TeachLab with Justin Reich

The timing of the arrival of AI has been bad for many schools and teachers. In the Fall of 2022, schools were just emerging from the extraordinary challenges brought by the pandemic. Teacher morale and turnover are at historic lows. Some school districts are on a good footing, and have the resources and stability to adapt, and even experiment with using AI to support learning, but many teachers tell us generative AI has just been one more thing they have to deal with, often without the support they would like.

At a moment when unprecedented sums are being invested in AI development — including many billions devoted to AI powered education technology — teachers wonder if our priorities are in the right place.

This episode was produced by Steven Jackson and Jesse Dukes. We had editing from Ruxandra Guidi and Alexandra Salomon. Reporting and research from Holly McDede and Andrew Meriwether. Reporting and research for the series from Chris Bagg, Andrew Parsons, Natasha Esteves, and Marnette Federis. Sound design and music supervision by Steven Jackson.

Production help from Yebu Ji. Data analysis from Manee Ngozi Nnamani and Manasa Kudumu. Administrative support from Jessica Rondon. Special thanks to Josh Sheldon and Eric Klopfer.

Original music for this series was created by Steven Jackson, Andrew Meriwether and Jesse Dukes, as part of the music project Cue Shop. Thanks to Will Grueb, Andy Wilds, and the MIT Music Department for letting us use the MIT Harpsichord.

The research and reporting you heard in this episode was supported by the Spencer Foundation, the Kapor Foundation, the Jameel World Education Lab, the Social and Ethical Responsibility of Computing initiative at MIT, and the RAISE initiative, Responsible AI for Social Empowerment and Education also at MIT. We had support from Google’s Academic Research Awards program.
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We keep saying that most ed tech is invited into schools, but generative AI crashed the party. But the truth in, some teachers have invited AI into schools. Some think it’s been great, and others see opportunities as well as challenges.

We visit four classrooms where students and teachers are using AI in ways they say are innovative, fulfilling, and effective: a student in Oregon who worked with her teacher to expand the scope of her assignments with AI; a Southern California instructor using AI to bypass busywork and focus on the creative aspects of filmmaking; a teacher in Illinois deploying AI to provide targeted feedback; and, finally, a former engineer whose students use AI coding tools to make circuitboards…play Rick Astley?

And we ask the question: What are the human teachers doing to create conditions in which AI can support learning?

This episode was produced by Jesse Dukes. We had editing from Alexandra Salomon and Ruxandra Guidi. Reporting and research from Chris Bagg, and Andrew Parsons. Additional reporting from Andrew Meriwether, Holly McDede, Natasha Esteves, and Marnette Federis. Sound design and music supervision by Steven Jackson.

Thanks to Jerry Shaw for digging up some of those Arduino Uno songs. The “Uptown Funk” was a simulation, created in our studios.

Production help from Yebu Ji. Data analysis from Manee Ngozi Nnamani and Manasa Kudumu. Administrative support from Jessica Rondon. Special thanks to Josh Sheldon and Eric Klopfer.

Original music for this series was created by Steven Jackson, Andrew Meriwether and Jesse Dukes, as part of the music project Cue Shop. Thanks to Will Grueb, Andy Wilds, and the MIT Music Department for letting us use the MIT Harpsichord.
The research and reporting you heard in this episode was supported by the Spencer Foundation, the Kapor Foundation, the Jameel World Education Lab, the Social and Ethical Responsibility of Computing initiative at MIT, and the RAISE initiative, Responsible AI for Social Empowerment and Education also at MIT.

We had support from Google’s Academic Research Awards program.

Education technology companies have been falling over themselves to provide AI powered tools for schools, and to convince schools to buy their product. The array of AI powered products is dizzying, and while some can likely be put to good use, it can be hard for school leaders to gauge which are worth adopting.

One school system in Iowa, Winterset Community School District has gone all in on generative AI, piloting at least three AI powered products and encouraging faculty and students to experiment with AI. They kindly let us visit for a week, and we dive in to see what they’re excited about, and what has been challenging.

This episode was produced by Andrew Meriwether and Jesse Dukes. We had editing from Ruxandra Guidi and Alexandra Salomon. Reporting and research for the Homework Machine from Natasha Esteves, Holly McDede, Andrew Parsons, Marnette Federis, and Chris Bagg. Sound design and music supervision by Steven Jackson. Production help from Yebu Ji. Data analysis from Manee Ngozi Nnamani and Manasa Kudumu. Special thanks to Josh Sheldon and Eric Klopfer.

Administrative support from Jessica Rondon.

Thanks to Chad Sussex and everybody at Winterset who spoke to Andrew Meriwether. We’re happy to share that AI Express: Leading the Future of Learning — written by Chad Sussex and Sandy Groom-Meeks, who guided the AI journey for Winterset — is now available. This book offers school leaders and educators a practical roadmap for safely and effectively implementing AI in schools. Find the book here:

https://bit.ly/AIExpress_Amazon

Original music for this series was created by Steven Jackson, Andrew Meriwether and Jesse Dukes, as part of the music project Cue Shop. Thanks to Will Grueb, Andy Wilds, and the MIT Music Department for letting us use the MIT Harpsichord.

The research and reporting you heard in this episode was supported by the Spencer Foundation, the Kapor Foundation, the Jameel World Education Lab, the Social and Ethical Responsibility of Computing initiative at MIT, and the RAISE initiative, Responsible AI for Social Empowerment and Education also at MIT. We had support from Google’s Academic Research Awards program.

We’re halfway through “The Homework Machine” series, and as our production team works to finish the final episodes, we check in with Teaching Systems Lab director Justin Reich, who is returning to the classroom after a full year of sabbatical. Justin has been nervous, since some of the assignments he’s relied on in the past are not “AI-proof”. He tells us how he plans to adapt,

We want to share A Guide to AI in Schools: Perspectives for the Perplexed the guidebook authored by TSL, based on the interviews we conducted for “The Homework Machine”.

Justin is a fan of these protocols for evaluating student work:

We’re about halfway through The Homework Machine min-series, and we have more episodes coming. We're taking a break this week, which gives us the opportunity to share an episode of one of our favorite education podcasts with you.

Mindshift from KQED features in depth interviews and reports from classroom about education and educators. We particularly enjoy this episode about a shift to teach contemporary poets, alongside the classics.

Hanif Abduraqqib. Sarah Kay. Elizabeth Acevedo. Clint Smith. Do any of these names sound familiar? How about Amanda Gorman? All of these writers are part of America’s thriving contemporary poetry scene. But you won’t find them in many text books, because high school poetry units tend to focus on dead poets, like Robert Frost, Walt Whitman and Edgar Allen Poe. North Carolina teacher Melissa Smith is working to change that. For the last seven years, she’s been diversifying the canon in her classroom, and encouraging other teachers to do the same with the hashtag #teachlivingpoets. The shift has inspired teachers across the country to get creative with how they teach students things like tone, rhythm and structure in poetry. And it’s inspired students to connect with and see reflections of themselves in the poets they study.

We have more episodes of the Homework Machine coming in a little over a week. In the meantime, please take our listener survey. (We'll enter you in a drawing to win a $25 gift card).

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Students tell us that they know learning in schools is important. But sometimes, turning to ChatGPT to get their work done feels like the best option. AI might help with what they perceive as busy work, or they might be confused about what counts as a legitimate use, and what counts as cheating.

And sometimes, students tell us, they know they’re crossing a line. When that happens, it’s usually because they’ve hit a wall in the learning process, and generative AI presents a quick and easy way through the blockage. For teachers, there is no single, off the shelf solution, that will ensure students make wise decisions, but understanding why students turn to AI can be a helpful starting point.

This episode was produced by Andrew Meriwether Jesse Dukes. We had editing from Ruxandra Guidi and Alexandra Salomon. Reporting and research from Natasha Esteves, Holly McDede, Andrew Parsons, Marnette Federis, and Chris Bagg. Sound design and music supervision by Steven Jackson. Production help from Yebu Ji. Data analysis from Manee Ngozi Nnamani and Manasa Kudumu. Special thanks to Josh Sheldon and Eric Klopfer. Administrative support from Jessica Rondon.

Featured guests include Miriam Reichenberg, Kaitleen Evangelista, as well as anonymous students. Thanks to InTandem for facilitating interviews.

Thanks to Greer Murphy and Jessa Kirk, at UC Santa Cruz's Academic Integrity Office. Check out Greer Murphy's co-authored survey of academic integrity policies.

Original music for this series was created by Steven Jackson, Andrew Meriwether and Jesse Dukes, as part of the music project Cue Shop. Thanks to Will Grueb, Andy Wilds, and the MIT Music Department for letting us use the MIT Harpsichord.
The research and reporting you heard in this episode was supported by the Spencer Foundation, the Kapor Foundation, the Jameel World Education Lab, the Social and Ethical Responsibility of Computing initiative at MIT, and the RAISE initiative, Responsible AI for Social Empowerment and Education also at MIT.

We had support from Google’s Academic Research Awards program.

Teachers have all sorts of opinions about AI. Some are optimistic, some are pessimistic. But the most common topic that came up in our interviews was cheating.

While students have always taken shortcuts to complete their work, ChatGPT and other generative AI have a historically unique power to quickly, convincingly and comprehensively do a students’ assignment. This is proving a powerful temptation to students.

So how do teachers help their students make good decisions? Teachers know that schools have historically struggled to manage discipline fairly but they also recognize that letting students get away with cheating isn’t doing them a favor. Teachers share how they’re navigating the Scylla and Charybdis of school discipline in the AI age.

Listen to a bleeped version of this episode (Coming soon!).

Transcript coming soon!

This episode was produced by Jesse Dukes with Yebu Ji.
Editing: Alexandra Salomon and Ruxandra Guidi
Reporting and research from Natasha Esteves, Andrew Meriwether, Holly McDede, Andrew Parsons, Marnette Federis, and Chris Bagg.
Sound design and music supervision by Steven Jackson.
Production assistance from Nathan Ray.
Data analysis from Manee Ngozi Nnamani and Manasa Kudumu.
Special thanks to Josh Sheldon, Camila Lee, Liz Hutner, and Eric Klopfer.
Administrative support from Jessica Rondon.

Thanks to the teachers who spoke to us including Joe O'Hara, Alec Jensen, Schuyler Hunt, Anna Rose Pandey, Ray Salazar, and Jessica Petit-Frere. And thanks to all the teachers and students who partipated in our research.

Thanks to Greer Murphy and Jessa Kirk, at UC Santa Cruz's Office of Academic Integrity. Check out Greer Murphy's co-authored survey of academic integrity policies.

The research and reporting you heard in this episode was supported by the Spencer Foundation, the Kapor Foundation, the Jameel World Education Lab, the Social and Ethical Responsibility of Computing initiative at MIT, and the RAISE initiative, Responsible AI for Social Empowerment and Education also at MIT.

We had support from Google’s Academic Research Awards program.

The Homework Machine is a program of the MIT Teaching Systems Lab, Justin Reich, director.

ChatGPT is the most well known of the Large Language Models (LLMs) but what is an LLM? We go deep into how this remarkable new technology is built, and why their performance is inconsistent — or jagged — across similar tasks. We dive into the techniques AI engineers use to align these tools’ behavior with our values, and explain why they don’t always work, and sometimes we get hallucinations or biased output. 

This episode was produced by Steven Jackson and Jesse Dukes

Editing:  Alexandra Salomon and Ruxandra Guidi  

Reporting and research from Holly McDede, Natasha Esteves, Andrew Parsons, Andrew Meriwether, Marnette Federis, and Chris Bagg.

Sound design and music supervision by Steven Jackson. 

Production assistance from Yebu Ji and Nathan Ray. 

Data analysis from Manee Ngozi Nnamani and Manasa Kudumu. 

Special thanks to Josh Sheldon, Camila Lee, Liz Hutner, and Eric Klopfer. 

Administrative support from Jessica Rondon. 

The research and reporting you heard in this episode was supported by the Spencer Foundation, the Kapor Foundation, the Jameel World Education Lab, the Social and Ethical Responsibility of Computing initiative at MIT, and the RAISE initiative, Responsible AI for Social Empowerment and Education also at MIT. 

We had support from Google’s  Academic Research Awards program. 

The Homework Machine is a program of the MIT Teaching Systems Lab, Justin Reich, director.