originally posted here: https://reflect.ucl.ac.uk/mcarena/2022/03/31/tbl/

I have been forced to confront a prejudice this week and I’m very glad I have because I have significantly changed my perspective on Team Based Learning (TBL) as a result. When I cook I rarely use a recipe: rough amounts and a ‘bit of this; bit of that’ get me results that wouldn’t win Bake Off but they do the job.  I’m a bit anti-authority I suppose and I might, on occasion, be seen as contrary given a tendency to take devil’s advocate positions.  As a teacher educator, and unlike many of my colleagues over the years, I tend to advocate a more flexible approach to planning, am most certainly not a stickler for detailed lesson plans and maintain a sceptisicm (that I think is healthy) about the affordances of learning outcomes and predictably aligned teaching. I think this is why I was put off TBL when I first read about it. Call something TBL and most people would imagine something loose, active, collaborative and dialogic. But TBL purists (and maybe this was another reason I was resistant) would holler: ‘Hang on! TBL is a clearly delineated thing! It has a clear structure and process and language of its own.’ However, after attending a very meta-level session run by my colleague, Dr Pete Fitch, this week I was embarrassed to realise how thoroughly I’d misunderstood its potential flexibility and adaptability as well as the potentials of different aspects I might be sceptical of in other contexts.

Established as a pedagogic approach in medical education in the US in the 1970s, it is now used widely across medical education globally as well as in many other disciplinary areas. In essence, it provides a seemingly rigid structure to a flipped approach that typically looks like this:

• Individual pre-work – reading, videos etc.
• Individual readiness assurance test (IRAT) – in class multi-choice text
• Team readiness assurance teast (TRAT) – same questions, discussed and agreed- points awarded according to how few errors are made getting to correct response
• Discussion and clarification (and challenge)- opportunities to argue, contest, seek clarification from tutor
• Application- opportunity to take core knowledge and apply it
• Peer evaluation

This video offers a really clear summary of the stages:

Aside from the rigid structure, my original resistance was rooted in the knowledge-focussed tests and how this would mean sessions started with silent, individual work. However, having been through the process myself (always a good idea before mud slinging!), I realised that this stage could achieve a number of goals as well as the ostensible self-check on understanding. It provides a framing point for students to measure understanding of materials read; it offers-completely anonymously- even to the tutor, an opportunity to guage understanding within a group; it provides an ipsative opportunity to measure progress week by week and acts additionally as a motivator to actually engage with the pre-session work (increasingly so as the learning culture is established). It turns a typically high stakes, high anxiety activity (individual test) into a much lower stakes one and provides a platform from which intial arguments can start at the TRAT stage. A further advantage therefore could be that it helps students formatively with their understanding of and approaches to multi-choice examinations in those programmes that utilise this summative assessment methodology.  In this session I changed my mind on three questions during the TRAT, two of which I was quietly (perhaps even smugly) confident I’d got right. A key part of the process is the ‘scratch to reveal if correct’ cards which Pete had re-imagined with some clever manipulation of Moodle questions. We discussed the importance of the visceral ‘scratching’ commitment in comparsion to a digital alternative and I do wonder if this is one of those things that will always work better analogue!

The cards are somewhat like those shown in this short video:

To move beyond knowledge development, it is clear the application stage is fundamental. Across all stages it was evident how much effort is needed in the design stage. Writing meaningful, level appropriate multi-choice questions is hard. Level-appropriate, authentic application activities are similarly challenging to design. But the payoffs can be great and, as Pete said in session, the design lasts more than a single iteration. I can see why TBL lends itself so well to medical education but this session did make me wish I was still running my own programme so I could test this formula in a higher ed or digital education context.

An example of how it works in the School of Medicine in Nanyang Technological University can be seen here:

The final (should have been obvious) thing spelt out was that the structure and approach can be manipulated. Despite appearances, TBL does enable a flexible approach. I imagine one-off and routine adaptations according to contextual need are commonplace.  I think if I were to design a TBL curriculum, I’d certainly want to collaborate on its design. This would in itself be a departure for me but preparing quality pre-session materials, writing good questions and working up appropriate application activites are all essential and all benefit from collaboration or, at least, a willing ‘sounding board’ colleague. 

This post and its slightly cheesy title (above) was generated using Claude and is based only on the transcript* from the recording of the Oxford Brookes webinar (part of the Talking Teaching across the globe series) I spoke at today on how we actually achieve that Russell Group committment:

Universities will support students and staff to become AI-literate

This is a ‘recast’ AI generated podcast of the article below- the emphases are not brilliant but I hope offers colleagues an idea of what can be done to supplement things like the webinar. Frankly, neither this post nor the recast summary would exist without the ability to produce it in minimal time. (The whole process from downloadingt he transcript to hitting ‘update’ to this post now has taken 19 minutes)

Introduction

We find ourselves in a complex moment as emerging generative AI both captivates and concerns academics. Powerful new tools hold promise yet prompt apprehension. How do we steer constructive conversations amidst clashing viewpoints and high stakes? Martin Compton offers insightful perspectives grounded in ethical priorities – perspectives that reframe AI literacy as a collective journey of discovery requiring diverse voices, embracing practical possibilities, and creating space for critical debate.

Multiple Voices Needed to Balance the Discussion

Martin emphasizes that no one person possesses definitive expertise in this nascent domain. Varied voices deserve air time, even those with “limited credentials.” Since AI intersects with so many fields and its societal ramifications span from climate impacts to employment shifts, cross-disciplinary dialogue matters deeply. We have much to learn from each other.

Further, the computer science sphere itself lacks internal concord on timelines and capabilities. Some hail rapid transformational change while others dismiss the possibility of huge impacts. These mixed messages understandably breed public confusion, sparking doomsday headlines alongside boundless optimism. Socratic humility may serve us well here – acknowledging the expanse of what we do not know.

Given such uncertainty, multiplicity of perspective becomes essential. We need humanities scholars probing ethical quandaries, social scientists weighing systemic biases, natural scientists modeling environmental tradeoffs, employers voicing hiring needs, students sharing studied hopes and fears. No singular authoritative stance exists. Martin rightly warns against perpetuating traditional classroom power dynamics that position instructors as all-knowing arbiters. Hierarchical positioning will not serve us in unmapped territory.

Practical Possibilities Over Limitations to Expand Understanding

Beyond balanced dialogue, Martin advises pivoting more conversations toward practical possibilities versus current limitations. Generative AI’s flaws are abundantly clear, including bias, inaccuracy, and authenticity concerns. These absolutely warrant continued attention, as does debate around academic integrity. But dwelling solely on weaknesses risks blinding us to potentially constructive use cases.

We owe it to students to explore how these technologies may assist real work in real fields, shaping their future employability. Are there accessibility gains for neurodiverse learners? Streamlined workflows for overwhelmed academics? Even those who condemn generative AI must grapple with its impending workplace uptake to best serve graduates. Beyond hypotheticals, where might AI tangibly supplement – not supplant – rich pedagogical environments if guided by ethical priorities?

Illustrating authentic applications can also demystify these tools for skeptical faculty and counteract media hyperbole around “robot grading essays.” When we broaden understanding of AI’s diversity beyond chatbots, we dispel myths. Asking, “how might this aid human creativity?” rather than “will this replace human jobs?” reveals unconsidered potentials.

Spaces for Critical Debate Across Campus

Finally, Martin asks the pivotal question of where open-ended debate will unfold on our campuses given diverse conflicting views. Even within single institutions, some departments welcome generative AI while others seek bans. For literacy efforts to prove lasting, they must transcend one-off workshops and invite ongoing dialogue around community priorities.

Martin offers models like King’s College London’s FutureLearn course allowing global participants to weigh complex issues like algorithmic bias. He spotlights the power of hackathons for convening multiple perspectives to spawn inventive projects. Funding student-faculty partnerships around AI applications grounds exploration in lived curriculum.

Constructing designated forums for airing ethical tensions matters deeply, given disparate departmental stances. We need space to hash out appropriate usage guides for our institutional contexts. No top-down policy prescription or unilateral corporate partnership will address the full scope of concerns. By mapping key campus constituents – from disability support offices to career centers to individual faculty across disciplines – we gain fuller understanding of the landscape before charting next wise steps.

Ultimately AI literacy lives in human connections – the degree to which we foreground multiplicity of voice, balance pragmatic possibility with diligent critique, and carve out shared venues for unpacking this technology on our own terms. The questions loom large, as does potential for substantiative harm. But committing to collective discovery widens possibilities for accountable innovation. We travel this emerging terrain together.

Event slides here

KCL GenAI in HE short course

my prompt: Isolate the comments from martin compton and using ONLY his ideas and contributions write an informal blog post for an academic audience that focusses on how we evolve AI literacy including his thoughts on what that means and how we should approach it. Use at least three subheadings

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.