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Towards Responsible Development of Generative AI for Education: An Evaluation-Driven Approach
Irina Jurenka, et al., 2024/05/16


Donald Clark has already gotten a couple posts out of Google's EdAI announcements. This paper (86 page PDF) presents Google's efforts to "translate high level principles from learning science into a pragmatic set of seven diverse educational benchmarks" and to "develop a new set of fine-tuning datasets to improve the pedagogical capabilities of Gemini." To develop, in other words, a generative AI tutor. It deserves a careful reading, and your impressions of the 35 page main text and 50 pages of supplementary material may vary from mine. While the paper is very up-to-date with respect to AI, it reveals (to me) a dated and psychology-heavy understanding of 'learning science' that seems limited to work on intelligent tutoring systems - but you can view their 'Pedagogy rubrics' in section 4.3.1. Saying, "the gen AI models that power most of the latest EdTech systems are not explicitly optimised for pedagogy" the authors state that they "focus on conversational tutoring because we believe that it is one of the most impactful and general use cases." This forms the basis for their LearnLM-Tutor, introduced in this paper. There's a ton of information in this paper, but a lot that is not said - the model was "trained on an offline dataset," whatever that means (they say they tried several), and no stats are reported.

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Human Curiosity in the Age of AI
Anne-Laure Le Cunff, Ness Labs, 2024/05/16


This is another effort to find some human skill or capacity that is at least different, if not better, than AI. "There are compelling reasons why human curiosity is needed more than ever in the age of AI," writes Anne-Laure Le Cunff, "and they stem from the fundamental differences between human and AI curiosity." The post offers a framework that identifies three key aspects of curiosity - processing, perspective, purpose - and tries to show how different they are for humans as compared to AI (not that people would really think of AI as 'curious' in the first place). I don't think it works because the descriptions are too surface-level. How do we know 'hunches and instincts to produce serendipitous discoveries' are actually different from 'processes vast amounts of data to uncover patterns'? The same mechanism could produce both results.

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MMuFIN: A Framework for Automating Multimodal Feedback Generation using Generative Artificial Intelligence
Jionghao Lin, Eason Chen, Ashish Gurung, Kenneth R. Koedinger, EdArXiv, 2024/05/16


This preprint is tantalizing but far too short for what the title promises. Basically, the idea is to use generative AI to automate the process of giving multi-modal feedback on assignments. The instructor works with the AI, which may offer correction (for example, recommending the instructor focus on effort rather than outcome). And what's key is the idea of offering feedback in multiple modes, such as 'talking heads', rather than just text. I can't comment on the implementation because we don't really get anything in the way of details (this paper is a classic example of why we shouldn't simply count publications, because things like this can be pumped out in a day; we should actually consider what the publications contribute to the literature).

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We publish six to eight or so short posts every weekday linking to the best, most interesting and most important pieces of content in the field. Read more about what we cover. We also list papers and articles by Stephen Downes and his presentations from around the world.

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