Digital Pedagogy in the Humanities: Concepts, Models, and Experiments - curateteaching/digitalpedagogy

Helloooo! I thought I should begin with a post to introduce the site before I really get into the harder work of reading, generating ideas and sharing what I learn. This space is, firstly, a little…

🤖 AI shouldn’t think for us. It should think with us. From June 2 to 4, the Universitat Oberta de Catalunya hosted its first AI Week, a three‑day event exploring how generative artificial intelligenc...

Well I’ve realised now just how hard this is to do. I’ve tried and tried in my prototype video and it has some value but it’s way off in terms of the overall required quality. But I’ll show it in cl...

Description This course will help faculty, staff, and instructional technologists conceptualize, design, and explore approaches to teaching digital book history and digital publishing. The course will...

Good to be able to contribute to the AI Connect and Reflect: AI and Education Showcase (January 2026), hosted through the University of Chester AI SIG. My contribution was a short recorded lightning ...

Hello LinkedIn community, I have a question of any UK based HE educators. All my primary resources I provide slides, documents, video links, podcasts etc are produced and delivered obviously in Englis...

This primer provides an overview of the rapidly evolving field of generative artificial intelligence, specifically focusing on large language models like ChatGPT (OpenAI) and Bard (Google). Large language models have demonstrated unprecedented capabilities in responding to natural language prompts. The aim of this primer is to demystify the underlying theory and architecture of large language models, providing intuitive explanations for a broader audience. Learners seeking to gain insight into the technical underpinnings of large language models must sift through rapidly growing and fragmented literature on the topic. This primer brings all the main concepts into a single digestible document. Topics covered include text tokenization, vocabulary construction, token embedding, context embedding with attention mechanisms, artificial neural networks, and objective functions in model training. The primer also explores state-of-the-art methods in training large language models to generalize on specific applications and to align with human intentions. Finally, an introduction to the concept of prompt engineering highlights the importance of effective human-machine interaction through natural language in harnessing the full potential of artificial intelligence chatbots. This comprehensive yet accessible primer will benefit students and researchers seeking foundational knowledge and a deeper understanding of the inner workings of existing and emerging artificial intelligence models. The author hopes that the primer will encourage further responsible innovation and informed discussions about these increasingly powerful tools.

According to a recent survey by Anthology (2023), 60% of students in the US have used AI tools, with 10% reporting weekly use 38% using them monthly. Instead of fearing AI, we should actively explore ...

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