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.