SRHE Blog

The Society for Research into Higher Education

Tag Archives: AI


by Leave a comment

Unmasking the complexities of academic work

by Inger Mewburn

Hang out in any tearoom and you will hear complaints about work – that’s if there even is a tea room at the end of your open plan cubicle farm. Yet surprisingly little is known about the mundane, daily realities of academic work itself – despite the best efforts of many SRHE members.

Understanding the source of academic work unhappiness is important: unhappy academics lead to unhappy students and stressed-out administrators. If we know more about academics’ working lives, we are better placed to care for our colleagues and produce the kind of research and teaching our broader communities expect of us.

To understand more about academics’ working lives, we are embarking on an ambitious research project to survey 5000 working academics and would love you to take part.

Who is doing the ‘academic housework’?

Higher education institutions are major employers and substantial contributors to national economies. Yet there is a notable lack of comprehensive research on the practicalities of academic work, particularly with respect to how we bring our ‘whole self’ to work.

Just about everyone in academia is dealing with some aspect of their lives which affects how they do their work. Some are neurodiverse, with neurodiverse teenagers at home. Others may have a disability and are part of an under-represented group. More of us than you would think face financial precariousness and just being a woman can result in being given more of the ‘academic housework’. The impact of these various circumstances can be negative or positive from the employer point of view. For example, we know that neurodivergent academics spend a lot of energy ‘masking’ to make other people’s work lives easier, often at the expense of their own wellbeing (Jones, 2023). But we also know that including neurodiverse people in research groups can increase scientific productivity. At the same time, many neurodivergent people avoid disclosing for fear of stigma (even the word ‘disclose’ suggests that individuals should feel shame for merely being who they are).

Benefits for our employers can come at a great cost for us as individuals. While a body of literature exists on factors that affect student academic performance in university settings, there is no equivalent focus on university staff. The literature on students helps us design appropriate processes and services to try to even out the playing field and help everyone reach their potential. But we do not show this same compassion towards ourselves. The existing discourse on academics as workers tends to revolve around output metrics and shallow performance measures. This narrow focus fails to capture the full spectrum of academic labour and our lived experiences.

Our research aims to fill this gap by exploring how academics experience their work from their own perspectives. We seek to understand how the production of knowledge occurs, how academic work is constructed and experienced through daily practices, with a specific focus on academic productivity and distraction. We want to see how various bio-demographic factors interrelate and impact feelings like overwhelm and exhaustion.

Why this research matters

The importance of this study is multifaceted:

1. Informing Policy and Practice: By gaining a deeper understanding of academic work patterns, institutions can develop more effective policies to support their staff and enhance productivity and wellbeing.

2. Addressing Inequalities: The COVID-19 pandemic has highlighted and exacerbated existing inequalities in academia. Our research will explore how factors such as gender, caring responsibilities, and neurodiversity impact academic work experiences.

3. Adapting to Change: As the higher education sector continues to evolve, particularly in the wake of the pandemic and the rise of digital technologies like AI, it’s crucial to understand how these changes affect academic work practices.

4. Supporting Well-being: By examining the interplay between productivity, distraction, and work intensity, we can identify strategies to better support academics’ well-being and job satisfaction.

5. Enhancing Knowledge Production: Ultimately, by understanding and improving the conditions of academic work, we can enhance the quality and quantity of knowledge production in higher education and make better classrooms for everyone.

A comprehensive approach

Our study employs a mixed-methods approach, combining a large-scale survey with follow-up interviews. This methodology allows us to capture both broad trends and individual experiences, providing a nuanced picture of academic work life.

The survey covers a wide range of topics, including:

– Perceptions of academic productivity

– Experiences of distraction and focus

– Work distribution across research, teaching, and administration

– Impact of factors such as neurodiversity, caring responsibilities, and chronic conditions

– Use of technology and AI in academic work

– Feelings of belonging and value within the academic community

We are particularly interested in exploring how these factors intersect and influence each other. For instance, how does neurodiversity impact experiences of productivity and distraction? How do caring responsibilities interact with gender in relation to the number of hours worked and where the work takes place? And who thinks AI is helpful to their work and how are people ‘cognitively offloading’ to machines?

Call for participation

The success of this research hinges on wide participation from across the academic community. We are seeking respondents from all career stages, disciplines, and geographical locations. Whether you’re a seasoned professor or a new PhD student, whether you identify as neurodivergent or not, whether you love academic life or find it challenging – your experiences are valuable and needed.

Moreover, this research provides an opportunity for self-reflection. By engaging with the survey questions, you may gain new insights into your own work practices and experiences, potentially leading to personal growth and improved work strategies.

Looking ahead

The findings from this study will be disseminated through various channels, including academic publications, teaching materials, and potentially, policy recommendations. We are committed to making our results accessible and applicable to the wider academic community.

We stand at a critical juncture in higher education. As the sector faces unprecedented challenges and changes, understanding the nature of academic work has never been more important. By participating in this research, you can play a crucial role in shaping the future of academia.

To participate in the survey or learn more about the study, please visit the survey here: https://anu.au1.qualtrics.com/jfe/form/SV_eEeXg1L3RZJJWce.

Professor Inger Mewburn is the Director of Researcher Development at The Australian National University where she oversees professional development workshops and programs for all ANU researchers. Aside from creating new posts on the Thesis Whisperer blog (www.thesiswhisperer.com), she writes scholarly papers and books about research education, with a special interest in post PhD employability, research communications and neurodivergence.

Reference

Jones, S (2023) ‘Advice for autistic people considering a career in academia’ Autism 27(7) pp 2187–2192


by Leave a comment

Spotlight on the inclusion process in developing AI guidance and policy

by Lilian Schofield and Joanne J. Zhang

Introduction

When the discourse on ChatGPT started gaining momentum in higher education in 2022, the ‘emotions’ behind the response of educators, such as feelings of exclusion, isolation, and fear of technological change, were not initially at the forefront. Even educators’ feelings of apprehension about the introduction and usage of AI in education, which is an emotional response, were not given much attention. This feeling was highlighted by Ng et al (2023), who stated that many AI tools are new to educators, and many educators may feel overwhelmed by them due to a lack of understanding or familiarity with the technology. The big issues then were talks on banning the use of ChatGPT, ethical and privacy concerns, inclusive issues and concerns about academic misconduct (Cotton et al, 2023; Malinka et al, 2023; Rasul et al, 2023; Zhou & Schofield, 2023).

As higher education institutions started developing AI guidance in education, again the focus seemed to be geared towards students’ ethical and responsible usage of AI and little about educators’ guidance. Here we reflect on the process of developing the School of Business and Management, Queen Mary University of London’s AI guidance through the lens of inclusion and educators’ ‘voice’. We view ‘inclusion’ as the active participation and contribution of educators in the process of co-creating the AI policy alongside multiple voices from students and staff.

Co-creating inclusive AI guidance

Triggered by the lack of clear AI guidance for students and educators, the School of Business and Management at the Queen Mary University of London (QMUL) embarked on developing AI guidance for students and staff from October 2023 to March 2024.  Led by Deputy Directors of Education Dr Joanne J. Zhang and Dr Darryn Mitussis, the guidance was co-created with staff members through different modes, such as the best practice sharing sessions, staff away day, student-staff consultation, and staff consultation. These experiences helped shape the inclusive way and bottom-up approach of developing the AI guidance. The best practice sharing sessions allowed educators to contribute their expertise as well as provide a platform to voice their fears and apprehensions about adopting and using AI for teaching. The sessions acted as a space to share concerns and became a space where educators could have a sense of relief and solidarity. Staff members shared that knowing that others share similar apprehensions was reassuring and reduced the feeling of isolation. This collective space helped promote a more collaborative and supportive environment for educators to comfortably explore AI applications in their teaching.

Furthermore, the iterative process of developing this guidance has engaged different ‘voices’ within and outside the school. For instance, we discussed with the QMUL central team their approach and resources for facilitating AI usage for students and staff. We discussed Russell Group principles on AI usage and explored different universities’ AI policies and practices. The draft guideline was discussed and endorsed at the Teaching Away Day and education committee meetings. As a result, we suggested three principles for developing effective practices in teaching and learning:

  1. Explore and learn.
  2. Discuss and inform.
  3. Stress test and validate.

Key learning points from our process include having the avenue to use voice, whether in support of AI or not, and ensuring educators are active participants in the AI guidance-making process. This is also reflected in the AI guidance, which supports all staff in developing effective practices at their own pace.

Consultation with educators and students was an important avenue for inclusion in the process of developing the AI policy. Open communication and dialogue facilitated staff members’ opportunities to contribute to and shape the AI policy. This consultative approach enhanced the inclusion of educators and strengthened the AI policy.

Practical suggestions

Voice is a powerful tool (Arnot & Reay, 2007). However, educators may feel silenced and isolated without an avenue for their  voice. This ‘silence’ and isolation takes us back to the initial challenges experienced at the start of AI discourse, such as apprehension, fear, and isolation. The need to address these issues is pertinent, especially now when employers, students and higher education drive AI to be embedded in the curriculum and have AI-skilled graduates (Southworth et al, 2023). A co-creative approach to developing AI policies is crucial to enable critique and learning, promoting a sense of ownership and commitment to the successful integration of AI in education.

The process of developing an AI policy itself serves as the solution to the barriers to educators adopting AI in their practice and an enabler for inclusion. It ensures educators’ voices are heard, addresses their fears, and finds effective ways to develop a co-created AI policy. This inclusive participatory and co-creative approach helped mitigate fears associated with AI by creating a supportive environment where apprehensions can be openly discussed and addressed.

The co-creative approach of developing the policy with educators’ voices plays an important role in AI adoption. Creating avenues, such as the best practice sharing sessions where educators can discuss their experiences with AI, both positive and negative, ensures that voices are heard and concerns are acknowledged and addressed. This collective sharing builds a sense of community and support, helping to alleviate individual anxieties.

Steps that could be taken towards an inclusive approach to developing an inclusive AI guidance and policy are as follows:

  1. Set up the core group – Director for Education, chair of the exam board, and the inclusion of educators from different subject areas. Though the development of AI guidance can have a top-down approach, it is important that the group set-up is inclusive of educators’ voices and concerns.
  2. Design multiple avenues for educators ‘voices’ to be heard (best practice sharing sessions within and cross faulty, teaching away day).
  3. Communication channels are clear and open for all to contribute.
  4. Engaging all staff and students – hearing from students directly is powerful for staff, too; we learned a lot from students and included their voices in the guidance.
  5. Integrate and gain endorsements from the school management team. Promoting educators’ involvement in creating AI guidance legitimises their contributions and ensures that their insights are taken seriously. Additionally, such endorsement ensures that AI guidance is aligned with the needs and ethical considerations of those directly engaged and affected by the guidance.

Conclusion

As many higher education institutions move towards embedding AI into the curriculum and become clearer in their AI guidance, it is crucial to acknowledge and address the emotional dimensions educators face in adapting to AI technologies in education. Educators’ voices in contributing to AI policy and guidance are important in ensuring that they are clear about the guidance, embrace it and are upskilled in order for the embedding and implementation of AI in teaching and learning to be successful.

Dr. Lilian Schofield is a senior lecturer in Nonprofit Management and the Deputy Director of Student Experience at the School of Business and Management, Queen Mary University of London. Her interests include critical management pedagogy, social change, and sustainability. Lilian is passionate about incorporating and exploring voice, silence, and inclusion into her practice and research. She is a Queen Mary Academy Fellow and has taken up the Learning and Teaching Enhancement Fellowship, where she works on student skills enhancement practice initiatives at Queen Mary University of London.

Dr Joanne J. Zhang is Reader in Entrepreneurship, Deputy Director of Education at the School of Business and Management, Queen Mary University of London, and a visiting fellow at the University of Cambridge. She is the ‘Entrepreneurship Educator of the Year’, Triple E European Award 2022. Joanne is also the founding director of the Entrepreneurship Hub , and the QM Social Venture Fund  - the first student-led social venture fund investing in ‘startups for good’ in the UK.  Joanne’s research and teaching interests are entrepreneurship, strategy and entrepreneurship education. She has led and engaged in large-scale research and scholarship projects totalling over GBP£7m.  Email: Joanne.zhang@qmul.ac.uk


by 1 Comment

For meta or for worse…

by Paul Temple

Remember the Metaverse? Oh, come on, you must remember it, just think back a year, eighteen months ago, it was everywhere! Mark Zuckerberg’s new big thing, ads everywhere about how it was going to transform, well, everything! I particularly liked the ad showing a school group virtually visiting the Metaverse forum in ancient Rome, which was apparently going to transform their understanding of the classical world. Well, that’s what $36 bn (yes, that’s billion) buys you. Accenture were big fans back then, displaying all the wide-eyed credulity expected of a global consultancy firm when they reported in January 2023 that “Growing consumer and business interest in the Metaverse [is] expected to fuel [a] trillion dollar opportunity for commerce, Accenture finds”.

It was a little difficult, though, to find actual uses of the Metaverse, as opposed to vague speculations about its future benefits, on the Accenture website. True, they’d used it in 2022 to prepare a presentation for Tuvalu for COP27; and they’d created a virtual “Global Collaboration Village” for the 2023 Davos get-together; and we mustn’t overlook the creation of the ChangiVerse, “where visitors can access a range of fun-filled activities and social experiences” while waiting for delayed flights at Singapore’s Changi airport. So all good. Now tell me that I don’t understand global business finance, but I’d still be surprised if these and comparable projects added up to a trillion dollars.

But of course that was then, in the far-off days of 2023. In 2024, we’re now in the thrilling new world of AI, do keep up! Accenture can now see that “AI is accelerating into a mega-trend, transforming industries, companies and the way we live and work…better positioned to reinvent, compete and achieve new levels of performance.” As I recall, this is pretty much what the Metaverse was promising, but never mind. Possible negative effects of AI? Sorry, how do you mean, “negative”?

It’s been often observed that every development in communications and information technology – radio, TV, computers, the internet – has produced assertions that the new technology means that the university as understood hitherto is finished. Amazon is already offering a dozen or so books published in the last six months on the impact of the various forms of AI on education, which, to go by the summaries provided, mostly seem to present it in terms of the good, the bad, and the ugly. I couldn’t spot an “end of the university as we know it” offering, but it has to be along soon.

You’ve probably played around with ChatGPT – perhaps you were one of its 100 million users logging-on within two months of its release – maybe to see how students (or you) might use it. I found it impressive, not least because of its speed, but at the same time rather ordinary: neat B-grade summaries of topics of the kind you might produce after skimming the intro sections of a few standard texts but, honestly, nothing very interesting. Microsoft is starting to include ChatGPT in its Office products; so you might, say, ask it to list the action points from the course committee minutes over the last year, based on the Word files it has access to. In other words, to get it to undertake, quickly and accurately, a task that would be straightforward yet tedious for a person: a nice feature, but hardly transformative. (By the way, have you tried giving ChatGPT some text it produced and asking where it came from? It said to me, in essence, I don’t remember doing this, but I suppose I might have: it had an oddly evasive feel.)

So will AI transform the way teaching and learning works in higher education? A recent paper by Strzelecki (2023) reporting on an empirical study of the use of ChatGPT by Polish university students notes both the potential benefits if it can be carefully integrated into normal teaching methods – creating material tailored to individuals’ learning needs, for example – as well as the obvious ethical problems that will inevitably arise. If students are able to use AI to produce work which they pass off as their own, it seems to me that that is an indictment of under-resourced, poorly-managed higher education which doesn’t allow a proper engagement between teachers and students, rather than a criticism of AI as such. Plagiarism in work that I marked really annoyed me, because the student was taking the course team for fools, assuming our knowledge of the topic was as limited as theirs. (OK, there may have been some very sophisticated plagiarism which I missed, but I doubt it: a sophisticated plagiarist is usually a contradiction in terms.)

The 2024 Consumer Electronics Show (CES), held in Las Vegas in January 2024, was all about AI. Last year it was all about the Metaverse; this year, although the Metaverse got a mention, it seemed to rank in terms of interest well below the AI-enabled cat flap on display – it stops puss coming in if it’s got a mouse in its jaws – which I’m guessing cost rather less than $36bn to develop. I’ve put my name down for one.

Dr Paul Temple is Honorary Associate Professor in the Centre for Higher Education Studies, UCL Institute of Education.


by 4 Comments

Fair use or copyright infringement? What academic researchers need to know about ChatGPT prompts

by Anita Toh

As scholarly research into and using generative AI tools like ChatGPT becomes more prevalent, it is crucial for researchers to understand the intersections of copyright, fair use, and use of generative AI in research. While there is much discussion about the copyrightability of generative AI outputs and the legality of generative AI companies’ use of copyrighted material as training data (Lucchi, 2023), there has been relatively little discussion about copyright in relation to user prompts. In this post, I share an interesting discovery about the use of copyrighted material in ChatGPT prompts.

Imagine a situation where a researcher wishes to conduct a content analysis on specific YouTube videos for academic research. Does the researcher need to obtain permission from YouTube or the content creators to use these videos?

As per YouTube’s guidelines, researchers do not require explicit copyright permission if they are using the content for “commentary, criticism, research, teaching, or news reporting,”as these activities fall under the umbrella of fair use (Fair Use on YouTube – YouTube Help, 2023).

What about this scenario? A researcher wants to compare the types of questions posed by investors on the reality television series, Shark Tank, with questions generated by ChatGPT as it roleplays an angel investor. The researcher plans to prompt ChatGPT with a summary of each Shark Tank pitch and ask ChatGPT to roleplay as an angel investor and ask questions. In this case, would the researcher need to obtain permission from Shark Tank or its production company, Sony Pictures Television?

In my exploration, I discovered that it is indeed crucial to obtain permission from Sony Pictures Television. ChatGPT’s terms of service emphasise that users should “refrain from using the service in a manner that infringes upon third-party rights. This explicitly means the input should be devoid of copyrighted content unless sanctioned by the respective author or rights holder” (Fiten & Jacobs, 2023).

I therefore initiated communication with Sony Pictures Television, seeking approval to incorporate Shark Tank videos in my research. However, my request was declined by Sony Pictures Television in California, citing “business and legal reasons”. Undeterred, I approached Sony Pictures Singapore, only to receive a reaffirmation that Sony cannot endorse my proposed use of their copyrighted content “at the present moment”. They emphasised that any use of their copyrighted content must strictly align with the Fair Use doctrine.

This evokes the question: Why doesn’t the proposed research align with fair use? My initial understanding is that the fair use doctrine allows re-users to use copyrighted material without permission from the right holders for news reporting, criticism, review, educational and research purposes (Copyright Act 2021 Factsheet, 2022).

In the absence of further responses from Sony Pictures Television, I searched the web for answers.

Two findings emerged which could shed light on Sony’s reservations:

  • ChatGPT’s terms highlight that “user inputs, besides generating corresponding outputs, also serve to augment the service by refining the AI model” (Fiten & Jacobs, 2023; OpenAI Terms of Use, 2023).
  • OpenAI is currently facing legal action from various authors and artists alleging copyright infringement (Milmo, 2023). They contend that OpenAI had utilized their copyrighted content to train ChatGPT without their consent. Adding to this, the New York Times is also contemplating legal action against OpenAI for the same reason (Allyn, 2023).

These revelations point to a potential rationale behind Sony Pictures Television’s reluctance: while use of their copyrighted content for academic research might be considered fair use, introducing this content into ChatGPT could infringe upon the non-commercial stipulations (What Is Fair Use?, 2016) inherent in the fair use doctrine.

In conclusion, the landscape of copyright laws and fair use in relation to generative AI tools is still evolving. While previously researchers could rely on the fair use doctrine for the use of copyrighted material in their research work, the availability of generative AI tools now introduces an additional layer of complexity. This is particularly pertinent when the AI itself might store or use data to refine its own algorithms, which could potentially be considered a violation of the non-commercial use clause in the fair use doctrine. Sony Pictures Television’s reluctance to grant permission for the use of their copyrighted content in association with ChatGPT reflects the caution that content creators and rights holders are exercising in this new frontier. For researchers, this highlights the importance of understanding the terms of use of both the AI tool and the copyrighted material prior to beginning a research project.

Anita Toh is a lecturer at the Centre for English Language Communication (CELC) at the National University of Singapore (NUS). She teaches academic and professional communication skills to undergraduate computing and engineering students.


by Leave a comment

What do artificial intelligence systems mean for academic practice?

by Mary Davis

I attended and made a presentation at the SRHE Roundtable event ‘What do artificial intelligence systems mean for academic practice?’ on 19 July 2023. The roundtable brought together a wide range of perspectives on artificial intelligence: philosophical questions, problematic results, ethical considerations, the changing face of assessment and practical engagement for learning and teaching. The speakers represented a range of UK HEI contexts, as well as Australia and Spain, and a variety of professional roles including academic integrity leads, lecturers of different disciplines and emeritus professors.

The day began with Ron Barnett’s fierce defence of the value of authorship and the concerns about what it means to be a writer in a Chatbot world. Ron argued that use of AI tools can lead to an erosion of trust; the essential trust relationship between writer and reader in HE and wider social contexts such as law may disintegrate and with it, society. Ron reminded us of the pain and struggle of writing and creating an authorial voice that is necessary for human writing. He urged us to think about the frameworks of learning such as ‘deep learning’ (Ramsden), agency and internal story-making (Archer) and his own ‘Will to Learn’, all of which could be lost. His arguments challenged us to reflect on the far-reaching social consequences of AI use and opened the day of debate very powerfully.

I then presented the advice I have been giving to students at my institution using my analysis of student declarations of AI use which I had categorised using a traffic light system for appropriate use (eg checking and fixing a text before submission); at risk use (eg paraphrasing and summarising); and inappropriate use (eg using assignment briefs as prompts and submitting the output as own work). I got some helpful feedback from the audience that the traffic lights provided useful navigation for students. Coincidentally, the next speaker Angela Brew also used a traffic light system to guide students with AI. She argued for the need to help students develop a scholarly mindset, for staff to stop teaching as in the 18th Century with universities as foundations of knowledge. Instead, she proposed that everyone at university should be a discoverer, a learner and producer of knowledge, as a response to AI use.

Stergios Aidinlis provided an intriguing insight into practical use of AI as part of a law degree. In his view, generative AI can be an opportunity to make assessment currently fit for purpose. He presented a three-stage model of learning with AI comprising: stage 1 as using AI to produce a project pre-mortem to tackle a legal problem as pre-class preparation; stage 2 using AI as a mentor to help students solve a legal problem in class; and stage 3 using AI to evaluate the technology after class. Stergios recommended Mollick and Mollick (2023) for ideas to help students learn to use AI. The presentation by Stergios stood out in terms of practical ideas and made me think about the availability of suitable AI tools for all students to be able to do tasks like this.

The next session by Richard Davies, one of the roundtable convenors, took a philosophical direction in considering what a ‘student’s own work’ actually means, and how we assess a student’s contribution. David Boud returned the theme to assessment and argued that three elements are always necessary: assuring learning outcomes have been met (summative assessment), enabling students to use information to aid learning (formative assessment) and building students’ capacity to evaluate their learning (sustainable assessment). He argued for a major re-design of assessment, that still incorporates these elements but avoids tasks that are no longer viable.

Liz Newton presented guidance for students which emphasized positive ways to use AI such as using it for planning or teaching, which concurred with my session. Maria Burke argued for ethical approaches to the use of AI that incorporate transparency, accountability, fairness and regulation, and promote critical thinking within AI context. Finally, Tania Alonso presented her ChatGPTeaching project with seven student rules for use of ChatGPT, such as proposing use only for areas of the student’s own knowledge.

The roundtable discussion was lively and our varied perspectives and experiences added a lot to the debate; I believe we all came away with new insights and ideas. I especially appreciated the opportunity to look at AI from practical and philosophical viewpoints. I am looking forward to the ongoing sessions and forum discussions. Thanks very much to SRHE for organising this event.

Dr Mary Davis is Academic Integrity Lead and Principal Lecturer (Education and Student Experience) at Oxford Brookes University. She has been a researcher of academic integrity since 2005 and has carried out extensive research on plagiarism, use of text-matching tools, the development of source use, proofreading, educational responses to academic conduct issues and focused her recent research on inclusion in academic integrity. She is on the Board of Directors of the International Center for Academic Integrity and co-chair of the International Day of Action for Academic Integrity.


by Leave a comment

Understanding the value of EdTech in higher education

by Morten Hansen

This blog is a re-post of an article first published on universityworldnews.com. It is based on a presentation to the 2021 SRHE Research Conference, as part of a Symposium on Universities and Unicorns: Building Digital Assets in the Higher Education Industry organised by the project’s principal investigator, Janja Komljenovic (Lancaster). The support of the Economic and Social Research Council (ESRC) is gratefully acknowledged. The project introduces new ways to think about and examine the digitalising of the higher education sector. It investigates new forms of value creation and suggests that value in the sector increasingly lies in the creation of digital assets.

EdTech companies are, on average, priced modestly, although some have earned strong valuations. We know that valuation practices normally reflect investors’ belief in a company’s ability to make money in the future. We are, however, still learning about how EdTech generates value for users, and how to take account of such value in the grand scheme of things.


Valuation and deployment of user-generated data

EdTech companies are not competing with the likes of Google and Facebook for advertisement revenue. That is why phrases such as ‘you are the product’ and ‘data is the new oil’ yield little insight when applied to EdTech. For EdTech companies, strong valuations hinge on the idea that technology can bring use value to learners, teachers and organisations – and that they will eventually be willing to pay for such benefits, ideally in the form of a subscription. EdTech companies try to deliver use value in multiple ways, such as deploying user-generated data to improve their services. User-generated data are the digital traces we leave when engaging with a platform: keyboard strokes and mouse movements, clicks and inactivity.


The value of user-generated data in higher education

The gold standard for unlocking the ‘value’ of user-generated data is to bring about an activity that could otherwise not have arisen. Change is brought about through data feedback loops. Loops consist of five stages: data generation, capture, anonymisation, computation and intervention. Loops can be long and short.


For example, imagine that a group of students is assigned three readings for class. Texts are accessed and read on an online platform. Engagement data indicate that all students spent time reading text 1 and text 2, but nobody read text 3. As a result of this insight, come next semester, text 3 is replaced by a more ‘engaging’ text. That is a long feedback loop.


Now, imagine that one student is reading one text. The platform’s machine learning programme generates a rudimentary quiz to test comprehension. Based on the students’ answers, further readings are suggested or the student is encouraged to re-read specific sections of the text. That is a short feedback loop.


In reality, most feedback loops do not bring about activity that could not have happened otherwise. It is not like a professor could not learn, through conversation, which texts are better liked by students, what points are comprehended, and so on. What is true, though, is that the basis and quality of such judgments shifts. Most importantly, so does the cost structure that underpins judgment.


The more automated feedback loops are, the greater the economy of scale. ‘Automation’ refers to the decoupling of additional feedback loops from additional labour inputs. ‘Economies of scale’ means that the average cost of delivering feedback loops decreases as the company grows.


Proponents of machine learning and other artificial intelligence approaches argue that the use value of feedback loops improves with scale: the more users engage in the back-and-forth between generating data, receiving intervention and generating new data, the more precise the underlying learning algorithms become in predicting what interventions will ‘improve learning’.


The platform learns and grows with us

EdTech platforms proliferate because they are seen to deliver better value for money than the human-centred alternative. Cloud-based platforms are accessed through subscriptions without transfer of ownership. The economic relationship is underwritten by law and continued payment is legitimated through the feedback loops between humans and machines: the platform learns and grows with us, as we feed it.


Machine learning techniques certainly have the potential to improve the efficiency with which we organise certain learning activities, such as particular types of student assessment and monitoring. However, we do not know which values to mobilise when judging intervention efficacy: ‘value’ and ‘values’ are different things.


In everyday talk, we speak about ‘value’ when we want to justify or critique a state of affairs that has a price: is the price right, too low, or too high? We may disagree on the price, but we do agree that something is for sale. At other times we reject the idea that a thing should be for sale, like a family heirloom, love or education. If people tell us otherwise, we question their values. This is because values are about relationships and politics.


When we ask about the values of EdTech in higher education, we are really asking: what type of relations do we think are virtuous and appropriate for the institution? What relationships are we forging and replacing between machines and people, and between people and people?


When it comes to the application of personal technology we have valued convenience, personalisation and seamlessness by forging very intimate but easily forgettable machine-human relations. This could happen in the EdTech space as well. Speech-to-text recognition, natural language processing and machine vision are examples of how bonds can be built between humans and computers, aiding feedback loops by making worlds of learning computable.


Deciding on which learning relations to make computable, I argue, should be driven by values. Instead of seeing EdTech as a silver bullet that simply drives learning outcomes, it is more useful to think of it as technology that mediates learning relations and processes: what relationships do we value as important for students and when is technology helpful and unhelpful in establishing those? In this way, values can help us guide the way we account for the value of edtech.

Morten Hansen is a research associate on the Universities and Unicorns project at Lancaster University, and a PhD student at the Faculty of Education, University of Cambridge, United Kingdom. Hansen specialises in education markets and has previously worked as a researcher at the Saïd Business School in Oxford.