A Map of Generative AI for Education

We introduce a new map of the current state-of-the-art

31 min readJun 2, 2023

A newer version of this article can be found here.

One morning shortly after Thanksgiving, 2022, we woke up to discover that technological capability had advanced by five years while we were sleeping. It took another week or two for us to realize it, but that event, the launch of ChatGPT, may have a more far-reaching effect on K-12 education than on any other sector of life.

In normal times, technology advances in step with its application, with the user experience, the interactions that unfold in and out of the classroom. Whiteboards become smart boards.

But 2023 feels more like a dislocation. How will these remarkable advances emerge into the experience of students and teachers? We want to map that landscape in its earliest stage and watch how it evolves.

Many of the possibilities we describe in more detail below are unexplored, while others have been substantially investigated by startups, researchers, and — as Ethan Mollick, a professor at Wharton, has emphasized — individual students and educators experimenting.

It’s not easy to predict, but two paths seem possible. The first is what has almost always happened to new technology in the classroom: it rearranges the furniture. Laptops become expensive slide projectors. Personalized instruction winds up meaning worksheets with garish dashboards added. It was recently estimated that the average teacher uses 42 edtech tools in the course of their work.

The second path is that the inefficiency and dullness of the industrial way of schooling begin to disappear. Many of the teaching practices that learning science has shown to be most effective — such as active learning and frequent feedback — and most engaging for students — such as role play and project work — require significant time most teachers just don’t have. Could that change if every teacher had an assistant, a sort of copilot in the work of taking a class of students (with varying backgrounds, levels of engagement, and readiness-to-learn) from wherever they start to highly skilled, competent, and motivated young people?

We will see.

(Note, even as a snapshot, our map is surely incomplete. Please let us know what we’ve missed.)

Compiled by Laurence Holt and Jacob Klein. Laurence is based in New York and has spent the last two decades leading innovation teams in for-profit and nonprofit K-12 organizations. Jacob is an edtech product leader, advisor, and entrepreneur based in Oakland, California. Special thanks to Doug Jaffe for review.

Teacher Practice Support

Lesson plan generation / feedback

A teacher who wants to incorporate more writing into sixth grade science uses a tool like Nolej to generate a lesson based on an existing OpenSciEd plan but with an embedded writing activity.

Studies show that a surprising proportion of teachers do not have a core program but use their own lessons or search TeachersPayTeachers or Pinterest, with results of highly varying quality. For them, AI might represent a sort of TeachersPayTeachers on steroids.
Tools need to get better at being guided by the teacher — eg, x minutes of group work, y minutes of class discussion, etc.
An area of growth may be in generating adaptations of existing lessons rather than wholly new material: “I have to follow the Illustrative Math scope and sequence but can we make this activity a role play?”
Coherence could be a problem. Tools need to generate units not individual lessons and to take account of other aspects of the learning experience including intervention and assessment.

Instruction coaching

A teacher records the audio of a lesson and uses a tool to get feedback on skills such as wait time and handling specific student misconceptions.

Tools allow a teacher to record a lesson and get automated analysis and feedback. Current tools like TeachFX or Edthena provide analysis after class. Future tools may allow real-time coaching on screen or via an earbud.
Tools can help coach teachers on evidence-based frameworks such as Marzano or Danielson, supporting school-wide implementations.
Evidence in other sectors (eg, customer service) suggests a significant improvement in performance is possible through AI coaching.
Audio quality remains an unsolved problem: the teacher may be audible only part of the time and students hardly ever. Better audio equipment may be intrusive.
Some teachers worry about who will have access to recordings and transcripts.
The technique is currently better suited to whole class pedagogies, whereas effective practice may be more small-group-oriented.
Several researchers are working on more sophisticated analysis and feedback — eg, Dora Demszky at Stanford.

Teaching advisor

An elementary school teacher finds that a subset of his class did not understand negative numbers. He uses AI to evaluate his current lesson plan, the students’ responses to in-class checks-for-understanding, and arrives at an alternative way to introduce the concept in small groups.

If fine-tuned on pedagogy, an AI tool could play the role of coach and advisor to a teacher. For instance, it could give advice on specific ways to teach concepts, suggest alternatives, and diagnose student strengths and misconceptions.
Tools can also ingest an existing lesson and advise the teacher on improvements to it such as changes of pace, embedded checks for understanding, activities to ensure foundational skills are in place, connections to concepts students have already learned, etc.

Competency-based grading

In a high school English unit, students zoom with refugees in order to write about their stories. The conversations are an opportunity for students to practice competencies including empathy and listening. An AI tool sends relevant snippets to the teacher to support competency-based feedback later.

Feedback and evaluation of transferable competencies — such as creative thinking and collaboration — is unfamiliar for most teachers. But evidence can often be found in student discussion or in work products such as presentations. AI tools could support teacher identification and evaluation of target competencies.
To be sufficiently accurate, tools will require units designed with specific competencies in mind, progressions for each competency, and good audio recording.

Tracking student project work (PBL)

Students work on a project to create financial models for a new business. An AI tool tracks assets that students post to an LMS. The AI supports some students directly and provides daily reports to the teacher on who is making progress, what mathematics is incorporated into each student’s model, and who needs teacher support.

Keeping track of the diversity of student work in a project-based learning (PBL) class can be challenging for a single teacher. PBL teachers also have anxiety about which standards are being covered by which students and when. Partly as a result, PBL is still a rare practice. AI supports could help it become more widespread.
If students keep a journal during the project, and/or post assets to a repository such as an LMS, an AI tool can analyze their work and (a) support student direction, (b) provide a synthesis to the teacher, and (c) alert the teacher when individual students need support.

Collaboration around content + practice

An elementary school teacher, frustrated with the procedural approach to fractions in her math program, turns to an AI-moderated professional learning platform. There she is able to connect with like-minded teachers, have articles and research papers recommended to her automatically, participate in discussion with AI as subject-matter expert, and ultimately select new activities for her class.

Online collaboration and Professional Learning Communities (PLCs) often don’t attract a critical mass of educators and so never quite ignite. AI tools could make them much more active places.
AI-powered collaboration tools can understand a teacher’s focus and challenges, match them with similar educators, and inject relevant research and blog posts into the conversation.

Analysis of student data

An elementary school teacher uses data from reading running records to analyze growth for students who have been receiving an intervention: Has their growth accelerated? Who is responding to the intervention and who is not? What letter combinations do they most frequently miss as a group? What skills do they no longer need to work on? She uses the data at a meeting with the interventionist to optimize instruction and to re-assign students to groups that fit them better.

AI can ingest student data (eg, in CSV format or as a pasted table) and perform analyses on it, suggesting optimal student grouping, focus areas, and generating other insights.
Tool’s that can perform analysis and data visualization include OpenAI’s Advanced Data Analysis and Channel. For instance, you could upload NWEA MAP data from several cohorts and ask the tool to identify areas with the strongest and weakest growth, or ask for an analysis of summer loss, or combine data from several sources to build a detailed, cross-subject picture of a class.

Background knowledge refresh

A middle grades science teacher wants to anticipate challenging questions her student might ask while discussing the human circulatory system.

Educators may want to refresh or deepen their knowledge of a topic before running an open discussion of it, especially if they didn’t major in the topic.
People may be more willing to ask for help from an AI than a peer or their supervisor.
Teachers who follow the curriculum closely rather than “teaching the domain” may get less value from a refresh but miss the engagement that can come from pursuing student-led inquiry.

Admin support to free teacher time

An elementary school teacher who used to spend an hour at the end of each week compiling a summary of what students will work on next week for a parent-facing app, now relies on AI. She pastes links to and extracts from reading, math, and other curricula into a chat window and asks for a parent-friendly summary. She reviews the result, adds an anecdotal sentence, and posts within five minutes.

A major obstacle to teachers implementing new and better practices is lack of time. Teachers may not have capacity to provide personalized feedback and support, or to plan for new pedagogical methods, even though they would like to.
Like a human assistant, AI tools could perform some teacher tasks such as drafting emails to colleagues and parents, responding to parent inquiries, inputting grades to the SIS, and tracking homework completion and use of supplemental digital tools.
AI can provide a natural language interface to common tasks — eg, “send the class a reminder that their prototype is due this Friday” which generates a post to the LMS.

Incorporating research-based practice

A reading coach finds a journal paper for a method of teaching vocabulary. She uses Elicit to get a summary of the paper and ask questions about the specifics of implementation. Then she generates a protocol customized to the vocabulary words she will be teaching in her next session.

Educators are, like doctors, expected to read the latest research on evidence-based practice and incorporate them into their teaching. Very few have the time or skills to do so unaided.
AI tools can find relevant papers, summarize them, answer questions as if they were the paper’s author, and even generate sample lesson segments incorporating the practice and customized for the topic a teacher is about to teach. This could potentially help bridge the long-standing research-to-practice gap in K-12 education.

Classroom Material

Activity-specific content

A tenth grade US history teacher wants to find a more engaging method of teaching the Cold War. He uses an AI tool to create a role-play simulation in which students play US and Soviet leaders in a re-enactment of the Cuban Missile Crisis. Before the simulation, students work in groups to research and write briefing documents for the players.

Many evidence-based and highly engaging methods of teaching require significant additional teacher effort to prepare. AI tools can dramatically reduce that effort, allowing a much larger proportion of teachers to use them.
Tools can take a topic or resource and design a jigsaw exercise for student groups with research and discussion prompts.
AI can generate role play materials — eg, you are a 1920s door-to-door vacuum cleaner salesperson (so you’ll have to be able to explain what a vacuum is). The AI could also play one of the roles.
AI can set up a debate on a topic and help students prepare.
AI can support teachers in creating activities based on research-based strategies such as contrasting cases (eg, Dan Schwartz, Stanford), for example contrasting the graphs of equations that differ only in their use of +/- operators.
Flipping the classroom, in which students study independently what would previously have been the subject of a teacher lecture and then spend teacher-time on applications and problem-solving, can be effective but hard work. AI can generate materials to support it and work with students while the teacher circulates.

Explanations

A high school biology class includes several students with below grade-level reading comprehension. The teacher decides to augment classroom explanations with those written in considerate text — ie, at the students’ level. She takes existing content and uses AI to rewrite it. She uses questions generated by the AI to get feedback from students on whether they understood the explanation.

A basic element of instruction is explanation — of a concept, a big idea, a process, an event, etc. There is evidence that explanations customized to individual learners are more effective.
For instance, AI tools such as DiffIt can help a teacher take an existing explanation and re-write it at any reading level. If the AI has data on the student’s knowledge level (eg, previous assessment data or student work), it can take that into account. Explanations that rely on prior knowledge a student doesn’t have are, of course, not very helpful.
The tool can also create explanations that incorporate specific student interests, for instance explaining area and volume in terms of Minecraft.
Tools like Revyze and PeerTeach allow students to create explanations for each other and can use AI to ensure the contents are accurate before sharing. Students may find explanations created by peers to be more accessible.
To spice things up a little, AI can generate explanations in unusual formats, for instance a ballad, hip-hop song, or story explaining DNA-RNA-protein. Tools can also create animations or YouTube-like videos (Prof Jim) from explanation text.

Student questions generation

A sixth grade student practicing unit rate questions asks her AI for questions based on Pixar movies. The AI says to complete her assignment she can answer either six moderately difficult questions, four challenging questions, or two formidable questions. She takes a deep breath and plunges in.

US classrooms use millions of questions and prompts for practice and formative assessment every day. In both cases, variety is good. But it is time-consuming to generate the perfect question set. AI tools such as PrepAI, to teach_ and Mindgrasp can take a content area and generate questions together with rubrics and model answers.
Tools can generate a variety of questions: multiple choice, short answer, essay prompts, exit tickets, etc.
They will evolve to offer questions that are customized to student interests, to different levels of challenge, different levels of Bloom’s taxonomy, open-ended Fermi problems, and mini-projects.
(Importantly, they can also automatically grade all these types of problems and give students detailed feedback. See “Feedback on student work” below.)

Active learning embeds

A middle school science teacher uses AI to take an existing lesson on plate tectonics and generate several challenging questions. The teacher selects one about the implications of tectonics. Students turn and talk then record their answers with feedback from AI. The teacher gets an evaluation of which students have understood the lesson so far.

Active learning is a much more effective method than traditional classroom teaching but it has proven difficult to train teachers to convert their lessons to active ones. AI tools could take an existing lesson and suggest active adaptations.
For instance, an AI tool can take the text of a traditional lesson and suggest active learning embeds such as having students break into groups to research a topic or work on problems. Students can receive feedback in real-time and AI could alert the teacher to groups who need further challenge or support.
Alternatively, the AI tool could convert an existing lesson into engaging media such as video with embedded questions, or a student interview with a character from science or history.

Focus on big ideas

An elementary school teacher, worried that her current unit on fraction focuses too much on algorithms and manipulations rather than the big idea uses AI to generate an alternative sequence. The AI recommends a number line game from the research literature designed to emphasize that a fraction is a number.

Off-the-shelf curricula sometimes attempt to cover so much ground that the big ideas get lost. For example, it is common to find entire units on fractions that fail to drive home the point that a fraction is a number.
AI can identify big ideas — in existing content, from standards, or by topic — and generate lesson material such as video, animation, or checks-for-understanding in varying contexts to ensure students have a solid grasp on the idea before going on to apply it.

Focus on transfer

An elementary school teacher notices that his students are adept at solving fractions problems but not at using them in real-world situations. He uses AI to generate fractions problems at fourth grade level across a wide range of contexts and has groups of students select three different contexts to work on.

Transfer is the ultimate goal of learning — enabling the learner to apply skills in new situations. Research shows that transfer is enhanced by practicing skills in varied contexts, for instance solving equations in abstract, word-problem, and authentic real-world situations.
AI tools can generate examples — including questions with and without solutions — across varying contexts, including the real world.
Tools can identify connections with similar concepts in other subjects, aiding transfer.
And tools can interleave examples of two or three different skills, so that students don’t always know what skill to expect.

Worked examples

A fifth grade math teacher wants to provide extra support to three of her students. She uses AI to generate worked examples interleaved with practice problems and compile them into a booklet she sends home with the students.

Worked examples — step-by-step demonstrations of how experts solve problems — improve students’ ability to solve similar problems. Without examples, students sometimes reinforce flawed strategies.
AI tools can generate worked examples (both correct and incorrect — students identify the misstep) for a given topic. Examples can be interleaved with practice problems, similar to those included in professional programs such as Algebra By Example.

Flashcard generation

A global history teacher takes a video on the industrial revolution and uses AI to generate flashcards based on the video transcript. She includes the flashcards in a digital study guide she posts to her class via the LMS.

AI can take text or video-transcript content and generate flashcards from it. For some types of material — eg, vocabulary — flashcards can be a helpful way for students to learn. Many flashcard apps provide practice based on spaced repetition which aids retention.
Flashcard apps like Quizlet and Anki and classroom engagement apps such as Kahoot are incorporating AI to generate flashcards and other formats such as quizzes and games.
A student can highlight any term, from any class, that they are unsure of, to add to their personal spaced-repetition flashcard bank.

Vocabulary / glossary

An elementary school teacher beginning a unit on the weather uses AI to create a glossary of terms with definitions, examples, and etymologies at a fourth grade reading level and with translations to Spanish. She asks the AI to create claymation images for each of the examples which she includes in the glossary.

AI can take text or video-transcript content and generate a vocabulary list or glossary for it including definitions, usage examples, and etymology.
The glossary can include definitions written at a specific reading level and/or translated into a student’s home language.

Quiz questions

An elementary school teacher wants to check that students have read and understood their homework reading: a short book on Paul Revere’s ride. The teacher uses AI to generate four questions per chapter, one at each Depth of Knowledge (DOK) level 1 through 4. He includes the quiz questions in a take-home pack for students.

AI can generate quiz questions based on a text or video transcript. Questions could be multiple choice, short answer, etc, at a specified Depth of Knowledge level or Bloom’s Taxonomy level. They can include model answers for the teacher.

Graphic organizers

A middle school science teacher is teaching a unit on ecosystems. She uses AI to generate a graphic organizer for a food web from producer to decomposer. She includes multiple blank versions of the organizer in a handout for students together with one model food chain, completed by the AI a pond ecosystem.

With help from plug-ins like Show Me, AI tools are already capable of rendering diagrams, such as a graphic organizer for a topic. They can also create a partially complete version of the graphic for students to fill in.

Just-in-time skill builder

A student working on a project to build and tune a wind instrument realizes she can’t succeed through trial and error tuning. At the prompting of her teacher, she collects data on the frequencies produced by different lengths of tube. But she is stuck in figuring out how to plot the data and fit a curve to it in a spreadsheet. She turns to an AI tool that walks her through the process and explains the underlying math in a way that gets her back to the project quickly.

Highly engaging learning experiences — projects, role plays, simulations, etc — often deliver students to a moment where they are motivated to upgrade their skills. Ideally, a teacher is right there but that can be difficult to orchestrate, especially across a whole class.
AI tools could step in providing just-in-time skill-specific instruction. That could be content that is part of a curriculum, provided by the teacher, generated by AI, or curated by AI from high-quality open content.
Just-in-time content is likely to be more effective if it refers to the specific context the student is in. For instance, if a student wants to fit a curve to air pollution data, the AI could incorporate that context into the instruction.

Extended learning

A middle school student who appears already to have a good grasp of natural selection is given a choice of extension questions to research. She is concerned about the environment and so chooses to find and report on an example of human activity influencing species via natural selection. She creates a video describing pesticide resistance in insects. The AI asks for more detail about the long-term consequences and strategies to mitigate them which the student enthusiastically provides in a follow-up video.

AI tools can provide extended learning, enrichment, and new challenges to students who are ready to go further. The AI can offer a set of directions for a student to pursue, enhancing engagement. Rather than just previewing the next unit, extensions can go deeper into the existing topic.
Extensions can build autonomy for instance by generating a big question for the student to research. The student can present ideas to be evaluated by the AI which also reports progress to their teacher.

Connecting new content to old

A high school history teacher wants to make a strong connection from the ideas in US founding documents to the Enlightenment precursors. An AI tool suggests that students read excerpts from John Locke that have been curated to highlight the relevant ideas and create a graphical representation showing the connections. The AI pinpoints the excerpts, generates a rubric, provides a model answer for the teacher, and gives feedback on student responses.

The press to get through content in subjects such as history can leave students with a feeling of disconnected silos. To offset that, teachers can make deliberate connections across material.
AI can help identify connections based on, for example, the full course syllabus. It can also generate content and activities to deepen the connection such as a graphic organizer mapping the ideas driving the American Revolution and founding documents to the Enlightenment precursors that inspired them.
This approach can also be used to “spiral” — ie, revisit prior material but with increased richness and complexity.

Personalized reading material

An elementary school teacher transforms daily reading time by giving students a tool to create their own on-level books aligned with a unit on Greek myths they are studying. The AI tool generates a mini-book for each student based on their favorite mythological character and creates illustrations to match. The books include comprehension checks embedded in the text. The teacher prints each book out so her students can share it with the class and with family.

Early readers need lots of reading material for practice. But curating a book set that combines narrative and non-fiction text matching both the student’s reading level and interests is challenging.
AI tools such as Koalluh can both identify suitable texts in the classroom library and, perhaps more importantly, generate new texts that fit perfectly. They can target phonics skills and embed comprehension checks. They can ensure vocabulary words are reinforced across texts rather than appearing only once, which makes learning more difficult.
Children can customize characters, choose how the plot unfolds (learning about story structure), and even change illustration styles.

Less-cheatable questions

A high school English teacher, worried that students may be using a chatbot to write essays, employs AI to interview students individually on their essay: what research they did, how they decided to structure the essay, what they left out, etc.

Students are already using ChatGPT to write essays and answer worksheet questions. GPTZero and others offer AI detection.
AI tools can, though, be used to make cheating difficult. For instance, an AI tool can question a student about their essay, what research they performed, decisions they made, their writing process, etc.
Teachers can also give alternate format questions: instead of having students summarize an article — something an AI does easily -ask them to record a presentation with audio or video, using AI to automatically generate a transcript and act as evaluator of the result.
If the purpose of teaching writing is, in some large part, to teach analytical thinking, there may be other ways to do the same. For example, some teachers embrace AI-as-essay-writer and ask students to analyze, fact-check, and improve on the generated essays.

Evaluation + Feedback

Holistic assessment (based on longitudinal student work)

A state agency proposes releasing multiple hours of formal assessment time to be used for instruction. Science faculty get together to develop a series of authentic performance tasks such as designing, building, and launching a rocket. Students use AI to curate a portfolio of work on the tasks including blog posts, video transcripts, and spreadsheets. The AI produces data for each student that mirrors and exceeds the traditional assessment data. After two years, the state drops the formal assessment requirement.

The dream of educators is that assessment as a separate, invasive moment could disappear and instead be fully embedded in instruction. (Formative assessment, embedded in instruction, is an important part of learning and should not disappear, of course.) AI tools may bring that dream closer to reality.
An AI tool could have access to the complete corpus of a student’s work across multiple years of development. The tool could track a student’s growth with respect to state standards (and other competency-based dimensions such as creativity), providing both the student and their teachers with a much richer view of what they know and can do.
Initially, formal assessment will continue in order to provide ‘ground truth’ to calibrate the AI. Over time, the AI’s insights will become more valuable than those of a single, two-hour snapshot which often will not accurately represent what the student is capable of.
This ‘holistic’ approach also allows more authentic assessment — eg, performance tasks and real-world projects rather than multiple choice questions and essays.
Note that the approach will only work if the student has been assigned grade-level, rigorous work to evaluate.

Feedback on student work

Elementary school students in a class studying the run up to the Civil War write two pages summarizing their understanding of events and causes. They get feedback from an AI tool that helps them improve their essay across several dimensions: their argument (eg, do they cite evidence), the clarity of reasoning, their understanding of specific events, and the completeness of their work. They can see their essay becoming stronger on each dimension as they work.

Learners advance by means of feedback on their work that is (a) immediate, or close to it and (b) includes an opportunity for them to try again. Since this requires a great deal of teacher effort, students typically don’t receive the optimal amount of feedback. This has led to a proliferation of low-rigor exercises that can be automatically graded.
AI tools can generate feedback instantly and repeatedly including for high-rigor prompts such as making persuasive arguments and solving multi-part problems.
AI is especially good at language so feedback on writing (Wordtune, Grammerly) is already strong.
Automated feedback allows students to iterate: not just to answer and find out if they were correct but to revise and extend (Quill) until they have a high-quality response.
Feedback on short-answer questions across subjects is also already very good, though hallucinations sometimes occur.
Feedback on high-rigor, open-ended math problems is less advanced (Mathnet) since student work often takes the form of sketches and handwritten computations.

Identification of student thinking

A middle school class working on unit rate answers an exit ticket on paper, drawing diagrams, tables, number lines, solving long division problems, scrawling arrows connecting parts, crossing out and starting over. They take a photo of their work and an AI tool takes a few seconds to identify thinking, whatever solution path they take, and separates conceptual understanding from computational error in an instant report to the teacher.

The last 20 years of proliferation of machine-scored assessment have had the perhaps unintended consequence that students seldom encounter deeper, open-ended problems, especially in STEM subjects. This, in turn, puts the emphasis back onto procedural thinking, often just tricks students have memorized (flip-and-multiply) and away from conceptual understanding.
Initiatives like Mathnet are developing AI tools to do what teachers can do: look at a student’s written approach to an open-ended problem and identify (a) evidence of conceptual understanding, (b) gaps in understanding, © computational errors. In this analysis, judging the solution correct or requiring of student students a single, ‘official’ solution path is not as important as uncovering the student’s mathematical thinking.
As well as the pedagogical benefits of drawing-to-learn, tools could provide teachers with much greater insight into student thinking in a way that can inform both subsequent lessons and subsequent teaching of the same lesson.

Competency-based feedback (eg, collaboration, critical thinking)

A middle school teacher wants to improve student critical thinking. He uses an AI tool to identify that a segment on video game links to aggression in an upcoming lesson would be a good target. He has students analyze statements for and against the proposition with the help of an AI tool that reframes their causal explanations as questions — eg, “If one person played video games and was aggressive does it follow that everyone who plays violent video games will be aggressive?” Students reported that the AI guide improved their reasoning.

AI tools can help students build transferable competencies such as critical thinking, problem solving, generating creative solutions, understanding other perspectives, etc. Typical school learning experiences, focused on academic standards, may not offer students opportunities to practice and get feedback on competencies.
For instance, having an AI tool reframe feedback on causal explanations as questions has been shown to help improve critical thinking.
AI can take a lesson or unit outline and suggest which transferable competencies it affords practice on. The AI could then analyze student written work or presentations to generate feedback on those competencies.

Tracking student progress

A sixth-grade mathematics teacher gets a detailed report for a new class based on longitudinal data from elementary school. The report identifies critical precursor work, leveraging data on previous sixth-grade cohorts in the sixth grade curriculum. It takes into account predictions of summer loss based on prior data.

For any given learning experience, some students master it and others need more time. Teachers sometimes have red-yellow-green dashboards reflecting the fragmentation of a class day by day. But few teachers have time to pore over dashboards and even fewer have time to solve the knotty problem that is captured there.
Like an expert assistant, AI can synthesize data across diverse tools and assessments into the most critical, specific recommendations for a classroom. It can take into account which gaps must be addressed before moving on, and which can safely wait til later, when the curriculum spirals back or, if the choice has to be made due to lack of time, let go.
Given access to longitudinal data for a student, AI could detect patterns that are not visible in single assessments such as a student whose conceptual understanding is masked by persistent computational errors.
AI can explain areas that require targeted practice in terms the student themselves or a family member can understand and act on, expanding the amount of learning time beyond class.

Rubric generation with model answers

A high school history teacher has developed a performance task in which students curate a museum display for the Great Depression. In previous years, some students did not understand what they were asked to produce, even though the teacher thought they were capable of doing so. This year, the teachers uses AI to ingest the task description together with previous student work and suggest rubrics for the coherence of the exhibit and how well it reflects the key ideas of the Great Depression unit.

For complex, multi-faceted skills that do not lend themselves to a correct/incorrect judgment, students may struggle simply because they are not clear on the expected performance. Providing a rubric and model answers at different levels of performance is time consuming for a teacher, but easy for an AI tool.
This is especially useful for competency-based skills such as creative thinking, critical thinking, and communication.

Student Support

24/7 Tutor

An ELL middle schooler trailing in math knowledge is assigned Adam, an AI tutor that speaks his home language, Spanish. The student meets with the tutor three times per week for 45 minutes. The tutor has access to the main class curriculum and tailors topics to support grade-level work. The AI tutor is also available 24/7 on the student’s phone to help with independent work in class or at home.

One-to-one human tutoring is perhaps the most effective educational approach we have. But it is expensive. AI holds the promise of being the tutor in your pocket that isn’t just another drill-and-practice app. It feels like interacting with a real human tutor.
Today, AI tools are strongest at language. AI tutors for writing (Quill) are already here. Foreign language tutors are also available today (Duolingo, LangoTalk).
AI models are also strong at coding. Tools to support students learning to code (Replit) interact more as a copilot than a tutor.
Math is harder. AI is, strangely, better at conceptual math than procedures, and conceptual understanding is more important for learning, but tools have yet to take advantage of that. Science tutors are not yet arrived.
There is still much to solve: costs for tools like Khanmigo are prohibitively high, though certain to come down. And the user experience for so-called tutors often feels more like a treadmill than the trusting relationships that often develop with human tutors. For instance, they use a chat interface, in part because text-to-speech is still too slow to feel natural.
Most tools take a step-by-step tutoring approach, which is perhaps helpful for homework but denies the learner the chance to find their own pathway. No tools are yet designed for the intensive, three-times-per-week scheduled sessions that are most effective in human tutoring. That will be solved in time and AI tutors may go further: providing the kind of immersive worlds (eg, through VR) that are highly engaging.

Teachable agents

A high school student is assigned Martha, a teachable agent for a physics course. Martha asks a lot of questions, especially about everyday things such as objects “bending” as they are immersed in water. (Her interests are coordinated with the physics course syllabus.) It’s the students job to teach Martha and address her misconceptions. Martha gets confused when she finds inconsistencies but evolves and grows as she gains deeper understanding.

An AI tool could play the role of a learner that the student has to teach a given topic. Teaching something is one of the most effective ways of learning it. (See “teachable agents”, Dan Schwartz, Stanford.)
A variant on this is to have the AI tool can act as a Socratic questioner to deepen a student’s understanding.
A further variant is to teach non-playing characters (NPCs) in educational games to fulfill relevant quests, acting as teachable agents.

Support for students with special needs

A high school student diagnosed with ADHD had found it challenging to stay organized and focus on tasks at hand. Now he uses an AI assistant that can log in to and understand the school’s LMS. It helps him break down assignments into manageable tasks, plan towards deadlines and get reminders. The tool tracks his behaviors, offers suggestions for optimal work periods, and coordinates times when teachers are available to provide extra support. It incorporates game-like features that reward focus and task completion.

AI tools could be particularly effective at providing additional support to students with special needs throughout their education journey.
Tools could provide assistive writing support such as starter prompts, alternative input methods such as voice recognition, executive function support such as planning tools, visual support, and real-time classroom support such as text-to-speech and speech-to-text.
This is as well, of course, as personalized learning tools that adjust the pace of instruction, offer alternative explanations, and scaffold specific skills.

Mental health support

An adolescent student feeling social and academic pressures accesses an AI tool provided by her school to deal with anxiety and stress issues. The tool is an AI chatbot counselor, ready to listen 24/7, and programmed with cognitive-behavioral therapy techniques, providing immediate coping strategies and relaxation exercises. Chatting with the AI allows her to open up about her feelings, a significant first step in acknowledging and addressing her struggles. It monitors patterns indicative of heightened stress and provides her with early interventions, personalized resources including videos, and connections to local mental health professionals.

AI tools such as Woebot Health can provide always-available, anonymous mental health support to students who may be hesitant to reach out to a human counselor.
Tools can offer advice and support techniques. They can provide a non-judgmental space for students to discuss their feelings and emotions, practice social skills, and receive encouragement and motivation. They can facilitate connections between students who are going through similar experiences.
They can monitor patterns that might indicate declining mental health and recommend professional help or alert a designated support person.

College / career advisor

A high school senior aspiring to a degree in environmental science uses an AI tool to analyze her academic background, interests, and extracurricular activities and generate a list of colleges that match. It suggested a local dual-enrolment course that would help bolster her credentials. It also identified financial aid resources tailored to her and it helped her make her essays compelling and avoid common errors.

In many schools, students have only infrequent access to a counselor. AI could in some cases provide an alternative.
AI tools can provide students with tailored college and career advice, help identifying academic pathways, evaluate career options (eg, CareerDekho, Unschooler), provide job market insights and future skills demand that may be geography-specific, and recommend networking events and internships.
Tools can also help ensure that, for a desired college or career path, a student is taking the courses necessary to succeed.
And tools can support students in navigating the college application process, identifying scholarships and financial aid opportunities, and guidance on essays.

AI-supported student notebook

Students in an earth science class on natural hazards use an AI notebook to capture ideas as they encounter them. The notebook lets them highlight concepts in school texts and build their own collection of examples of hazards and the processes that cause them. They then use the notebook as a jumping-off point for research on technologies to monitor and predict hazards. They spend five minutes at the end of each class answering quick questions generated by the notebook on material they have curated.

Student notes are sometimes scattered and not revisited. AI-enhanced notebooks such as Google Tailwind allow students to upload their course documents, automatically generate notes and summaries, create illustrations, and suggest related ideas and resources. They can also connect ideas from different sections of the notebook, helping students deepen understanding.
Notebooks can also become more active, rather than passive collection bins, for instance generating flashcards and quizzes.

Social Tools

Small group facilitation

An elementary school teacher notices that some students do not make much progress on workbook problems. He uses AI tools to run small group instruction on comparing fractions (a topic he introduced today) for those students and finds that they are much more engaged discussing problems with each other than working in a book.

Small group instruction is very widely used in early reading and somewhat less frequently in math. A common problem is that only one group can work with a teacher at once and other groups may not be academically engaged.
AI tools such as Oko can manage a small group of students by monitoring video and recognizing speech so that students are engaged in a task chosen by the teacher -for instance, practicing skills introduced in a whole class lesson.
In the near future, tools will be able to engage in discourse directly with students, for instance directing a discussion on a topic while ensuring everyone contributes.

Discourse support tool for student groups

A group of students are working together to solve a puzzle in a simulated physics world. An AI tool follows their conversation. When one student asks it for help, instead of giving physics support it suggests how to improve their discourse and collaboration. It notes that they have a habit of pursuing non-productive suggestions by group members. It offers to alert them when they next do that. They return to the task in a more focused way.

An AI tool could follow the conversation of a student group and give on-demand advice on group collaboration. For instance, it could point out which group members’ ideas are not being tapped, or highlight that the group doesn’t follow through on directions they identify, or don’t seem clear on the problem they are solving.
Sidney D’Mello at the University of Colorado Boulder leads a team working on this use case.
The same approach could give domain-specific support to a group for instance clarifying terminology or offering a starting point or an alternative point-of-view.

Facilitated student discussion board

Students in a unit on Newton’s Laws read a paper and post their questions and confusions to an AI moderated discussion board. The AI facilitates a discussion in which new understandings surface.

An AI-facilitated discussion board could help students discuss questions, wonderings, confusing points, challenging problems, project ideas, connections with other topics, etc.
Students who do not always contribute in class may be very active in a discussion board and the asynchronous modality can encourage more thoughtful responses.

Facilitating whole class discussion

A middle school math teacher is using Illustrative Math. Students begin by working on a difficult problem that often surfaces misconceptions. An AI tool monitors student work on the problem and automatically creates a slide show of student examples together with the key points the teacher should highlight during a whole class discussion.”

An effective teaching strategy — known as productive struggle — is to have students work on a problem individually or in small groups and then facilitate a whole class discussion on what they found, guiding them towards an accurate and formalized (or perhaps more than one) solution. Many teachers find it challenging to orchestrate such discussions in real time and so may not capitalize on the value of this pedagogy.
An AI tool with access to each student’s work can quickly recognize common misconceptions, strengths, and computational slips and produce a step-by-step discussion guide that the teacher can follow right away. The guide is similar to a skilled teaching assistant who was able to follow every student’s thinking simultaneously.
The guide can suggest which student to call on, in what order, and could project student solutions from the teacher’s laptop.

Eyes Wide Open

We should be aware of the risks to students and educators as we explore the many positive possibilities of AI in K-12:

The Null Hypothesis. Most promising edtech interventions do not scale. In this past year, generative AI’s acceleration has seemed almost magical, but so did television, computers, the internet, and mobile — previous foundational technologies that became part of K-12 education but didn’t necessarily improve it.
Hallucinations. Preventing AI from making up facts and sources may prove to be difficult. Teachers are now assigning students to critically investigate AI output, but some may lack the media skills or background knowledge to do so successfully.
Atrophy of Critical Thinking. Even if AI resources become extremely accurate, using them mindlessly will bypass productive struggle and negate writing’s potential as a tool for deep thinking and self-expression. Calculators enabled students to avoid computation; will AI do the same for thinking? As science fiction writer Ted Chiang wrote about AI more generally, “the desire to get something without effort is the real problem.”
The Deluge. As the cost of creating content and digital tools approaches zero, the web is already becoming more flooded with books, lesson plans, flashcards, study guides, and videos of varying quality, making it more time-consuming for educators to select and align to research-backed pedagogy. Eventually, trusted curation, integration frameworks, and rollups into unified curricula may bring quality and coherence, but until then students and teachers may suffer from more cognitive load deciding across resources and tools.
Distraction. “Attention Is All You Lack.” AI has already been sneaking into most K-12 classrooms for the past few years, powering addictive, distracting TikTok and YouTube feeds. As entertainment engagement algorithms improve, the battle for attention will become more difficult and more critical.
Bias — a corollary of the loss of critical thinking skills. Students may be more susceptible to the biased output of many AI models.
Information Bubbles. Hyper-personalization could lead to students learning from a narrow set of sources that never force them to grapple with divergent values and experiences.
Dehumanization. (See Mitch Resnick and Jennifer Carolan’s warnings.) Schools that employ AI tutors may overlook the ways that teachers care for students, motivate them, and model what it is to be a healthy adult. Ideally AI will free up more educator time for human connection; to enhance the community at the heart of school, we’ll need a lodestar of learning that’s about more than skills and information transfer.