This post is a AI/ me hybrid summary of the transcript of a conversation I had with Prof Oz Acar as part of the AI conversations series at KCL. This morning I found that my Copilot window now allows me to upload attachments (now disabled again! 30/4/24) but the output with the same prompt was poor by comparison to Claude or my ‘writemystyle’ custom GOT unfortunately (for now and at first attempt). I have made some edits to the post for clarity and to remove some of the wilder excesses of ‘AI cringe’.

“The beauty of PAIR is its flexibility,” Oz explained. “Educators can customise each component based on learning objectives, student cohorts, and assignments.” An instructor could opt for closed problem statements tailored to specific lessons, or challenge students to formulate their own open-ended inquiries. Guidelines may restrict AI tool choices, or enable students more autonomy to explore the ever-expanding AI ecosystem. That oversight and guidance needs to come from an informed position of course.

Crucially, by emphasising skills like problem formulation, iterative experimentation, critical evaluation, and self-reflection, PAIR aligns with long-established pedagogical models proven to deepen understanding, such as inquiry-based and active learning. “PAIR is really skill-centric, not tool-centric,” Oz clarified. “It develops capabilities that will be invaluable for working with any AI system, now or in the future.”

The early results from over a dozen King’s modules across disciplines like business, marketing, and arts have piloted PAIR have been overwhelmingly positive. Students have reported marked improvements in their AI literacy – confidence in understanding these technologies’ current capabilities, limitations, and ethical implications. “Over 90% felt their skills in areas like evaluating outputs, recognising bias, and grasping AI’s broader impact had significantly increased,” Oz shared.

While valid concerns around academic integrity have catalysed polarising debates, with some advocating outright bans and restrictive detection measures, Oz makes a nuanced case for an open approach centred on responsible AI adoption. “If we prohibit generative AI for assignments, the stellar students will follow the rules while others will use it covertly,” he argued. “Since even expert linguists struggle to detect AI-written text reliably (especially when it has been manipulated rather than simply churned from a single shot prompt), those circumventing the rules gain an unfair advantage.”

Instead, Oz advocates assuming AI usage as an integrated part of the learning process, creating an equitable playing field primed for recalibrating expectations and assessment criteria. “There’s less motivation to cheat if we allow appropriate AI involvement,” he explained. “We can redefine what constitutes an exceptional essay or report in an AI-augmented age.”

This stance aligns with PAIR’s human-centric philosophy of ensuring students remain firmly in the driver’s seat, leveraging AI as an enabling co-pilot to materialise and enrich their own ideas and outputs. “Throughout the PAIR process, we have mechanisms like reflective reports that reinforce students’ ownership and agency … The AI’s role is as an assistive partner, not an autonomous solution.”

Looking ahead, Oz is energised by generative AI’s potential to tackle substantial challenges plaguing education systems globally – from expanding equitable access to quality learning resources, to easing overstretched educators’ burnout through intelligent process optimisation and tailored student support. “We could make education infinitely better by leveraging these technologies thoughtfully…Imagine having the world’s most patient, accessible digital teaching assistants to achieve our pedagogical goals.”

However, Oz also acknowledges legitimate worries about the perils of inaction or institutional inertia. “My biggest concern is that we keep talking endlessly about what could go wrong, paralysed by committee after committee, while failing to prepare the next generation for their AI-infused reality,” he cautioned. Without proactive engagement, Oz fears a bifurcated future where students are either obliviously clueless about AI’s disruptive scope, or conversely, become overly dependent on it without cultivating essential critical thinking abilities.

Another risk for Oz is generative AI’s potential to propel misinformation and personalised manipulation campaigns to unprecedented scales. “We’re heading into major election cycles soon, and I’m deeply worried about deepfakes fuelling conspiracy theories and political interference,” he revealed. “But even more insidious is AI’s ability to produce highly persuasive, psychologically targeted disinformation tailored to each individual’s profile and vulnerabilities.”

Despite these significant hazards, Oz remains optimistic that responsible frameworks like PAIR can steer education towards effectively harnessing generative AI’s positive transformations while mitigating risks.

PAIR Framework- Further information

Previous conversation with Dan Hunter

Previous conversation with Mandeep Gill Sagoo

Generative AI in HE- self study short course

An additional point to note: The recording is of course a conversation between two humans (Oz and Martin) and is unscripted. The Q&A towards the end of the recording was faciliated by a third human (Sanjana). I then compared four AI transcription tools: Kaltura, Clipchamp, Stream and Youtube. Kaltura estimated 78% accuracy, Clipchamp crashed twice, Stream was (in my estimation) around 90-95% accurate but the editing/ download process is less convenient when compared to YouTube in my view so the final transcript is the one initially auto-generated in in YouTube, ChatGPT punctuated then re-edited for accuracy in YouTube. Whilst accuracy has improved noticeably in the last few years the faff is still there. The video itself is hosted in Kaltura.

AI Law

March 20, 2024March 21, 2024 heducationistLeave a comment

Watch the full video here

In the second AI conversation of the King’s Academy ‘Interfaculty Insights’ series, Professor Dan Hunter, Executive Dean of the Dickson Poon School of Law, shared his multifaceted engagement with artificial intelligence (AI). Prof Hunter discussed the transformative potential of AI, particularly generative AI, in legal education, practice, and beyond. With a long history in the field of AI and law, he offered a unique perspective on the challenges and opportunities presented by this rapidly evolving technology. To say he is firmly in the enthusiast camp, is probably an understatement.

From his vantage point, Prof Hunter presents the following key ideas:

AI tools (especially LLMs) are already demonstrating significant productivity gains for professionals and students alike but it is often more about the ways they can do ‘scut work’. Workers and students become more efficient and improve work quality when using these models. For those with lower skill levels the improvement is even more pronounced.
While cognitive offloading to AI models raises concerns about losing specific skills (examples of long division or logarithms were mentioned), Prof Hunter argued that we must adapt to this new reality. The “cat is out of the bag” so our responsibility lies in identifying and preserving foundational skills while embracing the benefits of AI.
Assessment methods in legal education (and by implication across disciplines) must evolve to accommodate AI capabilities. Traditional essay writing can be easily replicated by language models, necessitating more complex and time-intensive assessment approaches. Prof Hunter advocates for supporting the development of prompt engineering skills and requiring students to use AI models while reflecting on the process.
The legal profession will undergo a significant shakeup, with early adopters thriving and those resistant to change struggling. Routine tasks will be automated obligating lawyers to move up the value chain and offer higher-value services. This disruption may lead to the need for retraining.
AI models can help address unmet legal demand by making legal services more affordable and accessible. However, this will require systematic changes in how law is taught and practiced, with a greater emphasis on leveraging AI’s capabilities.
In the short term, we tend to overestimate the impact of technological innovations, while underestimating their long-term effects. Just as the internet transformed our lives over decades, the full impact of generative AI may take time to unfold, but it will undoubtedly be transformative.
Educators must carefully consider when cognitive offloading to AI is appropriate and when it is necessary for students to engage in the learning process without AI assistance. Finding the right balance is crucial for effective pedagogy in the AI era.
Professional services staff can benefit from AI by identifying repetitive, language-based tasks that can be offloaded to language models. However, proper training on responsible AI use, data privacy, and information security is essential to avoid potential pitfalls.
While AI models can aid in brainstorming, generating persuasive prose, and creating analogies, they currently lack the ability for critical thinking, planning, and execution. Humans must retain these higher-order skills, which cannot yet be outsourced to AI.
Embracing AI in legal education and practice is not just about adopting the technology but also about fostering a mindset of change and continuous adaptation. As Prof Hunter notes, “If large language models were a drug, everyone would be prescribed them.” *

The first in the series was Dr Mandeep Gill Sagoo

* First draft of this summary generated from meeting transcript via Claude

Navigating the Path of Innovation: Dr. Mandeep Gill Sagoo’s Journey in AI-Enhanced Education

March 8, 2024March 8, 2024 heducationist1 Comment

Dr. Mandeep Gill Sagoo, a Senior Lecturer in Anatomy at King’s College London, is actively engaged in leveraging artificial intelligence (AI) to enhance education and research. Her work with AI is concentrated on three primary projects that integrate AI to address diverse challenges in the academic and clinical settings. The following summary (and title and image, with a few tweaks from me) was synthesised and generated in ChatGPT using the transcript of a fireside chat with Martin Compton from King’s Academy. The whole conversation can be listened to here.

*AI generated image of a path winding through trees in sunlight and shadow*

Animated Videos on Cultural Competency and Microaggression: Dr. Sagoo has led a cross-faculty project aimed at creating animated, thought-provoking videos that address microaggressions in clinical and academic environments. This initiative, funded by the race equity and inclusive education fund, involved collaboration with students from various faculties. The videos, designed using AI for imagery and backdrops, serve as educational tools to raise awareness about unconscious bias and microaggression. They are intended for staff and student training at King’s College London and have been utilised in international collaborations. Outputs will be disseminated later in the year.
AI-Powered Question Generator and Progress Tracker: Co-leading with a second-year medical student and working across faculties with a number of others, Dr. Sagoo received a college teaching fund award to develop this project, which is focused on creating an AI system that generates single best answer questions for preclinical students. The system allows students to upload their notes, and the AI generates questions, tracks their progress, and monitors the quality of the questions. This project aims to refine ChatGPT to tailor it for educational purposes, ensuring the questions are relevant and of high quality.
Generating Marking Rubrics from Marking Schemes: Dr. Sagoo has explored the use of AI to transform marking schemes into detailed marking rubrics. This project emerged from a workshop and aims to simplify the creation of rubrics, which are essential for clear, consistent, and fair assessment. By inputting existing marking schemes into an AI system, she has been able to generate comprehensive rubrics that delineate the levels of performance expected from students. This project not only streamlines the assessment process but also enhances the clarity and effectiveness of feedback provided to students.

Dr. Sagoo’s work exemplifies a proactive approach to incorporating AI in education, demonstrating its potential to foster innovation, enhance learning, and streamline administrative processes. Her projects are characterised by a strong emphasis on collaboration, both with students and colleagues, reflecting a commitment to co-creation and the sharing of expertise in the pursuit of educational excellence.

Contact Mandeep

College Teaching Fund: AI Projects- A review of the review by Chris Ince

February 23, 2024 heducationist1 Comment

On Wednesday I attended the mid-point event of the KCL College Teaching Fund projects – each group has been awarded some funding (up to £10,000, though some came in with far smaller budgets) to do more than speculate on the possibility of using AI within their discipline and teaching, but carry out a research project around design and implementation.

Each team had one slide and three minutes to give updates on their progress so far, with Martin acting as compere and facilitator. I started to take notes so that I could possibly share ideas with the faculty that I support (and part-way through thought that I perhaps should have recorded the session and used an AI to summarise each project), but it was fascinating to see links between projects in completely different fields. Some connections and thoughts before each project’s progress so far:

The work involving students was carried out in many ways, but pleasingly many projects were presented by student researchers, who had either been part of the initial project bid or who had been employed using CTF funds. Even if just considering being surveyed and trialled, students are at all levels through this work, as they should be.
Several projects opened with scoping existing student use of gAI in their academic lives and work. This has to be taken with a pinch of salt, as it requires an element of honesty, but King’s has been clear that gAI is not prohibited so long as it is acknowledged (and allowed at a local level). What is interesting is that scoping consistently found that students did not seem to be using gAI as much as one might think (about a third); however their use has been growing throughout projects and the academic year as they are taught how to use it.
That being said, several projects identify how students are sceptical of the usefulness of gAI to them and in some that scepticism grows through the project. In some ways this is quite pleasing, as they begin to see gAI not as a panacea, but as a tool. They’re identifying what it can and can’t do, and where it is and isn’t useful to them. We’re teaching about something (or facilitating), and they’re learning.
Training AIs and ChatBots to assist in specific and complex tasks crops up in a number of projects, and they’re trialling some very different methods for this. Some are external, some are developed and then shared with students, and some give students what they need to train them themselves. Evidence that there are so many approaches, and exactly why this kind of networking is useful.
There’s frequently a heavily patronising perception sometimes that young people know more about a technology that older people. It’s always more complex than that, but the involvement of students in CTF projects has fostered some sharing of knowledge, as academic staff have seen what students can do with gAI. However, it’s been clear that the converse is also true, and that ‘we’ not only need to teach them but there is a desire for us to. This is particularly notable when we consider equality of access and unfair advantages, and two projects highlight this when they noted students from China had lower levels of familiarity with AI.

Project Title	Lead	Thoughts
How do students perceive the use of genAI for providing feedback	Timothy Pullen	A project from Biochemistry that’s focused on coding, specifically AI tools giving useful feedback on coding. Some GTAs have developed some short coding exercises that have trialled with students (they get embedded into Moodle and the AI provides student feedback). This has implications in time saved on the administration of feedback of this kind, but Tim suggests seems that there are limits to what customised bots can do within this “significantly” – I need to find out more, and am intrigued around the student perception of this: are there some situations where students would rather have a real person look at their work and offer help?
AI-Powered Single Best Answer (SBA) Automatic Question Generation & Enhanced Pre-Clinical Student Progress Tracking	Isaac Ng (student) Mandeep Sagoo	Isaac, a medical student, presents, and it’s interesting that there’s quite a clear throughline to producing something that could have commercial prospects further down the line – there’s a name and logo! An AI has been ‘trained’ with resources and question styles that act as the baseline; students can then upload their own notes and the AI uses these to produce questions in an SBA format that is consistent with the ‘real’ ones. There’s a clear focus on making sure that the AI won’t generate prompts from the material that it’s been given that aren’t factually wrong. A nice aspect is that all of the questions the AI generates are stored, and in March students are going to be able to vote on other student-AI questions. I’m intrigued about the element of students knowing what a good or bad question is, and do we need to ensure their notes are high-quality first?
Co-designing Encounters with AI in Education for Sustainable Development	Caitlin Bentley	Mira Vogel from King’s Academy is speaking on the team’s behalf – she leads on teaching sustainability in HE. The team have been working on the ‘right’ scaffolding and framing to find the most appropriate teaching within different areas/subjects/faculties – how to find the best routes. They have a broad range of members of staff involved, so have brought this element into the project itself. The first phase has been recursive – recruiting students across King’s to develop materials – Mira has a fun phrase about “eating one’s own dog food”. They’ve been identifying common ground across disciplines to find how future work should be organised at scale and wider to tackle ‘Wicked problems’ (I’m sure this is ‘pernicious or thorny problems’ and not surfer dude ‘wicked’, but I like the positivity in the thought of it being both).
Testing the Frontier – Generative AI in Legal Education and beyond	Anat Keller and Cari Hyde Vaamonde	Trying to bring critical thinking into student use of AI. There’s a Moodle page and online workshop (120 participants) and focus group day (12 students-staff) to consider this. How does/should/could the law regulate financial institutions? The project focused on the application of assessment marking criteria and typically identified three key areas of failure: structure, understanding, and a lack of in-depth knowledge (interestingly, probably replicating what many academics would report for most assessment failure). The aim wasn’t a pass, but to see if a distinction level essay could be produced. Students were a lot more critical than staff when assessing the essays. (side-note: students anthropomorphised the AI, often using terms like ‘them’ and ‘him’ rather than ‘it’). Students felt that while using AI at the initial ideas stage and creation may initially feel more appropriate than using it during the actual essay writing, this was where they lost the agency and creativity that you’d want/find in a distinction level student – perhaps this is the message to get across to students?
Exploring literature search and analysis through the lens of AI	Isabelle Miletich	Another project where the students on the research team get to present their work; it’s a highlight of the work, which also has a heavy co-creational aspect. Focused on Research Rabbit: a free AI platform that sorts and organises literature for literature reviews. Y2 focus groups have been used to inform material that is then used with Y1 dental students. There was a 95.7% response to Y1 survey. Resources were produced to form a toolbox for students, mainly guidance for the use of Research Rabbit. There was also a student produced video on how to use it for Y1s. The conclusion of the project will be narrated student presentations on how they used Research Rabbit.
Designing an AI-Driven Curriculum for Employable Business Students: Authentic Assessment and Generative AI	Chahna Gonsalves	Identifying use cases so that academics are better informed about when to put AI into their work. There have been a number employer-based interviews around how employers are using AI. Student participants are reviewing transcripts to match these to appropriate areas that academics might then slot them into the curriculum. An interesting aspect has been that students didn’t necessarily know/appreciate how much that King’s staff did behind the scenes on curriculum development work. It was also a surprise to the team how some employers were not as persuaded by the usefulness of AI (although many were embedding this within work). Some consideration of there being a difference in approach between early-adopters and those more reticent.
Assessment Innovation integrating Generative AI: Co-creating assessment activities with Undergraduate Students	Rebecca Upsher	Based in Psychology – students described how assessment to them means anxiety and stress or “just a means to get a degree” (probably some work around the latter one for sure). There’s a desire for creative and authentic assessment from all sides. Project started by identifying current student use of AI in and around assessment. One focus group (A learning and assessment investigation. Clarity of existing AI guidance. Suggestions for improvements) and one workshop (students more actively giving suggestions about summative AI suggestions to staff). Focus on inclusive and authentic assessment, being mindful of neurodiverse students and the group have been working with the neurodiverse society. Research students have been carrying out the literature review, prepared recruitment materials for groups, and mapped assessment types used in the department. Preliminary interest that has been a common thread was a desire for assessments to be designed with students, and a shift in power dynamics – interesting is that AI projects like this are fostering these sorts of co-design work that could have taken place before AI, but didn’t necessarily – academic staff are now valuing what students know and can do with AI (particularly if they know more than we do).
Improving exam questions to decrease the impact of Large Language Models	Victor Turcanu	A medicine-based project. Alignment with authentic professional tasks, that allow students to demonstrate their understanding, critical and innovative thinking, can students use LLMs to enhance their creativity and wider conceptual reach? The project is using 300 anonymous exam scripts to compare with ChatGPT answers. More specifically it’s about asking students their opinion in a question that doesn’t have an answer (a novel question embedded within an area of research around allergies – can students design a study to investigate something that doesn’t have a known solution: talk about the possibilities, or what they think would be a line of approach to research an answer). LLMs may be able to utilise work that has been published, but cannot draw on what hasn’t been published or isn’t yet understood. While the project was about students using LLMs, there’s also an angle here that it’s a way of an assessment where an AI can’t help as much.
Exploring Generative AI in Essay Writing and Marking: A study on Students’ and Educators’ Perceptions, Trust Dynamics, and Inclusivity	Margherita de Candia	Political science. Working with Saul Jones (an expert on assessment), they’ve also considered making an essay ‘AI proof’. They’re using the PAIR framework developed at King’s and have designed an assessment using the framework to make a brief they think is AI proof but still allows students to use AI tools. Workshops with students where they write an essay using AI will then be used to refine the assignment brief following a marking phase. If it works they want to disseminate the AI-proof brief for essays to colleagues across the social science faculties, however they are running sessions to investigate student perceptions, particularly around improvements to inclusivity in using AI. An interesting element here is what we consider to be ‘AI proof’, but also that students will be asked for thoughts on feedback for their essays when half will have been generated by an AI.
Student attitudes towards the use of Generative AI in a Foundation Level English for Academic Purposes course and the impact of in-class interventions on these attitudes	James Ackroyd	Action research – King’s Foundations within the team working on English for Academic purposes. Two surveys through the year and a focus group, specific interventions in class on use of AI. Another survey to follow. 2/3 of students initially said that they didn’t use AI at the start of the course (40% of students from China where AI is less commonly used due to access restrictions). But half-way through the course 2/3 said that they did. Is this King’s demystifying things? Student belief in what AI could do reduced during the course of the course. Faith in the micro-skills required for essay writing increased. Lots of fascinating threads of AI literacy and perceptions of it have come out of this so far.
Enhancing gAI literacy: an online seminar series to explore generative AI in education, research and employment.	Brenda Williams	Online seminar series on the use of AI (because students asked for them online, but there also more than 2,000 students in the target group and it’s the best way to get reach. Consultation panel (10 each of staff/students/alumni) to design five sessions to be delivered in June. Students have been informed about the course and a pre-survey to find out about use of AI by participants (and post-) has been prepared. This project in particular has a high mix of staff from multiple areas around King’s and highlights that there is more at play within AI than just working with AI in teaching settings.
Supporting students to use AI ethically and effectively in academic writing	Ursula Wingate	Preliminary scoping of student use of AI. Focus on fairness about a level playing field to upskill some students, and to reign in others. Recruited four student collaborators. Four focus groups (23 participants in January). All students reported having used Chat GPT (did this mean, in education, or in general?) and there is a wide range of free ones they use. Students are critical and sceptical of AI: they’ve noticed that it isn’t very reliable and have concerns about IP of others. They’re also concerned about not developing their own voice. Sessions designed to focus on some key aspects (cohesion, grammatical compliance, appropriateness of style, etc.) when using AI in academic writing are being planned.
Is this a good research question?	Iain Marshall, Kalwant Sidhu	Research topics for possible theses are being discussed at this half-way point of the academic year. Students are consulting chatbots (academics are quite busy, but also supervisors are usually only assigned when project titles and themes are decided – can students have space to go to beforehand for more detailed input?) The team have been utilising prompt engineering to create their own chatbot to help themselves and others (I think this is through the application of provided material, so students can input this and then follow with their own questions). This does involve students utilising quite a number of detailed scripts and coding, so this is supervised by a team – aimed that this will be supportive.
Evaluating an integrated approach to guide students’ use of generative AI in written assessments	Tania Alcantarilla &Karl Nightingale	There are 600 students in the 1st year of their Bioscience degrees. The team focused on perceptions and student use of AI. Design of a guidance podcast/session. Evaluation of the sessions and then of ultimate gAI use. There were 200 responses to student survey (which is pretty impressive). Lower use of gAI than expected (1/3 of students, but this increased after being at King’s – mainly by international students). It’s now that I’ve realised people ‘in the know’ are using gAI and not genAI as I have…am I out of touch?
AI-Based Automated Assessment Tools for Code Quality	Marcus Messer, Neil Brown	A project based around the assessment of student produced code. Here the team have focused on ‘Chain of thought prompting’ – a example is given to the LLM where there is a gobbet that includes the data, a show of reasoning steps, and the solution. Typically eight are used before the gAI is used to apply what it learned to a new question or other input. Here the team will use this to assess the code quality of programming assignments, including the readability, maintainability, and quality. Ultimately the grades and feedback will be compared with human-graded examples to judge the effectiveness of the tool.
Integrating ChatGPT-4 into teaching and assessment	Barbara Piotrowska	Public Policy in the Department of Political Economy – Broad goal was to get students excited and comfortable with using gAI. Some of the most hesitant students have been the most inventive in using it to learn new concepts. ChatGPT used as co-writer for an assessment – a policy brief (advocacy) – due next week. Teaching also a part (conversations with gAI on a topic can be used as an example of a learning task).
Generative AI for critical engagement with the literature	Jelena Dzakula	Digital Humanities – reading and marking essays where students engage with a small window of literature. Can gAI summarise what are considered difficult articles and chapters for students? Initial survey showed that students don’t use tools for this, they just give up. They mainly use gAI for brainstorming and planning, but not for helping their learning. Designing workshops/focus groups to turn gAI into a learning tool, mainly based around complex texts.
Adaptive learning support platform using GenAI and personalised feedback	Ievgeniia Kuzminykh	This project aims to embed AI, or at least use it as an integral part, of a programme, where it has access to a lot of information about progress, performance and participation. Moodle has proven quite difficult to work with for this project as the team wanted an AI that would analyse Moodle (to do this a cloned copy was needed, uploaded elsewhere so that it can be accessed externally by the AI). ChatGPT API not being free has also been an issue. So far, course content, quizzes, answers, were utilised and gAI asked to give feedback and generate a new quizzes. Paper design for a feedback system is being written and will be disseminated.
Evaluating the Reliability and Acceptability of AI Evaluation and Feedback of Medical School Course Work	Helen Oram	Couldn’t make the session- updates coming soon!

Fascinating stuff. For me, I want to consider how we can take this work from projects that have been funded by the CTF, and use them as ideas and models that departments, academics, and teaching staff can look to when considering teaching, curriculum and assessment in ways where they may not have funding.

Understanding and Integrating AI in Teaching

January 26, 2024January 26, 2024 heducationist2 Comments

This morning I discussed this topic with colleagues from King’s Natural, Mathematical and Engineering Sciences faculty. The session was recorded and a transcript is available to NMES colleagues but, as I pointed out in the session, AI is enabling ways of enhancing and/ or adding to the alternative ways of accessing the core information. By way of illustration the post below is generated from the transcript (after I sifted content to remove other speakers.) The only thing I edited was the words ‘in summary’ from the final paragraph.

TL:DR Autopodcast version

Slides can be seen here

***Screenshot from title slide showing AI generated image of a foot with only 4 toes and a quote purportedly from da Vinci which says: ‘The human foot is a masterpiece*** ***of engineering and a work of art’***

Understanding and Integrating AI in Teaching

Martin Compton’s contribution to the NMS Education Elevenses session revolved around the integration of AI into teaching, learning, and assessment. His perspective is deeply rooted in practical application and cautious understanding of these technologies, especially large language models like ChatGPT or Microsoft Co-pilot.

——-

My approach towards AI in education is multifaceted. I firmly believe we need a basic understanding of these technologies to avoid pitfalls. The misuse of AI can lead to serious consequences, as seen in instances like the professor in Texas who misused ChatGPT for student assessment or the lawyer in Australia who relied on fabricated legal precedents from ChatGPT. These examples underline the importance of understanding the capabilities and limitations of AI tools.

The Ethical and Practical Application of AI

The heart of my argument lies in engaging with AI responsibly. It’s not just about using AI tools but also understanding and teaching about them. Whether it’s informatics, chemistry, or any other discipline, integrating AI into the curriculum demands a balance between utilisation and ethical considerations. I advocate for a metacognitive approach, where we reflect on how we’re learning and interacting with AI. It’s crucial to encourage students to critically evaluate AI-generated content.

Examples of AI Integration in Education

I routinely use AI in various aspects of my work. For instance, AI-generated thumbnails for YouTube videos, AI transcription in Teams, upscaling transcripts using large language models, and even translations and video manipulation techniques that were beyond my skill set a year ago. These tools are not just about easing workflows but also about enhancing the educational experience.

One significant example I use is AI for creating flashcards. Using tools like Quizlet, combined with AI, I can quickly generate educational resources, which not only saves time but also introduces an interactive and engaging way for students to learn.

The Future of AI in Education

I believe that UK universities, and educational institutions worldwide, face a critical choice: either embrace AI as an integral component of academic pursuit or risk becoming obsolete. AI tools could become as ubiquitous as textbooks, and we need to prepare for this reality. It’s not about whether AI will lead us to a utopia or dystopia; it’s about engaging with the reality of AI as it exists today and its potential future impact on our students.

My stance on AI in education is one of cautious optimism. The potential benefits are immense, but so are the risks. We must tread carefully, ensuring that we use AI to enhance education without compromising on ethical standards or the quality of learning. Our responsibility lies in guiding students to use these tools ethically and responsibly, preparing them for a future where AI is an integral part of everyday life.

The key is to balance the use of AI with critical thinking and an understanding of its limitations. As educators, we are not just imparting knowledge but also shaping how the next generation interacts with and perceives technology. Therefore, it’s not just about teaching with AI but also teaching about AI, its potential, and its pitfalls.

The AI Literacy Frontier

January 23, 2024January 24, 2024 heducationistLeave a comment

Despite the best efforts of storm Isha I still managed to present at the 2024 National Conference on Gen AI in Ulster today (albeit remotely). Following on from my WONKHE post I focussed on the ‘how’ and ‘who’ of AI literacy in universities and proposed 10 (and a bit) principles.

When I was planning it I happened to have a chat with my son about AI translation getting us a step closer to Star Trek universal translators and how AI is akin to a journey …’where no-one has gone before’. Before I knew it my abstract was choc full of Star Trek refs and my presentation played fast and loose with the entire franchise.

The slides and my suggested principles are here

AI image depicting a scene on the bridge of a Star Trek-inspired starship, with a baby in the captain’s chair wearing a Starfleet-inspired uniform.

In the presentation I connected with Dr Kara Kennedy’s AI literacy Framework, exemplified a critical point with reference to Dr Sarah Eaton’s Tenets of Post-plagiarism and share some resources including my Custom GPT ‘Trek: The Captain’s Counsel’ and a really terrible AI generated song about my presentation.

Abstract

A year beyond our initial first contact with ChatGPT, the Russell Group has set a course with their principles on generative AI’s use in education, acting as an essential guide for the USS Academia. Foremost among these is the commitment to fostering AI literacy: an enterprise where universities pledge to equip students and staff for the journey ahead. This mission, however, navigates through sectors where perspectives on AI range from likely nemesis to potential prodigy.

Amidst the din of divergent voices, the necessity for critical, cohesive, and focused discourse in our scholarly collectives is paramount. In this talk Martin argues that we need to view AI and all associated opportunities and challenges as an undiscovered country where we have a much greater chance not only of survival in this strange new world but also of flourishing if we navigate it together. This challenge to the conventional knowledge hierarchies in higher education suggests genuine dialogue and collaboration are essential prerequisites to success.

Martin will chart the course he’s plotted at King’s College London, navigating through the nebula of complex AI narratives. He will share insights from a multifaceted strategy aimed at fostering AI understanding and literacy across the community of stakeholders in their endeavour to ensure the voyage is one of shared discovery.

13 ways you could integrate AI tools into teaching

January 17, 2024February 8, 2024 heducationist1 Comment

For a session I am facilitating with our Natural, Mathematical and Engineering Sciences faculty I have below pulled together a few ideas drawn from a ton of brilliant suggestions colleagues from across the sector have shared in person, at events or via social media. There’s a bit overlap but I am trying to address the often heard criticism that what’s missing from the guidance and theory and tools out there is some easily digestible, accessible and practically-focussed suggestions that focus on teaching rather than assessment and feedback. Here my first tuppenceworth:

1.AI ideator: Students write prompts to produce a given number of outputs (visual, text or code) to a design or problem brief. Groups select top 2-3 and critique in detail the viability of solutions. (AI as inspiration)

2. AI Case Studies: Students analyse real-world examples where AI has influenced various practices (e.g., medical diagnosis, finance, robotics) to develop contextual intelligence and critical evaluation skills. (AI as disciplinary content focus)

3. AI Case Study Creator: Students are given AI generated vignettes, micro case studies or scenarios related to a given topic and discuss responses/ solutions. (AI as content creator)

4. AI Chatbot Research: For foundational theoretical principles or contextual understanding, students interact with AI chatbots, document the conversation, and evaluate the experience, enhancing their research, problem-solving, and understanding of user experience. (AI as tool to further understanding of content)

5. AI Restructuring: Students are tasked with using AI tools to reformat content into different media accordsing to pre-defined principles. (AI for multi-media rreframing).

6. AI Promptathon: Students formulate prompts for AI to address significant questions in their discipline, critically evaluate the AI-generated responses, and reflect on the process, thereby improving their AI literacy and collaborative skills. (Critical AI literacy and disciplinary formative activity)

7. AI audit: Students use AI to generate short responses to open questions, critically assess the AI’s output, and then give a group presentation on their findings. Focus could be on accuracy and/ or clarity of outputs. (Critical AI literacy)

8. AI Solution Finder: Applicable post work placement or with case studies/ scenarios, students identify real-world challenges and propose AI-based solutions, honing their creativity, research skills, and professional confidence. (AI in context)

9. AI Think, Pair & Share: Students individually generate AI responses to a key challenge, then pair up to discuss and refine their prompts, improving their critical thinking, evaluation skills, and AI literacy. (AI as dialogic tool)

10. Analyse Data: Students work with open-source data sets to answer pressing questions in their discipline, thereby developing cultural intelligence, data literacy, and ethical understanding. (AI as analytical tool)

11. AI Quizmaster : Students design quiz questions and use AI to generate initial ideas, which they then revise and peer-review, fostering foundational knowledge, research skills, and metacognition. (AI as concept checking tool)

12. Chemistry / Physics or Maths Principle Exploration with AI Chatbot: Students engage with an AI chatbot to learn and understand a specific principle. The chatbot can explain concepts, answer queries, and provide examples. Students (with support of GTA/ near peer or academic tutor) compare the AI’s approach to their own process/ understanding. (AI chatbot tutor)

13. Coding Challenge- AI vs. Manual Code Comparison: Coding students create a short piece of code for a specific purpose and then compare their code to a pre-existing manually produced code for the same purpose. This comparison can include an analysis of efficiency, creativity, and effectiveness. (AI as point of comparison)

HEducationist

Martin Compton @ KCL on HE + education

Tag: education

The wizard of PAIR