Tag: artificial-intelligence

Breathless AI for EDU

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Microsoft EDU presentations at the BETT show were high energy and breathless and this video adopts the same tone. Being ancient myself I carry within me hard to shake cultural norms and, despite my love of so many things from across the pond, still blink nervously when confronted with ‘Wow, look at this people!’ approaches to sales- BETT is increasingly like this it has to be said. (side note: all the Twitter folk who shout ‘MOST PEOPLE ARE USING CHATGPT WRONG!’ get automatic hard passes from me every single time).

Anyway, this video is a summary of one of the presentations I watched and I watched it all then and recently watched the video summary too. There’s a lot to be sceptical about and a lot that wouldn’t leave me quite as breathlessly excited but there’s also a ton of things in here that are indicative of the direction MS products are going in the education space- particularly in relation to schools. The MS Teams for Education integrations suggest we may soon be talking again about the what and where of VLEs too. To be fair, the Copilot ‘side by side’ in MS Edge approach is something I don’t routinely do but it may nudge me towards using a browser other than Safari or Chrome finally (or maybe not!). The Copilot for Educators resource mentioned is very useful. The big deal towards the end is on school-focussed Teams embellishments but they are worth thinking about as they suggest likely trajectories for all sectors. Much of the reader and speaker AI support look like tools that would transfer to the HE context and the admin/ resource creation ideas will likely be popular too.

The presenter’s examples and own style really underline the American bias in the tool development and the way the reading and speaker coach tools will further homogenise accent and dialect. My daughter already says ‘gotten’ and ‘sidewalk’ and was delighted yesterday to find out a show she wants to see was ‘on Broadway’ until we explained that Broadway is in New York. The question for us in the ‘not America’ English speaking/ using world is how much loss to homogenisation will be perceived to be acceptable for assumed gains: actually a question you might ask about a lot of these tech. Predicted degrees of divergence in orthography and dialect leading to an inability to understand one another never manifested beyond some pretty well-known differences (though subtitles are a solid friend with some TV) so I think accent and tone variants are the most at risk.

Anyway, what I came here to say was I think it’s worth a watch (32 mins) or having on in the background when you’re doing something placid, calm and terribly British, like drinking tea, having a curry or watching football.

TL:DW? I used Gemini (whilst waving fist at Copilot) to produce a summary based on the transcript

Understanding and Integrating AI in Teaching

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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.

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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.

13 ways you could integrate AI tools into teaching

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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)

Custom GPTs

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There are two main audiences for custom GPTs built within the ChatGPT Pro insfrastucture. The first is anyone with a pro account. There are other tools that allow me to build custom GPTs with minimal skills that are open to wider audiences so I think it’ll be interesting to see whether OpenAI continue to leverage this feature to encourage new subscription purchases or whether it will open up to further stifle competitor development. In education the ‘custom bots for others’ potential is huge but, for now, I am realising how potentially valuable they might be for the audience I did not initially consider – me.

One that is already proving useful is ‘My thesis helper’ which I constructed to pull information only from my thesis (given that even the really obvious papers never materialsed I am wondering whether this might catalyse that!) It’s an opportunity to use as source material much larger documents than the copy/ paste tokens allow or even the relatively generous (and free) 100k tokens and document upload Claude AI permits. In particular, it facilitates much swifter searching within the document as well as opportunities for synthesising and summarising specific sections. Another is ‘Innovating in the Academy’ (try it yourself if you have a pro account) which uses two great sources of case studies from across King’s, collated and edited by my immediate colleagues in King’s Academy. The bot enables a more refined search as well as an opportunity to synthesise thinking.

Designed to be more outward facing is ‘Captain’s Counsel’. This I made to align with a ‘Star Trek’ extended (and undoubtedly excruciating) metaphor I’ll be using in a presentation for the forthcoming GenAI in Education conference in Ulster. Here I have uploaded some reference material but also opened it to the web. I have tried to tap into my own Star Trek enthusiasm whilst focussing on broader questions about teaching. The web-openness means it will happily respond to questions about many things under the broad scope I have identified though I have also identified some taboos. Most useful and interesting is the way it follows my instruction to address the issue with reference to Captain Kirk’s own experiences. 

Both the creation and use of customised bots enables different ways of perceiving and accessing existing information and it is in these functions broadly that LLMs and image generators as well as within customised bots are likely to establish a utility niche I think, especially for folk yet to dip their toes or whose perceptions are dominated by LLMs as free essay mills.