Tag: teaching

Navigating the AI Landscape in HE: Six Opinions

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Read my post below or listen to AI me read it. Have to say, I sound very well spoken in this video. To my ears doesn’t sound much like me. For those that know me: what do you think?

As we attempt to navigate uncharted (as well as expanding and changing) landscapes of artificial intelligence in higher education, it makes sense to reflect on our approaches and understanding. We’ve done ‘headless chicken’ mode; we’ve been in reactive mode. Maybe we can start to take control of the narratives; even if what is ahead of us is disrupting, fast-moving and fraught with tensions. Here are six perspectives from me that I believe will help us move beyond the hype and get on with the engagement that is increasingly pressing but, thus far, inconsistent at best.

1. AI means whatever people think it means

In educational circles, when we discuss AI, we’re primarily referring to generative tools like ChatGPT, DALL-E, or Copilot. While computer scientists might argue- with a ton of justification- this is a narrow definition, it’s the reality of how most educators and students understand and engage with AI. We mustn’t get bogged down in semantics; instead, we should focus on the practical implications of these tools in our teaching and learning environments whilst taking time to widen some of those definitions, especially when talking with students. Interrogating what we mean when we say ‘AI’ is a great starting point for these discussions in fact.

2. AI challenges our identities as educators

The rapid evolution of AI is forcing us to reconsider our roles as educators.  Whether you buy into the traditional framing of higher education this way or not, we’re no longer the sole gatekeepers of knowledge, dispensing wisdom from the lectern. However much we might want to advocate for notions of co-creation or discovery learning, the lecturer/ teacher as expert is a key component of many of our teacher professional identities.  Instead, we need to acknowledge that we’re all navigating this new landscape together – staff and students alike. This shift requires humility and a willingness to learn alongside our students. The alternatives: Fake it until you make it? Bury your head? Neither are viable or sustainable. Likewise, this is not something that is ‘someone else’s job’. HE is being menaced from many corners and workload is one of the many pressures- but I don’t see a beneficial path that does not necessitate engagement. If I’m right then something needs to give. Or be made less burdensome.

3. Engage, not embrace

I’m not really a hugger, tbh. My family? Yes. A cute puppy? Probably. Friends? Awkwardly at best. A disruptive tech? Of course not. While some advocate for ’embracing’ AI, I prefer the term ‘engage’. We needn’t love these technologies or accept them unquestioningly, but we do need to interact with them critically and thoughtfully. Rejection or outright banning is increasingly unsupportable, despite the many oft-cited issues. The sooner we at least entertain the possibilities that some of our assumptions about the nature of writing and what constitutes cheating and how we best judge achievement may need review the better.

4. AI-proofing is a fool’s errand

Attempts to create ‘AI-proof’ assessments or to reliably detect AI-generated content are likely to be futile. The pace of technological advancement means that any barriers we create will swiftly be overcome. Many have written on the unreliability and inherent biases of detection tools and the promotion of flawed proctoring and surveillance tools only deepens the trust divide between staff and students that is already strained to its limit.  Instead, we should focus on developing better, more authentic forms of assessment that prioritise critical thinking and application of knowledge. A lot of people have said this already, so we need to build a bank of practical, meaningful approaches, draw on the (extensive) existing scholarship and, in so doing, find ways to better share things that address some of the concerns that are not: ‘Eek, everyone do exams again!’

5. We need dedicated AI champions and leadership

To effectively integrate AI into our educational practices, we need people at all levels of our institutions who can take responsibility for guiding innovations in assessment and addressing colleagues’ questions. This requires significant time allocation and can’t be achieved through goodwill alone. Local level leadership and engagement (again with dedicated time and resource) is needed to complement central policy and guidance. This is especially true of multi-faculty institutions like my own. There’s only so much you can generalise. The problem of course is that whilst local agency is imperative, too many people do not yet have enough understanding to make fully informed decisions.  

6. Find a personal use for AI

To truly understand the potential and limitations of AI, it’s valuable to find ways to develop understanding with personal engagement – one way to do this is to incorporate it into your own workflows. Whether it’s using AI to summarise meeting or supervision notes, create thumbnails for videos, or transform lecture notes into coherent summaries, personal engagement with these tools can help demystify them and reveal practical benefits for yourself and for your students. My current focus is on how generative AI can open doors for neurodivergent students and those with disabilities or, in fact, any student marginalised by the structures and systems that are slow to change and privilege the few.

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

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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
  1. 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.
  2. 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.
  3. 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

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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 TitleLead Thoughts
How do students perceive the use of genAI for providing feedbackTimothy PullenA 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 TrackingIsaac Ng (student) Mandeep SagooIsaac, 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 DevelopmentCaitlin BentleyMira 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 beyondAnat Keller and Cari Hyde VaamondeTrying 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 AIIsabelle MiletichAnother 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 AIChahna GonsalvesIdentifying 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 StudentsRebecca UpsherBased 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 ModelsVictor TurcanuA 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 InclusivityMargherita de CandiaPolitical 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 attitudesJames AckroydAction 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 WilliamsOnline 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 writingUrsula WingatePreliminary 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 SidhuResearch 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 assessmentsTania Alcantarilla &Karl NightingaleThere 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 QualityMarcus Messer, Neil BrownA 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 assessmentBarbara PiotrowskaPublic 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 literatureJelena DzakulaDigital 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 feedbackIevgeniia KuzminykhThis 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 WorkHelen OramCouldn’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.