threshold over which a student is deemed to have passed a course. This saves Boards of Examiners from having to wrestle with its implications: are we passing students who have achieved 2 out of 5 learning outcomes? Are we contenting ourselves with “achievement” being defined as 40% of the best possible performance?

In contrast, from an educator’s perspective, that is perhaps more internal facing, “academic standards” is understood as starting with rigorous, pedagogically-informed course and assessment design, and includes everything that happens before the final grade. In this sense, academic standards are standards we set on students’ engagement, learning, and growth, and on ourselves as instructors. We design assessment to give students the opportunity to demonstrate their progress; to give us confidence that they are ready for the next stage.

Regulatory mitigations only introduced more possible shortcuts for Boards of Examiners to produce aggregated outcomes with limited information from courses. Clearly, this cannot fit with educators’ understanding of “maintaining academic standards” – by definition, we did not have sufficient information to assess whether they were maintained. These different understandings of “academic standards” explain how academic staff who raised concerns about mitigations were left unsatisfied by the University’s response, and particularly concerned about continuing students [4]. Indeed, the major concern to UCEA [5] was primarily the disruption to the administrative function of exam boards, rather than, say, the delay of feedback on coursework, or the impact on student learning [6].

Returning to the Feedback and Assessment Principles and Priorities

Perhaps we can better understand the relevant assumptions about marks and feedback by considering the University’s Feedback and Assessment Principles and Priorities. None of these refer to marks or grades, although an educator’s perspective might be that these all contribute to upholding robust academic standards:

In our experience, the dissociation of marks from feedback is not only supported by research, as demonstrated at recent university events (such as talks Ungrading: What it is and why should we do it? by Rashne Limki, and To Grade or to Ungrade, that is the question! by Dave Laurenson, James Hopgood and Itamar Kastner), its benefits are also clear to students. While they would be lost without feedback – in that learning, by definition, does not happen without feedback loops – most students clearly understood (and supported) the withholding of marks as a tool.

We argue that the MAB laid bare how we have administrative processes serving a distinct purpose from that of teaching/learning. Institutional requirements for an aggregated numerical grade can place an inherent barrier for educators seeking alternative feedback and assessment methods which could be more beneficial for learning, and which could in fact contribute to maintaining robust academic standards.

So, where do these reflections leave us? We would encourage individuals, Schools, and managers to consider learning and feedback as a priority in the first stages of designing curriculum and assessment. We should be ensuring that the pedagogy is in place, and only later thinking about satisfying the administrative requirements of numerical evaluation (if at all). We must continue to challenge the model that defines “academic standards” as both requiring, and being limited to, a quantitative interface with external stakeholders.

Here’s one example to end on: in the National Student Survey (NSS), “feedback” has consistently been a sore point for our University. One of management’s responses has been to implement stricter deadlines for returning marks and feedback to students. But how does this move engage with the pedagogical grounding of feedback? Where does the magic number in the “15 working days” turnaround come from? And how does any of this support educators to improve their practice?

[1] APRC 22/23 8 – Minutes of the 2/05/2024 meeting of APRC. Accessed 23/07/2024.

[2] SQAC 23/24 5B – Appendix C, Degrees Awarded Outcomes. Paper presented at the 16/05/2024 meeting of SQAC. Accessed 23/07/2024.

[3] See footnote 2, Appendix B. Accessed 23/07/2024.

[4] e-S 23/24 3F – Appendix 1 (Maintaining Academic Standards), Report of Motions and Items not included on Senate Billet from 2022 to April 2024. Paper presented at the April/May 2024 e-Senate. Accessed 23/07/2024.

[5] Universities and Colleges Employers Association; the body representing universities as employers in the dispute which led to the MAB.

[6] See e.g. UCEA news release, 28^th July 2023, describing the “sector-wide impact” of the MAB exclusively as the percentage of students who were (un)able to graduate in July.

Itamar Kastner

Itamar is a Senior Lecturer in Linguistics and English Language in the School of Philosophy, Psychology and Language Sciences. His research investigates the structure of words from theoretical, experimental and computational perspectives, alongside evidence-based approaches to pedagogy. He has been a member of the university Senate since August 2023.

Charlotte Desvages

Charlotte is a teaching Lecturer in Mathematical Computing in the School of Mathematics, teaching computing courses focused on Python programming skills and introductory numerical methods. Her interests include peer learning for programming (via pair programming, code review, live coding); the development of computational thinking linked to mathematical thinking; and accessible and inclusive teaching practices. She is also currently the director of Equality, Diversity, and Inclusion in the School, and has been a member of Senate since August 2022.

In this post, Dr Matjaz Vidmar offers Part 2 of his exploration about the future of the take-home essay as a form of assessment in the era of generative large-language models. Matjaz is Lecturer in Engineering Management and Deputy Director of Learning and Teaching overseeing the interdisciplinary courses at the School of Engineering. This post is Part 2 of 2, and belongs to the Hot Topic theme: Critical insights into contemporary issues in Higher Education.

As explained in Part 1 of this article, generative writing tools need not be feared as the end of take-home writing assignments, if they are grounded on students’ critical reflection in connecting theory and practice. On the contrary, with the increase of group-work and experiential learning models, the take-home assessment has become more common in fields beyond the social science and humanities domains, such as engineering. For example, in the final assessment set in a number of courses I designed and now deliver across management, systems engineering and futures design domains, 60-70% of the final mark is obtained from a final, take-home essay.

However, for this to work at a time when chat-bots can read and write much faster than humans, I have developed an explicit assessment brief to discuss practical experience with respect to the core course concepts and literature. This assessment structure pre-dates the advent of generative writing tools, and is based on the pedagogy of reflexive critical thinking as a major marker of experiential learning and knowledge making. Most importantly, the students are also required to offer some original insight that the practical experience inspired. This leads them beyond the well-rehearsed generic principles from course literature and requires them to examine unique points of view (a dimension where generative algorithms struggle).

In my experience, whilst it is possible for students to articulate a well-structured generative writing prompt that identifies all three key components of the assessment answer (underlying theory, practical experience, and new insight), in doing so, they have, by-and-large, already demonstrated the critical understanding and skills outcomes from the course. Thus, the writing of the text in itself is not as pedagogically relevant (especially in an era where such tools are widely used in any case). Furthermore, by keeping the assignment length reasonably short (<1500 words), the limited scope and required conciseness requires writing skills that ensure the clarity of message, again demonstrating critical thinking and academic practice.

These considerations and observations have also been demonstrated in practice. During marking take-home essay assignments in all of my courses with this mode of assessment, we could only detect a small number of submissions where more extensive use of generative writing tools was likely (<5%). Furthermore, these submissions did suffer from the tale-tail signs of algorithmic syntax, both in terms of the artificial linguistic forms and predicted poor integration of different sections, even where the content was edited to be more or less on point.

Overall, we can note that there was no detectable advantage in terms of quality of writing output. If comparing the mark distribution for the final take-home essay assignment in the course Technology and Innovation Management (5 / MSc), for which we have the required comparable longitudinal data[1], there is no discernible change in student relative performance between academic years 2021-22 (pre-ChatGPT; n=74/63) and 2023-24 (post Chat-GPT; n=77/65). If anything, within the normal boundaries of cohort differentiation, the results from the more recent academic year(s) are slightly worse, despite the teaching team’s clear policy that use of generative writing tools is allowed as means to improve grammar, syntax and text flow.

Overall, this model is demonstrating that, with more focus in course delivery on experiential learning and then framing the take-home essay as an examination of critical reflection, the current generation of generative writing tools do not pose any serious threat to the robustness and integrity of take-home essay as a form of assessment. In addition, if the assessment objectives target explicit linkage of theoretical concepts to a managed, in-class experience, then the intellectual work required to construct a writing prompt for a generative writing tool already meets the core learning outcomes examined by such an assessment.

Having said that, it is nonetheless important that we educate learners about ethical and epistemological issues surrounding large language models within the context of in-class exercises and take-home writing support. On the epistemic side, it is critical to stress that language models are based on statistical patterns of language use, and as such cannot serve as un-checked sources of knowledge.For many widely accepted theories, the two can be strongly aligned. But with newer or more peripheral bodies of literature, the margins of error in accurately representing scholarly insights increase significantly.

On the matter of ethics, two critical issues need to be communicated:

1.  The models have been constructed with inherently biased and often exploitative data practices.
2. Inputting own original ideas into publicly available tools can lead to them being used for future model development / training and thus lack of credit to the originators.

Colleagues have already set the scene for making the most of generative writing tools, proposing both more adaptive teaching practices as well as assessment innovation. Given that it is here to stay, we should focus on making the most of this new technology to enhance the experiential learning process, and reject the temptation to revert to outdated assessment practices, as that would also inevitably make our teaching less relevant to students.

[1] Apart from lowering the word-count (which was planned ahead of time and unrelated to arrival of generative writing tools) there was no change to the assessment brief. Three other courses which I organise, with comparable assessment models (Building Near Futures, Systems Engineering: Thinking and Practice and Social Dimensions of Astrobiology and Space Exploration) also show no discernible advantage to students, though they are new, so there is no pre-generative-writing-tools data. Colleagues teaching Technology Entrepreneurship 5 / MSc, which I set up in 2020-2022 academic year with similar final assessment also report there is no change in results and little use of generative writing tools.

Matjaz Vidmar

Dr Matjaz Vidmar is Lecturer in Engineering Management at the University of Edinburgh and Deputy Director of Learning and Teaching overseeing the interdisciplinary courses at the School of Engineering. He is researching the collaborations within Open Engineering by bridging technical and social dimensions of innovation processes and (eco)systems as well as futures, strategies and design. In particular, he co-leads The New Real programme, a collaboration between the Edinburgh Futures Institute and Alan Turing Institute, experimenting with new AI experiences, practices, infrastructures, business models and R&D methodologies, including the flagship Open Prototyping. He is also the Deputy Director of the Institute for the Study of Science, Technology and Innovation and is involved in many international initiatives to develop the future of these fields, including several start-up companies and an extensive public engagement programme on interplay of STEM, arts, and futures literacy. More at www.blogs.ed.ac.uk/vidmar.

In this post, Dr Matjaz Vidmar explores the future of the take-home essay as a form of assessment in the era of generative large-language models. Matjaz is Lecturer in Engineering Management and Deputy Director of Learning and Teaching overseeing the interdisciplinary courses at the School of Engineering. This post is Part 1 of 2, and belongs to the Hot Topic theme: Critical insights into contemporary issues in Higher Education.

Since the global launch of chat-based access to generative large-language models (most notably, ChatGPT), a sense of technological and moral panic set in across the education sector. In particular, it seems that despite the well-documented existence of “essay mills”, where assignments could be written by hired “professional writers”, we nonetheless relied extensively on take-home written work as form of formative and summative assessment. With the now ubiquitous and freely-available writing tools in the hands of students, a number of colleagues became worried that marks on their courses were unsafe and considered (re)introduction of in-person timed exams as a way to stop what was perceived a wide-spread possibility of “cheating”.

However, is it that perhaps the issue with take-home written assessment is not about a sudden crisis arising from new technological development, but rather with some of our teaching and assessment practices?

In many ways, the sector has been struggling for years with the parallel challenges of the need to grow student numbers to keep up with maintaining expensive real-estate and facilities, whilst at the same time questioning what is the role of (higher) education in the modern information-driven, digital society. As more up-to-date knowledge is increasingly available online, the traditional (formal) forms of higher education have been resorting to focusing on honing in graduate skills and attributes in critical thinking, effective professional practice, and cross-cultural citizenship. Thus, it has become less clear how to identify the most effective and appropriate teaching and learning models.

One promising direction emerged when grappling with more complex and advanced concepts: an emphasis on experiential learning and group / project work. This also follows the leading pedagogical frameworks about practice-based learning and greater integration of individual ways of knowing and learning. The emphasis here is placed on critical personal reflection by connecting practical experience of challenges, and the strategies adopted to address them, with the academic understanding of the issues at stake.

The schematic shown below brings together some of the critical contributions mentioned above into a more holistic approach to learning and teaching, framing experiential learning as the grounding of the development of students through experimentation and experience. However, crucial to the attainment and assessment of deep personal understanding of the learning process is a meaningful reflective observation, connecting the (individual) practice with (collective) sense / knowledge-making.

In this post, Indigo Williams explores some of the factors contributing to the student mental health crisis, the varied ways this is shaping their university experience, and how we can begin to tackle it. Indigo is the Vice President Welfare at Edinburgh University Students’ Association. This post belongs to the Hot Topic theme: Critical insights into contemporary issues in Higher Education.

The mental health crisis amongst University of Edinburgh students is an increasingly pressing issue. We’ve been talking about the student mental health crisis for years and it’s not getting better. So, we must get serious about responding to it and accepting that this is, sadly, the new reality for students. For our students, transitioning to this new environment, coping with increasing academic pressures, trying to maintain some semblance of social life, and the rising cost-of-living, can all contribute to the development of new mental health concerns, or exacerbate existing ones.

Our community, with its diverse student body, is not immune to these challenges. A 2022 report by the Mental Health Foundation found that nearly three quarters of students in Scotland reported low well-being, and 45% said they had suffered from a serious psychological issue that they felt required professional help, so this is not an isolated issue. Ongoing conversations about where the responsibility for students with complex support needs lies – whether with the University or the NHS – often result in students falling through the gaps and not receiving the support they need. This is particularly concerning as students are increasingly coming to university with, or developing, more complex, less well-known, and chronic conditions such as disordered eating, obsessive-compulsive disorder, and psychosis.

Pressures faced by students

Students face a variety of different pressures, which can contribute to mental health concerns:

• Academic pressure: Higher Education, particularly at prestigious institutions such as Edinburgh, fosters a demanding academic environment with high student expectations, and where academic pressure is frequently intertwined with career pressure. This is especially true for international students, whose families have often invested significantly in their education expecting them to provide financial support to their families after graduation. The competitive nature of higher education, particularly on programmes where performance is compared publicly, can contribute to feelings of imposter syndrome.
• Financial hardship: As my fellow Sabbatical Officers, Dora and Ruth, explored in their contribution to this series, the cost-of-living in Edinburgh is soaring with sky-high rents and the ever-increasing cost of bills and essentials. These, combined with the debt burden of tuition fees and student loans, create a constant stress on all our students, even those not experiencing direct financial hardship.
• Social adjustment: For many of our students, University is their first experience of living away from home, often in a new city or even new country. This can be overwhelming for some, which when combined with the loss of existing friend and family support networks can lead to loneliness or isolation.
• Cultural adjustment: For the thousands of international students who make up nearly half of our community, adjusting to a new culture, language, and educational system can be challenging and contribute to mental health issues. These students can also sometimes find it challenging to navigate support services due to language and cultural barriers.
• Marginalised Communities: Students from marginalised communities often face unique challenges in higher education. Black, Asian, Minority Ethnic (BAME) students may experience discrimination, LGBTQ+ students may face bullying, and students with disabilities may encounter accessibility barriers. These challenges, combined with socioeconomic disadvantages and cultural barriers, can create significant pressure for underrepresented groups, making it difficult to thrive in academic environments.

The impact of mental health struggles on students’ lives

When we are experiencing a period of poor mental health and well-being, it’s not just our personal lives or social relationships that are affected. Students facing a mental health crisis often see a knock-on impact on their academic work. Whether that’s because they are struggling to concentrate and remember information, a specific activity – such as a presentation or group work – is causing them anxiety, or because they cannot find the motivation to engage with their studies, students often see their academic performance suffer. These challenges can often have further cumulative effects, creating a vicious cycle where students struggle to find their footing. The pressure to catch up and recover from academic setbacks can lead to increased stress, reduced confidence, and even more difficulties. Resubmitting assignments or re-sitting exams can further exacerbate these issues, making it difficult for students to break out of this negative spiral.

The transition to university can also be a challenging time for many students, often marked by feelings of loneliness and social isolation. The loss of familiar support systems, combined with the pressure to succeed academically, can contribute to feelings of stress and anxiety. Students may also assume that their struggles are unique, leading them to isolate themselves and avoid seeking help. This can create another vicious cycle, as social isolation can further exacerbate mental health issues. Particularly for new students, who may not yet have developed strong friendships or support networks, it’s important to recognise the value of reaching out to others and seeking support.

Some students may turn to substances to self-medicate or escape. When struggling with mental health, substance abuse can be used as a coping mechanism for arising challenges that may lack necessary support approaches or availability.

Mental health struggles can significantly hinder a student’s ability to reach their full academic potential. Challenges like anxiety, depression, and other complex conditions can lead to difficulties with concentration, motivation, and engagement in academic activities, often resulting in declining performance and increased stress. For some students, the cumulative impact of these challenges may become overwhelming, contributing to higher dropout rates among those experiencing severe mental health crises. The inability to continue their studies not only disrupts their immediate academic goals but also affects their prospects and overall well-being.

Let’s address the problem

It is important to recognise that even students with complex, severe, and chronic mental health conditions can still thrive when provided with the right support. This raises a critical question: what is the solution?

Addressing the growing mental health crisis will require us to work together to improve the well-being of our students. Below are a few suggestions:

• Destigmatise and open up the conversation: Students can feel embarrassed about seeking out support, and we all have a part to play in tackling the stigma around mental health. We have excellent Professional Services staff who can give students the in-depth support they might need, but there is a lot that other members of our community can do. If we all were aware of the signs to look out for and knew about the resources available, more students might feel comfortable in seeking out the support. Staff that want to feel more comfortable engaging in conversations around mental health and signposting students appropriately are encouraged to take the University’s Mental Health Awareness Course.

Doing so creates an open and kind environment in classes, and we often see this approach reflected in submissions for our annual Teaching Awards, with students telling us how supportive our community can feel.

• Effective services and responses: Students will likely seek out support and specific services when they’re in crisis, so the design and development of this offer should be carried out through the lens of the service user. Websites and resources should be easy to navigate, quick to digest and understand, and putting the focus on the needs of service users, rather than internal structures.
• Preventative measures: One effective approach is encouraging staff to consider how their course design impacts well-being. Simple adjustments, such as spacing out assessment deadlines to avoid clustering or scheduling them directly after weekends, can significantly reduce stress. Presentations and public speaking can also be anxiety-inducing, so offering alternatives like smaller group settings or pre-recorded options can help ease this pressure. Moreover, when teaching sensitive or distressing topics, staff should provide content warnings and foster a supportive and inclusive environment where students can express concerns.
• Early intervention: Although a crucial part to supporting students, early intervention does not lie solely on individual staff. While they do play a role in identifying students who may be struggling, the broader, structural issues must also be addressed to create lasting impact. Universities need to invest in comprehensive mental health resources, including accessible counselling services, proactive outreach programmes, and clear pathways for students to seek help early on. Addressing structural issues like high academic pressure, financial stress, and accessibility barriers requires coordinated efforts at the institutional level.

To conclude

Tackling the student mental health crisis requires a collective effort from both staff and students. By taking on the responsibility of understanding and recognising when members of our community are struggling, and by implementing the steps outlined above, we can make a meaningful impact. This approach not only supports those who are facing mental health challenges but also helps foster a positive, welcoming, and sportive university environment for everyone. Together, we can create a community where all students and staff feel valued, supported, and empowered to reach their full potential.

Students and staff can learn more about well-being and mental health services and support available on the University’s Wellbeing Services and the EUSA Advice Place webpages.

Indigo Williams

Indigo is Vice President Welfare at Edinburgh University Students’ Association.

In this post, Dr Avita Rath explores the topic of emotional labour, and its impact for those working in Higher Education. Avita is a year 3 distance learning student (MSc Clinical Education) at Edinburgh Medical School. This post belongs to the Hot Topic theme: Critical insights into contemporary issues in Higher Education.

Beyond the “Service Smile”

Behind the carefully crafted smiles, the reassuring words, and the meticulously managed demeanour lies a silent toll, a heavy burden we carry. In academia, we’re often expected to suppress our true selves, conform to a narrow definition of “professionalism,” and mask the real emotions that shape our experiences. This unseen, rarely acknowledged labour is a storm brewing beneath the surface of our work, threatening to drown us in a sea of burnout and exhaustion.

As sociologist Arlie Russell Hochschild (1983) famously coined it, emotional labour is the invisible work of managing our emotions to meet the demands of our job. It’s about regulating our feelings and expressions, often at the cost of our well-being.

While emotional labour has been studied extensively in various fields, it’s often overlooked in higher education. Why? Because we tend to conflate it with professionalism, viewing it as a natural and expected part of the job rather than a form of labour that needs to be recognised and compensated. This is particularly true in the context of marketisation, which has transformed universities into “service institutions,” where academics are increasingly expected to cater to the needs of students and stakeholders.

This “professionalism,” however, can be a double-edged sword. It often involves suppressing genuine emotions and conforming to a set of unrealistic expectations. As Ogbonna and Harris (2004) noted, the “professional” persona academics are expected to project can create a “gap” between their true selves and their public performances.

A personal journey and a broader truth

As a neurodivergent woman in a predominantly Asian academic setting, I’ve experienced this gap firsthand. The cultural taboo surrounding emotional expression, particularly for women, combined with the pressure to conform to a narrow definition of “professionalism,” created a sense of alienation and isolation. I often felt like I was performing a role, hiding my true self behind a carefully constructed mask.

Imagine, for a moment, the demanding life of a dental academic like myself, or any academic for that matter: you’re expected to be a skilled clinician, a mentor, a teacher, a researcher, and a leader – all at once. This constant pressure to excel in multiple areas fuels the need for emotional management, often at a significant cost.

This emotional strain is not simply a personal experience. It’s a pervasive issue within academia. A recent Nature poll and HEPI policy papers (Forrester, 2023; Morrish, 2021) found that 67% of academics are burned out, with counselling and occupational referrals rising by more than 100% over the past two years. This suggests that emotional labour is not just a personal challenge but a systemic problem within academia that affects our wellbeing and our ability to thrive. Moreover, the rise of “quiet quitting” – where academics are disengaging from their work by reducing their output and limiting their involvement – is another alarming sign of the impact of emotional labour in academia.

As Shuler (2007) aptly points out, “[as] scholars and practitioners… we often write as if WE are not also engaging in emotional labor” (p. 255). This is the core of the issue. Emotional labour is often seen as an intrinsic part of “caring” professions (Grandey et. al., 2013), yet it’s rarely acknowledged or valued. It is treated as an expected part of the job rather than a form of labour that needs to be recognised and compensated.

The consequences of ignoring this ‘work’ in academia appear to be significant. It can lead to burnout, decreased job satisfaction, and even mental and physical health problems that eventually affect the quality of teaching and student well-being (Berry & Cassidy, 2013; Abery & Gunson 2016). We need to change how we think about emotional labour, recognise its impact, and stop this cascade of worrying reactions.

Moreover, as Bellas and Krupnick (2007) found, this burden is disproportionately weighted on women. Women are often socialised to be more emotionally expressive and nurturing. These societal expectations can lead to a “double bind” for women in academia, who are expected to be both caring and competent but are often penalised for displaying their genuine emotions. This double bind is further intensified for neurodivergent women in academia, who may face additional pressures and stigmas due to the often pervasive cultural taboos against neurodiversity.

A call to action: Valuing emotional well-being in academia

To create a more sustainable and equitable academic environment, we need to:

• Acknowledge emotional labour: Universities need to openly acknowledge the emotional labour that academics undertake and recognise its importance in the performance of educators.
• Promote well-being: Universities should offer programs and workshops focusing on emotional intelligence, self-care, and academic stress management.
• Foster open dialogue: Encouraging open communication and fostering a culture of mutual respect and understanding among faculty members can create a more supportive environment that helps to alleviate emotional distress.
• Reduce administrative burdens: Universities should strive to reduce the administrative burden on academics, allowing them to focus more on teaching and research.
• Embrace neurodiversity: Universities should actively promote neurodiversity and create a more inclusive and supportive environment for neurodivergent academics.

Embracing change for a “hopeful” future

We can move towards a future where academic institutions recognise the human cost of emotional labour. A future where universities prioritise the emotional well-being of their faculty, creating a more supportive and inclusive environment for all. A future where we can move beyond the “service smile” and embrace the full range of our emotions, bringing our authentic selves to our work.

This future is within reach. By demanding change, fostering a more empathetic and compassionate approach to academia, and advocating for a world where emotional labour is recognised, valued, and addressed, we can begin to create a more just and equitable academic environment.

References

Abery, B., & Gunson, C. (2016). This paper applies Berry and Cassidy’s Higher Education Emotional Labour model to the management of extension requests in a short space of time in a large, first year Health Sciences topic. International Journal of Allied Health Sciences Education, 6(1), 22–26.

Bellas, M. L., & Krupnick, C. G. (2007). The Costs of Caring: Examining the Relationship Between Gender, Emotional Labor, and Burnout. Journal of Women and Social Work, 22(4), 381-395.

Berry, K., & Cassidy, S. (2013). Emotional Labour in University Lecturers: Considerations for Higher Education Institutions. International Journal of Curriculum and Teaching, 2, 1-21.

Forrester, V. (2023). Fed up and burnt out: ‘quiet quitting’ hits academia. Nature, 615, 751-753.

Grandey, A., Rupp, D. E. & Diefendorff, J. 2013. Emotional labor in the 21st century: diverse perspectives on the psychology of emotion regulation at work, Routledge.

Morrish, L. 2021. Emotional Labour in the Post-Pandemic Academy. Available from: https://postpandemicuniversity.net/2021/10/31/emotional-labour-in-the-post-pandemic-academy/.

Ogbonna, E., & Harris, L. C. (2004). Work Intensification and Emotional Labour among UK University Lecturers: An Exploratory Study. Organization Studies, 25, 1185-1203.

Shuler, S. (2007). Autoethnographic Emotion: Studying and Living Emotional Labor in the Scholarly Life. Women’s Studies in Communication, 30, 255-283.

Avita Rath

Dr Avita Rath is a year 3 distance learning student (MSc Clinical Education) at Edinburgh Medical School, The University of Edinburgh. She is also a senior lecturer, academic coordinator and periodontist at the Faculty of Dentistry, SEGi University, Malaysia. She is a Common Wealth scholar, a Fellow in Advance Higher Education, UK (FHEA), and an Association of Medication Education in Europe (AMEE) member. Some of her research interests include equity, diversity and inclusivity issues in health professional education, mindfulness in dental education, and student engagement concepts. . She would like to thank Professor Gill Aitken, her Master’s supervisor, without who she would never been acquainted of this ‘invisible’ work that led to this blog post.