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).

https://forms.gle/KwPGTeVYZh2mo6gF7

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.

In late November of 2022, ChatGPT was released to the public as a free research preview. Students quickly realized ChatGPT was pretty good at doing their homework for them. Schools scrambled to figure out what to do: Ban it? Embrace it? Teachers and students found themselves adapting to a new reality.

Buckle Up, Here it Comes kicks off “The Homework Machine” a mini series in the Teachlab podcast. Hosts Jesse Dukes and Justin Reich share stories of teachers and students reacting to the arrival of an exciting, alarming, and strange new technology.

Producer: Jesse Dukes

Editors: Ruxandra Guidi and Alexandra Salomon.

Reporting and research: Holly McDede, Natasha Esteves, Andrew Meriwether, and Chris Bagg.

Sound design and music supervision: Steven Jackson.

Data analysis: 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.

Additional support from Google’s Academic Research Awards program.
InTandem facilitated some of our student interviews.

Full episode transcript.

Generative AI is not like other education technologies, which schools often invite into the classroom. This one crashed the party. And then, it started re-arranging the furniture. We wanted to learn more, so in a little over a year, the Teaching Systems Lab has talked to over 90 teachers and 30 students about the impact of Generative AI.

Some are excited about AI's potential to transform education for the better.

Others are troubled by the temptations of a machine that can quickly and convincingly do many homework assignments.

And some think AI is just a shiny object – a distraction from the much larger problems facing education.

Over the next seven episodes, we'll try to answer the question: Is AI a game-changing tool, a threat to critical thinking, another ed tech fad? … or something else?

Media requests can go to jpd009@mit.edu.

School leaders, education researchers, and others often point to a study conducted by Stanford researchers that suggested the arrival of generative AI in K-12 school has not meaningfully increased the percentage of students who acknowledge some kind of academic dishonesty. Sometimes, school leaders or experts suggest it means there’s “nothing to see here” when it comes to tools like ChatGPT and worries about students bypassing learning. Researcher + Journalist Jesse Dukes joins Justin to dive into the specifics of that study, and compare it with anecdotes from interviews with students and teachers.

This episode was produced by Andrew Meriwether and Jesse Dukes. We had additional reporting from Holly McDede and research help from Natasha Esteves and Manassa Kudumu. Thanks to the Spencer Foundation and the Social and Ethical Responsibilities of Computing Initiative at MIT for funding our ongoing research into the arrival of generative AI in schools. And thanks to the Kapor Foundation for funding Jesse’s work in California with KALW public radio. Thanks to all of the teachers and administrators who have talked with us.

If you want to take our survey, or learn more about our research into Generative AI and K12 education, head over to tsl.mit.edu/ai and if you want to volunteer for the sister study, in California, visit Jesse’s Linktree.

Justin Reich and researcher and producer Jesse Dukes argue that AI in requires a new theoretical framework. Generative AI, unlike many teaching technologies, is an "arrival" technology, meaning it will be present in school environments regardless of what choices school leaders make about whether to adopt it. Their new preprint Toward a New Theory of Arrival Technologies: The Case of ChatGPT and the Future of Education Technology after Adoption explores the idea of "arrival technologies"

But rather than summarize it, hey, why not let the arrival technology do it? So we turn to Google's new tool, Notebook LM for a "Deep Dive Conversation" to summarize the article.

Thanks to the Spencer Foundation and the Social and Ethical Responsibilities of Computing initiative at MIT for funding our ongoing research into the arrival of generative AI in schools. Thanks to all of the teachers and administrators who have talked with us. This episode was produced by Jesse Dukes. We had research help from Chris Bagg, Manasa Kudumu, Natasha Esteves, and Andrew Meriwether. If you want to take our survey, or learn more about our research into Generative AI and K12 education, head over to tsl.mit.edu and if you want to volunteer for the sister study, in California, visit Jesse’s Linktree.