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Chatbots took many schools by surprise upon their release a few years ago. Suddenly, students carried an app in their phones that could magically answer almost any homework question or spin up an essay in seconds. Of course, teachers can often tell when a student is using AI—models make mistakes that most humans don’t, and some teachers say that AI-generated text has simple giveaways like too many em dashes. 

Nevertheless, the generative AI boom increased the burden on teachers, who were already working long hours to plan lessons, make homework assignments, and grade exams, and now needed to adapt to a new technology. For many, it still feels like there’s no clear path forward. Organizations ranging from OpenAI to UNESCO encourage AI use in the classroom, but many teachers feel confused about how exactly to handle it. 

Case study

Cheshire Academy is a private boarding and day school in Connecticut with about 400 students in grades 9 through 12. Administrators there don’t force instructors to use AI at all, though the school’s librarian and technology coordinator George Aiello claims the “vast majority” of instructors use it in some way. The educators there are trying a patchwork of programs, including general-purpose chatbots like ChatGPT and Perplexity as well as more specialized tools like MagicSchool, an AI-powered platform meant specifically for educators.

That patchwork approach is partly because the school, on the advice of consultants, opted to train its staff on general techniques for how to use AI instead of prescribing certain tech. The staff training covered topics like how to craft useful prompts but also stressed the technology’s limits, highlighting its potential for generating incorrect and biased responses. 

Now, teachers there often use generative AI to prepare class materials. This means asking the AI of their choice for help with planning lessons or creating grading rubrics. Some even want to use it to help them give feedback to students, though concerns over quality, personalization, and privacy have prevented any of them from doing that just yet.

Others, like Miriam Przybyla-Baum, who teaches French, don’t use AI themselves but do address it with their teaching. Przybyla-Baum says she doesn’t really need AI’s help since she’s built up plenty of classroom materials across nearly 30 years of teaching. But she started seeing students try to use AI-powered tools like Google Translate to take shortcuts on their assignments years before ChatGPT’s launch. 

She’s developed a system to make students reflect on how AI can and can’t teach language skills. In one assignment, students let a large language model (LLM) edit their homework. Then, they go through the edits and decide which ones were correct and which ones removed their voice. In another, she has students anonymously grade each other’s AI-assisted assignments, making annotations as to which parts they think are AI-assisted.

Cheshire Academy is continuing to experiment with how AI can be harnessed productively in the classroom. The school is piloting a program in which students create media and lead discussions regarding healthy AI use. This program, which they call a “Student AI Council,” aims to push students to reflect on how AI should and shouldn’t be used to benefit the community around them.

The academy as a whole has since adopted similar techniques to those Przybyla-Baum introduced to make students reflect on their own use of AI. Assignments are now labelled like traffic lights, with green meaning AI is fully allowed and red banning any AI use. Yellow, then, lets the teacher permit some tools while banning the rest, like allowing students to use spell-check but not message a chatbot.

The tool

As generative AI was becoming mainstream, Cheshire Academy previewed MagicSchool to its staff. 

For many, MagicSchool’s main strength seems to lie in the sheer amount of offerings it provides in one package. It can generate questions and assignments of all kinds, from quizzes to worksheets, across many subjects and grade levels. It has a specialized grading rubric generator, which outputs a ready-to-go table that teachers can use to score assignments. It can make presentations and lesson plans and administrative reports, too. 

All of this is done through a single platform where educators enter specific prompts tailored for each task. For example, to make an assignment, a teacher can specify the students’ grade level, number of questions, the types of questions (such as multiple choice or short answer), and more, and include documents to align the questions with. 

Not every teacher feels comfortable using LLMs to generate student-facing text, whether because they don’t think an AI can produce effective teaching materials or helpful feedback, or because they’re concerned about accuracy. MagicSchool, which has free and paid versions, does offer tools on its platform for other tasks like lesson planning. If teachers want unlimited access and complete records in the system, though, they need to pay just under $100 per year for an individual plan. Alternatively, many of the general-purpose generative AI tools (think the chatbots on offer from Anthropic, Google, OpenAI, and more) seem suited to administrative tasks, as well, attested by the fact that many of the teachers at Cheshire Academy use those instead. Some of these companies are even rolling out features tailored for schools, to mixed results.

How to apply this

• Meet students where they’re at. Students will be tempted to try AI, and there’s no way to entirely police this for take-home assignments. The internet and social media can spread a lot of misinformation as to what AI can and can’t do, and it’s critical to counter these narratives and teach healthy strategies and relationships.
  
• Model best practices. AI is very good at automating tasks, but it struggles with precision and voice. Keep this in mind, especially when generating any text that anyone else may see. Impressionable students who see those in authority using AI in a lazy way could internalize this as an excuse to cut corners in their own work.
  
• Refine and replace. AI is great for brainstorming lesson plans and extra problem sets, especially for teachers early in their careers who don’t have a big problem bank already built up. However, because it can make mistakes (called hallucinations), using it for final drafts can result in assignments that confuse students and impair learning. Be sure to check every citation, equation, and statement an LLM makes.

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This article is from Making AI Work, MIT Technology Review’s limited-run newsletter examining how to apply LLMs across industries. To receive it in your inbox,sign up here.

From accounting to design to market research and product development, there’s a staggering breadth of skills needed to run a business. A large company can hire experts to handle these tasks, but small businesses don’t always have this luxury.

That’s where AI comes in. Today’s AI models do a decent job at these tasks. The trick for small businesses is to understand where AI is good enough and where it’s not.

One place where a “good enough” AI can already be quite valuable to small business owners is in providing secretarial skills and handling basic administrative matters. Let’s take a look at how one private tutor is using it to improve his recordkeeping and free up his time.

Case study

Sam Finnegan-Dehn works in fundraising for a charity, but he moonlights as a math and philosophy tutor for university students from his home in London. Through this part-time business, he can leverage his degrees in philosophy and share his love of the subject with clients.

But meeting with students is only a fraction of the work it takes to be a good tutor. He also plans lessons and finds fresh reading materials, creates assignments, sends invoices, and keeps up with new research—all on top of his regular job. Given these demands, Finnegan-Dehn doesn’t have as much time as he’d like to grow his tutoring roster.

So he’s turned to AI for some help in managing the day-to-day aspects of his business. He says AI has taken on a secretarial role across all of his digital notebooks, where he jots down reminders about his clients’ progress and new readings to keep himself up-to-date. He describes using AI as kind of like having a second memory that helps him connect ideas he’s written down in various places.

While he has experimented with different tools like Claude and ChatGPT, he’s now landed on Notion AI because it integrates better with his tutoring notes, which live across his notebook tabs in the Notion app. Finnegan-Dehn doesn’t use AI to create teaching materials, but he does let Notion AI record meetings with his clients (after getting their consent), and then uses its automated summaries to refine his teaching strategy. For example, if he notices from the AI’s summary that it seems like a certain technique was not helping a student, he may change how he approaches the subject next time.

Beyond this, Notion AI also helps him with goal-setting, drafting lesson notes, invoicing, and generating and syncing social media posts. For goal-setting, for example, Finnegan-Dehn says he understands his long-term goals for his business but not always the concrete steps to build to them. He uses AI to help fill in these gaps. He starts by writing down a “North Star” goal—say, to have a certain number of clients by the end of the year. Next, he asks his AI to generate the steps that he needs to take to get there, given the profile he has built up in the app. Then, he can reflect on the results and choose which tasks to tackle first.

The tool

Notion has been a big player in note-taking software for many years. Its AI add-on, released in late 2023, now has tools that enable it to interact with many other online productivity platforms. There’s an email client, calendar integrations, and a newly released agent. And while this level of access has raised privacy concerns, it can also make for a pretty powerful virtual assistant.

Many of the tasks targeted by Notion AI are less creative and more rote: syncing information across documents or searching through old scribbles, for example. This makes the tool especially appealing to small business owners, who have limited bandwidth, particularly for menial work.

Other companies are developing tools targeted at specific industries. For example, Grandma’s Quilt Shop in Yuma, Arizona, uses Rain, which has a software suite tailored to craft companies, to generate inventory descriptions and pricing for its stock of fabric designs. The owners claim this AI tool cuts the time it takes to list items by 60 to 80%.

There are drawbacks, though, as Finnegan-Dehn described some of Notion AI’s idiosyncrasies as “clunky” at times. And the AI add-on for Notion costs $20 per month. As with all new tools, small business owners should carefully assess how the potential gains and headaches measure up against the cost of just doing the job themselves.

User tips

Consider these points when thinking about whether AI might be able to help you run a business, or make any part of your work life just a little bit easier. 

• Look before you leap. Since LLMs feed on the data you input to answer your queries or complete tasks, you want to give them information in a way that’s convenient for you and for the model. For many of these notebook AI services, this means, for example, using their platform for notetaking so you don’t have to input or upload notes later. Because of this, it’s a good idea to weigh your options carefully before committing to an AI-powered ecosystem.
• Work to your strengths. Think about what skills you lack in-house, and see if AI can either help with training or take these tasks on for you. Just be aware: AI hallucinates and makes mistakes, so think about where accuracy is needed and keep humans in charge there.
  
• AI isn’t always the best tool. It’s okay to use something off the shelf when that’s the better choice. It’s going to be safer, for example, to use existing payment processing platforms like Shopify or Square than to vibe-code one using AI.
  
• Consider using local models for any sensitive information. Our reporting has covered the risks that online AI models have in leaking sensitive data, and there have been many reports about how AI companies collect your data when you ask their chatbots questions. Even if your business doesn’t handle personal information, there can still be some things you’d prefer not to share publicly. In these cases, using an open-source model that makes inferences on your prompts locally can be a great option, instead of ChatGPT or Claude or other proprietary models. Thankfully, some LLMs can now be run off of laptops and small desktops. Here’s how to set one up and start using it.

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