Another live Ed-Technical episode! In this short, Owen does a deep dive on AI and discourse analysis (the study of how meaning is constructed through language) with three experts. The conversation explores the intersection between AI, particularly Large Language Models (LLMs), and the study of discourse. This is a topical conversation as LLM capabilities continue to evolve. LLMs have mastered sentence level communication. However we know less about their ability to be useful over the course of a full conversation and complex and interactive processes (like learning) that require deeper appreciation of context. Featuring:Pani Kendeu: Professor at the University of Minnesota, researching learning, cognition, and technology, and a former elementary school teacher. Alyssa Wise: Professor of Technology and Education at Vanderbilt University, directing the Live Learning Innovation Incubator which bridges technology with real-world classroom challenges.Art Graesser: Professor at the University of Memphis, co-founder of the Institute for Intelligent Systems and the Society for Text and Discourse. Join us on social media: BOLD (@BOLD_insights), Libby Hills (@Libbylhhills) and Owen Henkel (@owen_henkel) Listen to all episodes of Ed-Technical here: https://bold.expert/ed-technical Subscribe to BOLD’s newsletter: https://bold.expert/newsletter Stay up to date with all the latest research on child development and learning: https://bold.expert Credits: Sarah Myles for production support; Josie Hills for graphic design

Everyone is talking about AI’s power to provide answers, but what about your lingering questions? What does the latest research actually tell us? Join Libby and Owen for this live session from SXSW EDU as they delve into the latest research to uncover where AI is truly adding value in the educational landscape — and where it falls short. They’re joined by two expert guests: Kristen DiCerbo from Khan Academy and Assistant Professor Peter Bergman from University of Texas at Austin and Learning Collider. The group discusses the most pressing open questions and key findings from the latest research.Join us on social media: BOLD (@BOLD_insights), Libby Hills (@Libbylhhills) and Owen Henkel (@owen_henkel) Listen to all episodes of Ed-Technical here: https://bold.expert/ed-technical Subscribe to BOLD’s newsletter: https://bold.expert/newsletter Stay up to date with all the latest research on child development and learning: https://bold.expert Credits: Sarah Myles for production support; Josie Hills for graphic design

In this episode, Owen and Libby chat with Chris Agnew about Stanford's new generative AI hub for education. Chris leads this initiative within Stanford's SCALE program, which aims to be a trusted source for education system leaders on what works in AI and learning.Chris walks us through their research repository of 181 papers examining AI's impact in K-12 education. He outlines their GenAI tools typology which breaks down AI applications into three categories: efficiency gains, improving student outcomes, and reimagining schooling. The conversation explores key research gaps, including how schools can productively engage with teachers' unions on AI adoption and understanding how students use AI tools for homework - the "elephant in the room" that keeps education leaders up at night.Before joining Stanford, Chris worked in non-traditional learning environments from wilderness education to apprenticeship programs. He shares both aspirational and practical visions for AI in education over the next five years - though sadly, none involve Owen's hoped-for cyborg centaur tutors (yet).Links:Stanford Accelerator for Learning SCALE InitiativeGenerative AI Research RepositoryJoin us on social media: BOLD (@BOLD_insights), Libby Hills (@Libbylhhills) and Owen Henkel (@owen_henkel) Listen to all episodes of Ed-Technical here: https://bold.expert/ed-technical Subscribe to BOLD’s newsletter: https://bold.expert/newsletter Stay up to date with all the latest research on child development and learning: https://bold.expert Credits: Sarah Myles for production support; Josie Hills for graphic design

Owen and Libby discuss a study revealing the power of Microsoft Copilot on student learning in Nigeria. In just six weeks, students reportedly achieved learning gains equivalent to almost two years. They delve into the implications of rapid learning claims and discuss how effect sizes can be misunderstood. The conversation also explores the cognitive limits of rapid knowledge acquisition, questioning the feasibility of such interventions and setting the stage for future educational research.

In this episode, Libby and Owen interview Mike Frank, Professor at Stanford University and leading expert in child development. This episode has a different angle to the others, as it is more about AI as a scientific instrument rather than as a tool for learning. Libby and Owen have a fascinating discussion with Mike about language acquisition and what we can learn about language learning from large language models. Mike explains some of the differences between how large language models develop an understanding of human language versus how babies do this. There are some big questions touched on here, including how much of the full human experience it’s possible to capture in data. Libby and Owen also make excellent use of Mike’s valuable time by asking for his expert view on why infants find unboxing videos - videos of other children opening gifts - so addictive. LinksMike Frank’s biography New York Times piece about Mike’s work An interview with Mike about his research Join us on social media: BOLD (@BOLD_insights), Libby Hills (@Libbylhhills) and Owen Henkel (@owen_henkel) Listen to all episodes of Ed-Technical here: https://bold.expert/ed-technical Subscribe to BOLD’s newsletter: https://bold.expert/newsletter Stay up to date with all the latest research on child development and learning: https://bold.expert Credits: Sarah Myles for production support; Josie Hills for graphic design

In this short, Libby and Owen discuss a hot-off-the-press study that is one of the first to test how ChatGPT impacts the time science teachers spend on lesson preparation. The TLDR is that teachers who used ChatGPT, with  a guide, spent 31% less time preparing lessons - that’s 25.3 minutes per week on average. This very promising result points to the potential for ChatGPT and similar generative AI tools to help teachers with their workload. However we encourage you to dig into the summary and report to go beyond the headline result (after listening to this episode) - this is a rich and rigorous study with lots of other interesting findings!Links EEF summary Full study Join us on social media: BOLD (@BOLD_insights), Libby Hills (@Libbylhhills) and Owen Henkel (@owen_henkel) Listen to all episodes of Ed-Technical here: https://bold.expert/ed-technical Subscribe to BOLD’s newsletter: https://bold.expert/newsletter Stay up to date with all the latest research on child development and learning: https://bold.expert Credits: Sarah Myles for production support; Josie Hills for graphic design

Irina Jurenka, Research Lead at Google DeepMind, and Muktha Ananda, Engineering Leader in Learning and Education at Google, share their insights on developing LearnLM, a large language model tailored for education. They delve into the intricacies of fine-tuning AI to enhance pedagogical effectiveness, explaining how they measure learner outcomes and the challenges of creating an engaging AI tutor. The conversation highlights the delicate balance between emotional engagement and learning efficiency, showcasing a multidisciplinary approach to innovation in educational technology.

Ben Kornell, Managing Partner at Common Sense Growth Fund and co-founder of Edtech Insiders, dives into the intricacies of AI tutoring. He differentiates between AI-powered search and genuine tutoring, emphasizing how AI can enhance human interaction. The conversation explores age-specific needs, with younger students benefiting from personal connections while older ones seek independence. Ethical concerns, such as bias and dependency, are also discussed, alongside exciting future developments that may make AI tutoring feel almost like science fiction!

Rob Wong, the Product Lead for LearnX at Google, dives into the innovative intersection of AI and education. He sheds light on the challenges of modeling learner profiles and the importance of balancing user needs with educational value. The discussion showcases exciting AI tools like YouTutor and Learning Coach Gem, emphasizing personalization in learning experiences. Wong also explores the shift to an 'AI first' approach and the ongoing efforts to fine-tune AI for supportive, engaging education. Prepare for insights into the future of learning!

Ryan Baker, a Professor at the University of Pennsylvania and Director of the Penn Center for Learning Analytics, dives into the fascinating world of big data and algorithmic bias in education. He highlights how educational data mining can enhance learner engagement and outcomes. The discussion reveals the nuances of algorithmic bias, its societal implications, and why tailored approaches are necessary to ensure fairness. Moreover, Baker debunks myths about AI in education, advocating for a balanced integration that supports educators.