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On 11 June 2025 I attended a continuing professional development event concerning Generative AI that was intended for tutors. The ALSPD team has set up an online conference site to complement the session. What follows is a set of notes I have made during the session, along with some accompanying thoughts and reflections.

Keynote Address: Reclaiming the Pace of Digital Productivity in the Age of AI

The opening keynote was by Maren Deepwell. The abstract of her talk emphasised the pace of digital content production, and mentioned the need for uncovering our own ‘critical cadence’. A key phrase I picked up on was, of course, digital literacies. This is now morphing into a related idea of AI literacies where we become more aware of the ethical, cultural and social implications of AI tool use.

I made a note of a couple of links from OpenED Culture projects:

• Dimensions of AI Literacies: AI Literacies Applied
• Dimensions of AI Literacies: a diverse and interconnected set of competencies.

Moving on from AI Literacy, a further theme that was highlighted was digital wellbeing; a term I’ve not heard before, but one that can mean different things to different people. It can relate to the use of technology for healthcare and physical activity, or it could be linked to the notion of a ‘digital detox’, which is a phrase that I struggle with. We were introduced to a resource called the Data Detox Kit which contains a number of guides.

Another curious term was ‘digital rewilding’, which I had never heard before. There was a reference to a site called HackThisCourse. I spent a few minutes looking at this page, and was puzzled; I couldn’t immediately grasp what it was all about – but it does look as if it relates back to the theme of digital pedagogy, and creative uses of digital technology. This point, of course, links to the emergence of GenAI.

An important role of a keynote is to challenge and to guide. An interesting question is: what is next? We were directed to Helen Beetham’s substack, which features articles and podcasts about AI.

A key question asked: what does digital capabilities for the AI age look like? During the presentation, I noted that GenAI can be useful if you’re in a position to critically evaluate what it produces.

Session 1 - Short Talks

Teaching and assessing students' use of GenAI

Jonquil Lowe, Senior Lecturer in Economics, from the Faculty of Arts and Social Sciences (FASS) asks the question: “what exactly should we be teaching and assessing about GenAI?” I noted three important suggestions which relate to taching: include acceptable and ethical use, cover generic Gen AI skills, and discipline specific skills. I noted the suggestion that perhaps every module might need a Gen AI aspect, which would change and develop as student moved to higher levels of study. I also noted an interesting point that Gen AI may offer useful guidance for students for whom English is not their first language.

Within the chat, a link to an OpenLearn course was shared: Getting started with generative artificial intelligence (AI) 

Online academic misconduct 2025: real location, face to face assessment the only effective response

Next up was David Pell, Associate Lecturer from FASS. David was refreshingly direct in his opinions. In comparison to collusion, plagiarism, essay mills, GenAI is something that is different; it is ‘huge’. It is described as a useful tool for potential cheaters. David shared a direct opinion, and one that I agree with, which is: only real life, proctored assessment provides a guarantee against academic misconduct.

Some research was shared:

• University of Reading: AI exam submissions can go undetected – study (BBC)
• An OU study: Developing robust assessment in the light of Generative AI developments
• Richards et al.(2024) Bob or Bot: Exploring ChatGPT’s Answers to University Computer Science Assessment
• Fleckenstein et al. (2024) Do teachers spot AI? Evaluating the detectability of AI-generated texts among student essays

The very clear point is that GenAI can produce text that is not detectable by educators.

Session 2: The AI Hydra in Assessment - a Nine Point Response to the Nine-headed Beast

The next session, by Mark Campbell, Associate Lecturer, STEM, presents a model of potential responses to GenAI. These are: progression, policing, procedures, pilots, principles and strategy, polities, plans, programmes and modules, and practices.

In addition to these useful responses, I also made a note of the following resources:

1. OU Generative AI Staff Development
2. JISC Innovation resources: Embrace artificial intelligence (AI) with confidence
3. AI maturity toolkit for tertiary education
4. OU being digital resources (library services)

Session 3 – Short Talks

Transformative Teaching with AI - re-purposing the meaning of student-centred learning

Denise Dear, Associate Lecturer, STEM moves from the topic of assessment to the topic of teaching, and asks “how lecturers can use AI to enhance student topic knowledge retention, increase student engagement, improve student confidence, reduce student stress levels and assist students as a revision tool”. I haven’t used any of these tools as a part of my teaching, so I was curious about how it might be applied.

GenAI was presented as a potential study buddy. It has the potential to provide summaries of passages of text, may be also to provide suggestions about essay structures (which, as an arts student, terrifies me), and generate interactive quizzes. In other words, there is potential that it can provide support that is tailored to the needs of individual students.

During the session tutors were asked: how do you use it? Answers include case scenarios, sample computer code, scenario simulations, generating personas – but a word of caution was highlighted: it gets maths wrong. (LLMs also have no idea how to play chess; they know about language, but they cannot reason).

The discussion of teaching led us back to assessment. How do we assess learning? In an activity, some answers were: viva voce assessments, feedback from small group work, asking students to make an audio recording.

Generative AI and Good Academic English

The final session of the day was by Claire Denton, Associate Lecturer, FASS. Use of GenAI can suggest a lack of confidence with academic English. Telltale signs may be the use of a generic tone, no references to module materials and no supporting evidence. Or alternatively, students might begin with their own voice, which will then switch where text from AI is woven into their answer. A question that tutors face is: how do students provide feedback to students when this happens?

Anyone from any faculty can access the various subject sites. There is something called the Arts and Humanities Writing Centre which contains some useful writing resources. The site also provides a link to good academic English, and a phrase bank. (There are, of course, other study skills resources available. I’ve shared a summary of some of them through this article Study Skills Resources: what is available?)

Claire shared some great tips that could be shared to students, including: if you have spent some hours looking at a piece of writing, stop. Take your time to read it aloud. You will then pick up if you need additional punctuation, have used too many of the same words, or have repeated the same point you have made earlier. The key point is, of course, if tutors spot that GenAI might have been used, there may lie opportunities to provide additional help and guidance.

Reflections

The tone of this event implies that GenAI is going to be profoundly transformative. With this in mind, I remember the tone (and the enthusiasm) that accompanied the development of MOOCs and Open Educational Resources, and the view that they both had the potential to fundamentally change the nature and character of education. I've not heard MOOCs mentioned for a while. A lot of froth has been created in the wake of the emergence of GenAI.

Some of the important themes I picked up on from this session were importance of policies, the challenges to academic conduct and integrity, as well as the potential opportunities that targeted GenAI may present. It was interesting to hear GenAI being pitched in terms of being a ‘study buddy’. This is an interesting use case, but I’m also mildly worried by the fact that these free tools are remembering every single interaction we have ever had with them. 

I’m a GenAI sceptic. In my time as a computer scientist, I have seen AI bubbles come and go. In the 1970, researchers claimed they had solved all the key problems, and it was just a matter of scaling up until we get our own personal robotic butler. When it comes to GenAI I do feel that we’re approaching the Peak of Inflated Expectations and it won’t be long before we crash into the Trough of Disillusionment (see: Gartner hype cycle, Wikipedia). If we’re not at the GenAI hype peak already, we probably will be in 18 months time. (I'll be very happy to eat these words if proved wrong).

I remember a computer book from the mid 1980s. It contained a program that you type into your home computer, so it would ‘talk back to you’. It was written in a programming language called BASIC and was only three or four pages long. It recycled your words; it was a simpler version of a 1967 computer program called ELIZA. I once described GenAI as ‘ELIZA on steroids’. GenAI is seductive, since LLMs uncannily reflect back to us echoes of our own humanity. Looking to the bigger picture, philosophers still can’t really define what intelligence is. I don’t know who said it, but I always return to the phrase that ‘AI is really clever people getting really dumb machines to do things that look clever’.

All this takes me to a mild (and yet) important constructive criticism of the event: it was significantly light on ethics. We need to slow down and ask the question: who runs and owns these GenAI tools? What are the consequences for us, and for society when these are used? To what extent does a LLM create a model of its users? How are these models shared? What terms and conditions have we signed up to? There is, of course, the slightly important issues of transparency and copyright. It is likely that these words are, in some way or other, hoovered up by a LLM engine.

Out of curiosity, I asked Copilot the following question: “Who is Chris Douce and what does he think about artificial intelligence?” Here’s the answer: “Chris Douce is a Senior Lecturer in the School of Computing and Communications at the Open University. He has a background in software engineering and educational technology, with a focus on distance learning and accessibility. Regarding his views on artificial intelligence (AI), Chris Douce has expressed interest in how AI can be integrated into educational technologies to enhance learning experiences. He has discussed the potential of AI to provide personalized learning and support for students, particularly in distance learning environments. However, he also emphasizes the importance of ethical considerations and the need to address challenges related to accessibility and inclusivity”. That’s a pretty good summary, but I think it is, perhaps, an understatement.

One of the useful aspects of this session was the sharing of links to many related resources and references. There is always homework to do. Now, that’s something that GenAI can’t directly help me with. It’s down to me to do the work. Anything else would be cheating.

Addendum

I shared this post with a colleague, who recommended two interesting resources; a book and a podcast:

Book: Bender, E. M. & Hannah, A. (2025) The AI Con: How to Fight Big Tech’s Hype and Create the Future We Want. Bodley Head.

Podcast: Mystery AI Hype Theater 3000 by Bender and Hannah (Distributed AI Research Institute)

Acknowledgements

Many thanks to all the presenters, and for the team who facilitated the event. Thanks are also extended to David Hales.

On 23 May 24 I attended an AL development event (in my capacity as an OU tutor) that was all about Generative AI (which is abbreviated to here as GenAI). This blog sits alongside a couple of other blogs that I shared last year that also relate to GenAI and what this means for education, distance learning, and education practice.

What follows is some notes that I made during a couple of the sessions I attended, and what points and themes I took away from them. I also share some critical perspectives. Since GenAI is a fast moving subject, not just in terms of the technology, but in terms of policy and institutional responses, what is presented here is also likely to age quickly.

Opening keynote

The event opened with a keynote by Mychelle Pride which had the subtitle: Generative AI in Learning, Teaching and Assessment I won’t summarise it at length. Instead, I’ll share some key points that I noted down.

One important point was that AI isn’t anything new. A couple of useful resources were shared, one from the popular press, How AI chatbots like ChatGPT or Bard work – visual explainer (The Guardian) and another from industry: The rise of generative AI: A timeline of breakthrough innovations (Qualcomm).

An interesting use case was shared through a YouTube video: Be My Eyes Accessibility with GPT-4. Although clearly choreographed, and without any indication of whether any of this was ‘live’, one immediately wonders whether this technology is solving the right problems. Maybe this scenario implicitly implies that visually impaired people should adapt to the sighted world, whereas perhaps a better solution might be for the world to adapt to people with visual impairments? I digress.

There are clear risks. One significant concern lies with the lack of transparency. Tools can be trained with data that contains biases; in computing there’s the notion of GiGO: garbage in, garbage out. There’s also the clear potential that GenAI tools may accept and then propagate misinformation. It is clear that “risks need to be considered, along with the potential opportunities”.

A point was shared from a colleague Michel Wermelinger who was quoted saying “academic conduct is a symptom, not the problem”, which directly takes us to the university’s academic conduct policies about plagiarism.

In this session I learnt a new term: “green light culture”. The point here was that there are a variety of positions that relate to GenAI: in HE there are policy decisions that range from ‘forbid’ to ‘go ahead’.

I made a note of a range of searching questions. One of them was: how might students use Generative AI? It might become a study assistant, it might facilitate language learning, or support with creative projects. Another question was: how could pedagogies be augmented by AI? Also, is there a risk of over dependence in how we use these tools? Could it prevent us from developing skills? How can we assess in a generative AI world? Some answers to this question may be to have project-based assessment, collaborative assessment, to use complex case studies, and to consider the use of oral assessments. 

A point is that students will be using Generative AI in the future, which means that the university has a responsibility to educate students about it

Towards the end of the keynote, there was some talk about all this being revolutionary (I’ll say more about this later). This led onto a closing provocative question: what differentiates you (the tutor) from Generative AI?

During the keynote, some interesting resources were shared:

• Alpha Go Documentary (YouTube)
• Jisc: A Generative AI Primer
• How to use generative AI creatively in Higher Education

Teaching and learning with AI across the curriculum

The aim of a session by Mirjam Hauck was to explore the connection between AI and pedagogy, and to also consider the issue of ethics.

Just like the previous presentation, there were some interesting resources that were shared. One of them was a talk: TED Talk: How AI could save (not destroy) education.

Another resource was a recent book, Practical Pedagogy: 40 New Ways to Teach and Learn by Mike Sharples which students and staff can access through the OU Library.

I had a quick look at it, just to see what these 40 new ways were. Taking a critical perspective, I realised that the vast majority of these approaches were already familiar to me, in one way or another. These are not necessarily ‘new’ but are instead presented in a new way, in a useful compendium. The text also shares a lot of informal web links, which immediately limits the longevity of the text. It does highlight academic articles, but it doesn’t always cite them within a description of a pedagogy. My view is: do consider this text as something that shares a useful set of ideas, rather than something that is definite.

During this session, there were some complementary reflections about how GenAI could be linked with pedagogy: it could be used to help with the generation of ideas (but to be mindful that it might be regenerating ideas and bits of text that may be subject to copyright), play a role within a Socratic dialogue, or act as a digital assistant for learning (which was sometimes called an AIDA – an AI digital assistant).

Power was mentioned in this session, with respect to the power that is exerted by the corporations that develop, run, and deploy AI tools. The point I had in my mind during this part of the session was: ‘do be mindful about who is running these products, why, and that they hope to get from them’.

A brief aside…

Whilst I was prepping this blog, I was sent a related email from Hello World magazine, which is written for computing educators. In that email, there was a podcast which had the title: What is the role of AI in your classroom? 

There was an interesting discussion about assessment, and asking the question of ‘how can this help with pedagogy?’ and ‘how can we adapt our own practices?’ A further question is: ‘is there a risk that we dumb down creativity?’

A scholarship question?

A few times this year tutors have been in touch with me, to ask the question: ‘I’ve seen a students answer in a script that makes me think they may well have used Generative AI. What do I do?’ Copying TMA questions, or any other elements of university materials into a Generative AI tool represents a breach of university policy, and can potentially be viewed as an academic conduct issue. The question is: what do tutors do about this? At the moment, and without any significant evidence, tutors must mark what they have been given.

An important scholarship question to ask is: how many tutors think they are being presented with assessments that may have been produced by Generative AI tools?

Reflections

There was a lot of take on board during this session. I need to find the time to sit down and work through some of the various resources that were shared in this session, which is (in part) the reason for this blog.

When I was a computing undergraduate I went to a couple of short seminars about the development of the internet. When it came to the topic of the web browser, our lecturer said: “this is never going to catch on; who is going to spend time creating all these web pages and HTML tags?” Every day I make use of a web browser; it is, of course, an important bit of software that is embedded within my phone. This connects with an important point: it is notoriously difficult to predict the future, especially when it comes to how technologies are used. There are often unintended consequences, both good and bad.

Being a former student of AI (late in the last century) I’m aware that the fashions that surround AI is cyclical. With each cycle of hype, there are new technologies and tools. Following an early (modern) cycle of AI, I remember a project called SHRDLU, which demonstrated an imaginary world, where users could interact with natural language. This led to an expression that they key challenges had been solved, and all that needs to be done is to scale everything up. Reality, of course, is a whole lot more complicated.

A really important point to bear in mind is that GenAI (in the general sense) cannot reason. You can’t play chess with it. There are, however, other tools within the AI toolset that can do reasoning. As a postgrad student, I had to write an expert system that helped to solve a problem: to figure out a path through a university campus.

I’ve also been around for long enough to see various cycles of hype regarding learning technologies: I joined when e-learning objects were the fashion of the day, then there was the virtual learning environment, and then there was a craze that related to learning analytics. In some respects, the current generation of AI feels like a new (temporary) craze.

Embedding AI into educational tools isn’t anything new. I remember first hearing about the integration of neural networks in the early 2000s.  In 2009 I was employed on a project that intended to provide customised digital resources for learners who have different requirements and needs.

As the models get bigger, more data they hoover up, and the greater potential of these tools generating nonsense. And here lies a paradox: to make effective use of GenAI within education, you need education.

Perhaps there is a difference between generally available generative AI, to generative AI that is aligned to particular contexts. This takes me to an important reflection: no GenAI tool or engine can ever know what your own context is. You might ask it some questions and get a sensible sounding response, but it will not know why you’re asking a question, and what purpose your intended answer may serve. This is why the results produced by a GenAI tool might look terrible, or suspicious if submitted as a part of an assessment. Context is everything, and assessments relate to your personal understanding of very particular learning context.

Although the notion of power and digital corporations was mentioned, there’s another type of power that wasn’t mentioned: electrical power. I don’t have figures to hand, but large language models require an inordinate amount of electrical energy to do what they do. Their use has real environmental consequences. It's easy to forget this.

Here is my view: it is important to be aware of what GenAI is all about, but it is also really important not to get carried away and caught up in what could be thought of as technological froth. It’s also important to always remember that technology can change faster than pedagogy. We need to apply effective pedagogy to teach about technology. 

In my eyes, GenAI, or AI in many of its other forms isn’t a revolution that will change everything, or is an existential threat to humanity; it is an evolution of a set of existing technologies.

It’s important to keep everything in perspective.

Resources

A number of resources were highlighted in this session which are worth having a quick look at:

• Open Learn: An introduction to artificial intelligence – a free short course that also addresses the history of AI.
• Exploring Generative AI: critical skills and ethical use – some useful resources from the OU library.
• Generative AI for students – OU policy guidance for students.

Acknowledgements

Many thanks to the presenters of this professional development event, and the team that put this event together. Lots to look at, and lots of think about.