Yesterday, via CODE, I had the pleasure of working with two groups studying for the online PGCert LTHE at University of London. I repaced ‘Speaker 1’ with my name in the AI generated transcript, ran it through (sensible colleague) Claude AI to generate this summary and then asked (much weirder colleague) ChatGPT to illustrate it. I particularly like the ‘Ethical Ai Ai!’ in the second one but generally would not use either image: I am not a fan of this sort of representation. Alt text are verbatim from chatGPT too.

I forgot to request UK spelling conventions but I’m reasonably happy with the tone. The content feels less passionate and more balanced than I think I am in real life but, realisitically, this follow up post would not exist at all if I’d had to have typed it up myself.

Introduction

Recent advances in artificial intelligence (AI) are raising profound questions for me as an educator. New generative AI tools like ChatGPT can create convincing text, images, and other media on demand. While this technology holds great promise, it also poses challenges regarding academic integrity, student learning, and the nature of assessment. In a lively discussion with teachers recently, I shared my insights on navigating this complex terrain.

The Allure and Risks of Generative AI

I acknowledge the remarkable capabilities of tools like ChatGPT. In minutes, these AIs can generate personalized lessons, multiple choice quizzes, presentation slides and more tailored to specific educational contexts. Teachers and students alike are understandably drawn to technologies that can enhance learning and make routine tasks easier. However, I also caution that generative AI carries risks. If over-relied on, it can undermine academic integrity, entrench biases, and deprive students of opportunities to develop skills. As I argue, banning these tools outright is unrealistic, but educators must remain vigilant about their appropriate usage. The solution resides in open communication, modifying assessments, and focusing more on process than rote products.

Rethinking Assessment in the AI Era

For me, the rise of generative AI necessitates rethinking traditional assessment methods. With tools that can rapidly generate convincing text and other media, essays and exams are highly susceptible to academic dishonesty. However, I suggest assessment strategies like oral defenses, process-focused assignments, and interactive group work hold more integrity. Additionally, I propose reconsidering the primacy placed on summative grading over formative feedback. Removing or deemphasizing grades on early assignments could encourage intellectual risk-taking. Overall, rather than intensifying surveillance, I argue educators should seize this moment to make assessment more dynamic, dialogic and better aligned with course objectives.

Leveraging AI to Enhance Learning

While acknowledging the risks, I also see generative AI as holding great potential for enhancing my teaching and student learning. I demonstrated how these tools can aid personalized learning via customized study materials and tutoring interactions. They can also streamline time-consuming academic tasks like note-taking, literature reviews and citation formatting. Further, I showed how AI could facilitate more personalized feedback for students at scale. Technologies cannot wholly replace human judgment and mentoring relationships, but they can expand educator capacities. However, to gain the full benefits, both educators and students will need guidance on judiciously leveraging these powerful tools.

Problematizing Generative AI and Bias

One major concern I surface is how biases embedded in training data get reproduced in generative AI outputs. For instance, visual AI tools typically depict professors as predominantly white males. However, I note programmers are already working to address this issue by introducing checks against bias and nudging AIs to produce more diverse output. While acknowledging these efforts, I also caution about potential superficiality if underlying data sources and assumptions go unexamined. Pushing back on biases in AI requires grappling with systemic inequities still present in society and institutions. There are no quick technical fixes for complex socio-historical forces, but civil rights and ethics must be central considerations moving forward.

Navigating Uncharted Waters

In conclusion, I characterize this as a watershed moment full of possibility, but also peril. Preparing myself and fellow educators and students to navigate this terrain will require open, flexible mindsets and reexamining some core assumptions. Knee-jerk reactions could compound risks, while inaction leaves us vulnerable. Addressing challenges presented by AI demands engaging ethical perspectives, but also pedagogical creativity. By orienting toward learning processes over products and developing more robust assessments, educators can cultivate academic integrity and the all-important critical thinking skills needed in an AI-driven world. This will necessitate experimentation, ongoing dialogue and reconsidering conventional practices. While daunting, I ultimately express confidence that with care, wisdom and humanity, educators can guide students through the promise and complexity of this new frontier.

Just when you think you’re getting a handle on things…

So this week’s big announcement was the (still being rolled out) ability to create custom GPTs. Just as I was getting to grips with doing this in the OpenAI playground, it’s now completely WYSIWYG (for many) from within ChatGPT which has had a 8 bit to 16 bit type upgrade in graphics to boot. As much as I want to encourage use of Bing Chat for a bunch of institutional reasons, I am yet again pulled back to ChatGPT with the promise of custom GPTs (do we have to call them that?) After a few false starts yesterday, due to issues of some sort with the roll out I imagine, today it has been seamless and smooth. I learnt quickly that you can get real precision from configuring the instructions. For example, I have given mine the specific instruction to share link X in instance Y and Link A in instance B. To create the foundation I have combined links with uploaded documents and so far my outputs have been pretty good. I think I will need longer and much more precise instructions as the responses do veer to the general still a little too much but it is feeding from my foundation well. Here’s how it looks in the creation screen:

It comes with a testing space adjacent to the creation window and options to share:

And this is the screen you get if you access the link (but recipients themselves must have the new creation ability to access custom bots):

And finally the chatbot window is familair and, as can been seen focussed on my data:

I actually think this will re-revolutionise how we think about large language models in particular and will ultimately impact workflows for professional service and academic staff as well as students in equal measure.

The ChatGPT numbers are staggering: 100 million weekly users and 2 million developers. I wish I had the time and skills to be included in the developer stat but what is notable is that even someone like me -the compulsive fiddler but with very limited tech skills – can use the ‘playground’ to create a personal assistant bot. Using GPT 4 the diffefrence between outputs here and from within, say, chatgpt or any other large language models, is that I define the instruction (in this case focssing on sources to refer responses to and nature of the role) and, crucially, use uploaded documents to become the foundation information. The post below was generated using a prompt that asked for a summary of key information from across our guidance documentation on Gen AI at King’s and then improved because the first output included non descriptive hyperlinks. I hear and have seen that the system is struggling to keep up with interest but it’s well worth having a look I think because in no time Open AI and/ or smart developers will create user friendly wrappers that will enable anyone to have their own pre-trained bots to support whatever it is they are working on. This I think will change the way we think about LLMs relatively quickly.

Sample output ( I have tweaked it slightly to ensure it is factually accurate but have left the tone and style as is just for illustration):

King’s Generative AI info

In the realm of higher education, generative AI stands as a groundbreaking technology that’s redefining the scope of learning and teaching. King’s College London remains a beacon of innovation by methodically embracing the capabilities of AI, while also tactfully navigating the intricacies it introduces to the academic environment. We have devised a comprehensive framework that facilitates informed engagement with generative AI across the university community.

Navigating the AI Landscape in Higher Education

To equip our community with a nuanced understanding of generative AI’s role in academia, we have developed an insightful short course titled Generative AI in Higher Education, available on FutureLearn. This course invites participants to explore the multifaceted ways in which AI can enrich academic disciplines and paves the way for meaningful discussions about the evolution of educational practices.

Tailored Guidance for All Stakeholders

Our dedication to fostering a responsible AI-integrated culture is reflected in the guidance documents created specifically for each stakeholder within King’s College London:

For programme and module leaders, it’s essential to consider whether guidance on the responsible use of generative AI has been articulated to students. As a cornerstone of our initiative, we have drafted a detailed guide for program and module leaders to ensure transparency in the permissible applications of AI.

Lecturers and GTAs find themselves equipped with recommendations designed to seamlessly integrate AI into their instructional and evaluative methodologies.

Students’ guidance addresses the pivotal aspects of generative AI usage within their academic journey while maintaining academic integrity standards. An integral part of this guidance is an encouragement to engage with tutors on the appropriate use of AI in particular assignments.

Policy Enforcements and Ethical Considerations

Information on detection and acknowledgment of AI-generated content has been published, underscoring the significance of integrity in academic pursuits.

Conceptual Clarity through Definitions

The university community can access a comprehensive lexicon and context for generative AI in our context and definitions document, poised to enhance understanding and conversation about this emergent technology.

Revolutionising Assessment

With generative AI reshaping potential assessment methods, King’s College London embraces new approaches to assessment, dedicated to cultivating profound learning experiences and reflective critical thinking among students.

Practical Insights and Knowledge Sharing

Martin Compton shares his unique perspective and inventive tactics for integrating generative AI into academic settings through his personal blog, a source of inspiration and practical advice.

Conclusion

The proactive stance of King’s College London in the sphere of generative AI serves as a testament to our commitment to excellence. By providing clear and structured guidance that caters to every role within the institution, we stride forward into the educational future armed with foresight, creativity, and an unwavering commitment to academic integrity.

I am working my way through the langauges in HeyGen. If you speak either language let me know whether the translation works please!

A really convenient illustration of how biases in training data find their way into generative AI outputs for me these last few months has been to show images of ‘a university professor from….’ as generated by Midjourney. The first example below is exactly that and I will use it today when talking with colleagues in the history department: ‘A history professor at King’s College London’. But, updates and new releases appear at first glance to be tackling the issue head on. The second set of images is exactly the same prompt generated via Dall-e 3 (via mobile version of ChatGPT -4). Positive me is delighted. Cynical me, though, is more than sceptical. The training data is unlikely to have changed so much as to result in this so what has? A shroud of diversity masking realities of the foundation that sit beneath through clever algorithmic tweaking. The system level technique is a deliberate effort to reflect more accurately the diversity of the World where prompts do not specify gender or ethnicity. This may well contribute a significant challenge to the bias issue but we shouldn’t kid ourselves that it has been resolved.

I have used the image below a few times internally to summarise the various strands of GenAI activity from a King’s Academy/ central College perspective. The stuff that’s happening in faculties is huge too but appears here somewhat inadequately as the top bubble on the right. The other two bubbles represent how we are contributing at sector level (such as the framework for responsible use) as well as drawing on and being informed by the great work at Jisc and how we have centred the Russell Group principles on AI as well as close working with Microsoft as innovations and integrations are rolled out. The Staff Guidance on GenAI is published as is Student Guidance, which are represented on the left as two main elements of our approach that we are badging as the King’s AI in education Laboratory (KAIeLAB). It also references the PAIR Framework, The FREE short course and the College Teaching Fund (internal only) which this year is AI focussed and all funded projects will necessarily have a student engagement element or co-leadership.

In this personal, exemplified account from my colleague Amy Aisha Brown, a Technology Enhanced Learning Manager at KCL, we can see a worked example of how Amy uses ChatGPT. Amy describes herself in the video as a neurodivergent member of staff and uses personal experience of utilising ChatGPT for assistance in writing a succinct bio for an online learning platform. Despite the apparent simplicity of the task, Amy illustrates how generative AI significantly eased her process by aiding in initiating the task, organising ideas, and ensuring language accuracy. Through this, Amy demonstrates the potential of AI as an invaluable assistant in alleviating common challenges faced. For anyone who’s interested, you can explore Amy’s chat via OpenAI

Resharing via my blog this video I made as a contrbution to discussions we were having in my old job (feels like eons ago) under the banner of ‘freedom to learn’. I’m sharing again because we will soon be sharing a call for contributions to the ‘Freedom to Learn’ conference – save the date 5/4/24 at King’s College London – where will be exploring the following themes:

Themes: 

1. Rekindling a joy of learning: Was there ever a ‘golden age’ where learning for its own sake provided sufficient value? What role does/ could/ should ‘joy’ play in a higher education? Why is there an apparent mental health crisis amongst undergraduate students? How can we innovate for joyful learning (and teaching)?
2. Decentring grades: Realising ungrading possibilities from the micro (one class) to the meso (whole modules) through to the macro (entire programmes or even institutions). Why is there so much resistance? What are the barriers to change and how might they be overcome? To what extent can change happen given the current status quo? Do we need a grading revolution or how might we chip away?

3. Caring and compassionate pedagogies: Is content still king (or queen)? How far have we realised an endeavour to weave care and compassion into our learning designs, teaching and assessments? What else could we do? Where are the pitfalls?
4. Myth Busting the modern academy: “We’ve always done it that way!” “We’re not allowed to change!” “Employers want….” “PSRBs want….” “ Students want…” “A degree is all about employability” A lot of what we hear when discussing change is responded to with arguments like this.Why? Is tradition a strong reason to stick with convention? Are the best pedagogies those that favour economies of scale? How far do structures really impede innovation and transformation? What are you doing? What would you like to do?