SRHE Blog

The Society for Research into Higher Education

Tag Archives: emotional labour


by Leave a comment

Judgement under pressure: generative AI and the emotional labour of learning

by Joanne Irving-Walton

What AI absorbs and why that matters

Most debates about generative AI in higher education fixate on what it produces: essays, summaries, answers, paraphrases. I find myself increasingly interested in something else – what it absorbs. Over the past year, as conversations about AI have threaded through seminars and tutorials, a pattern has gradually become visible. In those discussions, students rarely begin with content production; instead, they talk about how it helps them get started and steadies them enough to keep going. They use it when the blank page paralyses, when feedback stings and when uncertainty feels exposing. One student described asking AI to “make it feel possible”. Another spoke of feeding tutor comments into the system so they could be “explained more kindly”. A third reflected, almost apologetically, “I don’t want it to do my work… I just need something to push against before I say it out loud and risk looking stupid”.

In each case, AI is not replacing thinking. It is absorbing part of the emotional labour involved in it, and as that labour is redistributed, the texture of judgement shifts. Academic judgement does not tend to emerge from comfort. It develops in the stretch between knowing and not knowing, when confidence dips, stakes feel heightened, and your sense of competence is quietly tested (Barnett, 2007). Staying in that stretch long enough for thinking to clarify demands more than intellectual effort; it requires emotional steadiness, time, space and the capacity to tolerate uncertainty without rushing to resolution (Biesta, 2013). Traditionally, that steadying work has been shared across learning relationships: tutors reframing feedback, peers normalising confusion, supervisors encouraging persistence through doubt. Generative AI now occupies part of that terrain.

I do not think this is inherently a problem. For some students, it is transformative. It marks a shift in where the labour of learning takes place and that change deserves examination rather than alarm.

Four modes of engagement and emotional labour

When students talk about how they use AI, their practices tend to cluster into four overlapping orientations. These are not moral categories so much as shifts in where emotional and cognitive labour is undertaken.

Instrumental engagement appears when students use AI to summarise readings, refine phrasing or impose structure. Here the friction lies in form-making and shaping thought into something communicable. The judgement at stake is procedural: what is proportionate or efficient in this context?

Dialogic engagement emerges when students test interpretations or rehearse arguments. AI becomes a low-stakes sounding board, absorbing some of the vulnerability of articulating something half-formed. The question beneath it is interpretive: what does this mean, and how far do I trust my reading and myself?

Metacognitive engagement is evident when students ask AI to critique their reasoning or compare approaches. What is absorbed here is evaluative tension and the discomfort of examining one’s own argument. The judgement in play is comparative and strategic: which option is stronger, and why? And then there is affective-regulatory engagement. Here, AI absorbs the anxiety that precedes judgement itself. It breaks tasks into steps, softens feedback, lowers the threshold for beginning, offers reassurance before submission and quietens the internal ruminations and rehearsals of everything that might go wrong. This is not peripheral to learning. It is increasingly central.

Figure: Where the labour of learning now lives

Accessibility, safety and the risk of smoothing too much

For many students, particularly those navigating anxiety, executive dysfunction, neurodivergence or heavy external commitments, this emotional buffering is not indulgence but access (Rose & Meyer, 2002). Breaking tasks into steps or privately rehearsing ideas before speaking can widen participation rather than diminish it.

We should not romanticise struggle. Nor should we imagine that institutional structures have ever been able to hold every student perfectly. For some learners, AI offers another place to rehearse thinking, one that sits alongside, rather than replaces, human dialogue.

But there is a tension here. If AI consistently absorbs the strain of uncertainty before ideas encounter resistance, if feedback is softened before it unsettles, if structure replaces the slow work of wrestling thought into form, then something quieter begins to shift. Much of this work happens privately, in browser tabs and late-night prompts, in spaces students do not always feel comfortable admitting to. That makes it harder for us to see what is being strengthened and what may be thinning. The danger is not comfort, but the quiet disappearance of formative strain.

By formative strain, I do not mean suffering for its own sake, nor simply the “desirable difficulties” described in cognitive load theory (Bjork & Bjork, 2011) or the stretching associated with a Vygotskian zone of proximal development (Vygotsky, 1978). I am referring to the lived experience of remaining with ambiguity, critique and partial understanding long enough for judgement to consolidate; the emotional as well as cognitive work of staying with a problem. If that work is always pre-processed, it may narrow the rehearsal space where judgement forms.

Scaffold or substitute

Much depends on whether AI remains a scaffold or begins to function as a substitute. Used as scaffold, it lowers the emotional threshold just enough for deeper engagement, absorbing anxiety without displacing judgement. Used as substitute, it reduces not only strain but evaluation itself; the work of deciding and committing shifts elsewhere. The distinction lies less in the tool than in how it is woven into the learning environment.

Individual awareness and institutional responsibility

It would be easy, and unfair, to frame this as a matter of individual discernment. Students already carry a great deal. But nor is this simply a matter of institutional correction. We are all navigating new terrain in real time, without a settled script.

If we are serious about judgement formation, then responsibility is shared — and it is evolving. This is less about detection or prohibition than about openness. AI engagement is happening whether we discuss it or not. The question is whether we bring it into the light. That might mean inviting students to reflect on how they used AI in a task, not as confession, but as analysis. It might mean modelling, in our own teaching, what it looks like to question or refine an AI response rather than accept it wholesale. It certainly means acknowledging the emotional labour of learning openly (Newton, 2014), recognising that starting can be harder than finishing and that this, too, is part of learning.

At a structural level, we also need some candour. Systems built on speed, metrics and visible output inevitably amplify the appeal of friction-reducing tools. If polish is rewarded more consistently than process, we should not be surprised when students bypass the stretch between uncertainty and articulation. Cultivating discernment, then, is not a matter of allocating blame. It is a collective project of making the shifting terrain of AI use visible, discussable and educative.

Where the emotional work now lives

Generative AI has not diminished the importance of human judgement. If anything, it has made visible how emotionally mediated that judgement has always been (Immordino-Yang & Damasio, 2007). The interior work of learning – the hesitation, the rehearsal, the private negotiation of uncertainty – has never been fully observable. It has always unfolded, at least in part, elsewhere.

What AI changes is not the existence of that interior space, but its texture. Some of that labour now takes place in dialogue with a system that can stabilise, extend or subtly redirect thinking. That creates an opportunity: we are at a juncture where the emotional dimensions of learning can be surfaced and examined more deliberately than before.

It also carries risk. Students can disappear down an AI rabbit hole just as easily as they once disappeared into rumination. The question is not whether the interior work exists, but how it is shaped and whether it ultimately strengthens judgement or thins it.

References

Barnett, R (2007) A will to learn: Being a student in an age of uncertainty Open University Press

Biesta, GJJ (2013) The beautiful risk of education Paradigm Publishers

Bjork, EL & Bjork, RA (2011) ‘Making things hard on yourself, but in a good way: Creating desirable difficulties to enhance learning’ in MA Gernsbacher, RW Pew, LM Hough & JR Pomerantz (eds), Psychology and the real world: Essays illustrating fundamental contributions to society (pp. 56–64) Worth Publishers

Newton, DP (2014) Thinking with feeling: Fostering productive thought in the classroom Routledge

Vygotsky, LS (1978) Mind in society: the development of higher psychological processes Harvard University Press Rose, DH & Meyer, A (2002) Teaching every student in the digital age: universal design for learning ASCD

Joanne Irving-Walton is a Principal Lecturer at Teesside University, working across learning and teaching and international partnerships. She is particularly interested in how academic judgement and professional identity develop through the emotional realities of higher education.


by Leave a comment

My Marking Life: The Role of Emotional Labour in delivering Audio Feedback to HE Students

by Samantha Wilkinson

Feedback has been heralded the most significant single influence on student learning and achievement (Gibbs and Simpson, 2004). Despite this, students critique feedback for being unfit for purpose, considering that it does not help them clarify things they do not understand (Voelkel and Mello, 2014).

Despite written feedback being the norm in Higher Education, the literature highlights the benefit of audio feedback. King et al (2008) contend that audio feedback is often evaluated by students as being ‘richer’ than other forms of feedback.

Whilst there is a growing body of literature evaluating audio feedback from the perspective of students, the experiences of academics providing audio feedback have been explored less (Ekinsmyth, 2010). Sarcona et al (2020) is a notable exception, exploring the instructor perspective, albeit briefly. The authors share how some lecturers in their study found it quick and easy to provide audio feedback, and that they valued the ability to indicate the tone of their feedback. Other lecturers, however, stated how they had to type the notes first to remember what they wanted to say, and then record these for the audio feedback, and thus were doing twice as much work.

Whilst the affectual impact of feedback on students has been well documented in the literature (eg McFarlane and Wakeman, 2011), there is little in the academic literature on the affectual impact of the feedback process on markers (Henderson-Brooks, 2021). Whilst not specifically related to audio feedback, Spaeth (2018) is an exception, articulating that emotional labour is a performance when educators seek to balance the promotion of student learning (care) with the pressures for efficiency and quality control (time). Spaeth (2018) argues that there is a lack of attention directed towards the emotional investment on the part of colleagues when providing feedback.

Here, I bring my voice to this less explored side by exploring audio feedback as a performance of emotional labour, based on my experience of trialling of audio feedback as a means of providing feedback to university students through Turnitin on the Virtual Learning Environment. This trial was initiated by colleagues at a departmental level as a possible means of addressing the National Student Survey category of ‘perception of fairness’ in relation to feedback. I decided to reflect on my experience of providing audio feedback as part of a reflective practice module ‘FLEX’ that I was undertaking at the time whilst working towards my Masters in Higher Education.

When providing audio feedback, I felt more confident in the mark and feedback I awarded students, when compared to written feedback. I felt my feedback was less likely to be misinterpreted. This is because, when providing audio feedback, I simultaneously scrolled down the script, using it as an oral catalyst. I considered my audio feedback included more examples than conventional written feedback to illustrate points I made. This overcomes some perceived weaknesses of written feedback: that it is detached from the students’ work (McFarlane and Wakeman, 2011).

In terms of my perceived drawbacks of audio feedback, whilst some academics have found audio feedback to be quicker to produce than written feedback, I found audio feedback was more time-consuming than traditional means; a mistake in the middle of a recording meant the whole recording had to be redone. I toyed with the idea of keeping mistakes in, thinking they would make me appear more human. However, I decided to restart the recording to appear professional. This desire to craft a performance of professionalism may be related to my positionality as a fairly young, female, academic with feelings of imposter syndrome.

I work on compressed hours, working longer hours Monday-Thursday. Working in this way, I have always undertaken feedback outside of core hours, in the evening, due to the relative flexibility of providing feedback (in comparison to needing to be in person at specific times for teaching). I typically have no issue with this. However, providing audio feedback requires a different environment in comparison to providing written feedback:

Providing audio feedback in the evenings when my husband is trying to get our two children to sleep, and with two dogs excitedly scampering around is stressful. I take myself off to the bedroom and sit in bed with my dressing gown on, for comfort. Then I suddenly think how horrified students may be if they knew this was the reality of providing audio feedback. I feel like I should be sitting at my desk in a suit! I know they can’t see me when providing audio feedback, but I feel how I dress may be perceived to reflect how seriously I am taking it. (Reflective diary)                     

I work in an open plan office, with only a few private and non-soundproof pods, so providing audio feedback in the workspace is not easy. Discussing her ‘marking life’, Henderson-Brooks (2021:113) notes the need to get the perfect environment to mark in: “so, I get the chocolates (carrots nowadays), sharpen the pens (warm the screen nowadays), and warn my friends and relatives (no change nowadays) – it is marking time”. Related to this, I would always have a cup of tea (and Diet Coke) to hand, along with chocolate and crisps, to ‘treat’ myself, and make the experience more enjoyable.

When providing feedback, I felt pressure not only to make the right kind of comments, but also in the ‘correct’ tone, as I reflect below:

I feel a need to be constantly 100% enthusiastic. I am worried if I sound tired students may think I was not concentrating enough marking their assessment; if I sound low mood that I am disappointed with them; or sounding too positive that it does not match their mark. (Reflective diary)

I found it emotionally exhausting having to perform the perfect degree of enthusiasm, which I individually tailored to each student and their mark. This is confounded by the fact that I have an autoimmune disease and associated chronic fatigue which means I get very tired and have little energy. Consequently, performing my words / voice / tone is particularly onerous, as is sitting for long periods of time when providing feedback. Similarly, Ekinsmyth (2010) says that colleagues in her study felt a need to be careful about the words used in, and the tone of, audio feedback. This was exemplified when a student had done particularly well, or had not passed the assignment.

Emotions are key to the often considered mundane task of providing assignment feedback to students (Henderson-Brooks, 2021).  I have highlighted worries and anxieties when providing audio feedback, related to the emotional labour required in performing the ‘correct’ tone; saying appropriate words; and creating an appropriate environment and atmosphere for delivering audio feedback. I recommend that university colleagues wishing to provide audio feedback to students should:

  1. Publicise to students the purpose of audio feedback so they are more familiar with what to expect and how to get the most out of this mode of feedback. This may alleviate some of the worries of colleagues regarding how to perform for students when providing audio feedback.
  2. Deliver a presentation to colleagues with tips on how to successfully provide audio feedback. This may reduce the worries of colleagues who are unfamiliar with this mode of feedback.
  3. Undertake further research on the embodied, emotional and affective experiences of academics providing audio feedback, to bring to the fore the underexplored voices of assessors, and assist in elevating the status of audio feedback beyond being considered a mere administrative task.

Samantha Wilkinson is a Senior Lecturer in Childhood and Youth Studies at Manchester Metropolitan University. She is a Doctoral College Departmental Lead for PhDs in Education. Prior to this, she was a Lecturer in Human Geography at the same institution. Her research has made contributions regarding the centrality of care, friendship, intra and inter-generational relationships to young people’s lives. She is also passionate about using autoethnography to bring to the fore her experiences in academia, which others may be able to relate to. Twitter handle:@samanthawilko


by Leave a comment

Staff experiences of the rapid move online: challenges and opportunities

by Eileen Kennedy and Allison Littlejohn

This is one of a series of position statements developed following a conference on ‘Building the Post-Pandemic University’, organised on 15 September 2020 by SRHE member Mark Carrigan (Cambridge) and colleagues. The position statements are being posted as blogs by SRHE but can also be found on The Post-Pandemic University’s excellent and ever-expanding website. The authors’ statement can be found here.

The COVID-19 pandemic has necessitated major changes to research, teaching and professional working in Universities. In the UK more generally, there was surprise at the degree of public support for lockdown, and the Prime Minister’s directive to “join together” to beat the virus. Commentators have observed, however, that despite the constant suggestions that we are all in this together, the pandemic has affected people very differently. For example, the UN has observed that while the virus may not discriminate, but the impacts on individuals certainly do. 

We wanted to investigate how far this picture of uneven impacts may be affecting university staff. So, to capture staff experiences as they changed how they worked during this period, we launched a programme of research at UCL which involved a series of staff surveys and follow up interviews, Moving to Online Teaching and Homeworking (MOTH). This research was led by Allison Littlejohn with Martin Oliver, Lesley Gourlay, Eileen Kennedy, Tim Neumann, Kit Logan, John Potter and Jennifer Rode. Our aim was to understand how the crisis might be exacerbating pre-existing structural inequalities that impact adversely on staff, as well as posing challenges and opportunities for teaching and research.

We had 421 responses to our initial survey, which included asking about the challenges and opportunities of the shift to online teaching and researching at home. We also asked participants to share images that they considered communicated their experience. We sent out follow up surveys throughout the summer, and invited 32 participants to take part in in-depth interviews to explain their responses in more depth. 

What the findings showed us quite clearly was that the impact on staff was not the same for everybody.  Although respondents who identified as men and women reported caring responsibilities, caring appeared to manifest differently. Women found it more difficult to focus on research because of the caring labour they were doing – caring for children and adult dependents, for students, for colleagues. These activities lead to reduced time for research and to publish research papers with potential consequences for long-term career progression. 

Staff with physical and/or mental health challenges – either pre-existing or as a result of the pandemic – also experienced the lockdown differently, with reports of headaches, eyestrain, aching back, shoulders or wrists health impacts from 7.7% of survey respondents. Those with more space were more positive about the move than those with fewer rooms to work in, and staff on fixed term contracts experienced anxiety about the impact of the pandemic on their careers. 

The research we conducted showed very clearly that University staff experience is not uniform. This is an important message to those making decisions about how to support staff at universities during this crisis and beyond.

As for the move to teaching online, once again, experiences greatly differed. A slightly higher number rated their feelings as positive or very positive than those who rated their feelings as negative or very negative. Most people, nearly 40%, were undecided. A number of themes emerged, however. In terms of the challenges they described, the biggest issue were the lack of interaction and engagement with students online, technology problems, time and resource demands and the need for professional development. 

It became apparent from the survey that the most reported challenges that concerned teaching involved using live video systems like Teams or Blackboard Collaborate, and the key problem was the lack of visual cues, impacting interaction and engagement from students. This made online teaching more difficult and stressful for staff. This was borne out in the interviews. Staff said that students would not switch their cameras on,  “so, I was talking to a picture of myself on the screen” (interview participant). Staff really missed the energy from students they were used to in traditional teaching, and without this teaching online was stressful and exhausting. What made the difference for those staff who were more positive about online was prior experience. Those participants were also much happier with student engagement, and saw the move online as an opportunity presented by the pandemic.

“I feel positive about the probability that one of the outcomes of the COVID crisis will be more widespread general understanding about productive ways to use technology to support learning, not least among academics, but also, I hope, among education administrators and managers” (survey participant).

This research has given us insights into how university staff have experienced the pandemic. But it has told us more than that. A defining feature of the data was the central role that emotions played in every aspect of the move to online teaching and homeworking. Participants regularly described their anxieties about colleagues and students, the extra time they were putting into tutorials,  pastoral  care for students who experienced extra difficulties during the crisis and the impact this was having on themselves. This has led us to revisit the theme of emotional labour in teaching, and how we can make sense of the care that participants show in digital education. This is an aspect of online teaching that is seldom discussed. Our question, therefore, is what is the role of technology and the move to online and home working in supporting the caring labour of University staff?

Dr Eileen Kennedy is a Senior Research Associate based at UCL Knowledge Lab, UCL Institute of Education. Her research focuses on ways of enhancing and sharing practice in online and blended learning. Eileen works with two ESRC-funded research centres: the Centre for Global Higher Education (CGHE) and the RELIEF Centre. With CGHE, Eileen is exploring the transformative potential of digital technologies for higher education. With RELIEF, Eileen is researching ways of using digital education (particularly in the form of MOOCs) to build inclusive prosperity in the contexts of mass displacement.

Professor Allison Littlejohn is Director of UCL Knowledge Lab, UCL Institute of Education. Allison works in the field in Education, specialising in Digital Higher Education. Her expertise is in applying learning theories to complex interventions for professional learning that capitalise on digital technologies. The context of her research spans the energy, health, finance, education, and international development sectors across various countries.