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So much is happening that you could be forgiven for deciding
to wait until the dust settles. But that would make you a spectator instead of
a participant in the greatest changes in K-12 education in our lifetimes.
Back in 2023, we mused over what that change might
ultimately look like:
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 regularly.
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.
If this map is anything to go by, like a great Robert Altman
movie, there are going to be a lot more characters before the story starts to
resolve.
(Thanks to everyone who alerted us to what we had missed.
Please don’t stop.)
Compiled by Laurence
Holt and Jacob Klein.
Laurence has spent the last two decades leading innovation teams in for-profit
and nonprofit K-12 organizations. Jacob is currently Head of Product at
TeachFX.
Download PDF here.
Teacher Practice Support
Note: A class of “do everything” tools is emerging,
combining several of the features listed here — for instance, MagicSchool.ai and Eduaide.ai—or allow you to
build your own — eg, Playlab.ai
Lesson generation
A teacher who wants to incorporate more writing into
sixth grade science uses an AI tool 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
such as Nolej, DiffIt, MyLessonPal, Copilot, teachology.ai, and
many others can generate lessons on any topic to order. Some will also
provide trappings such as quizzes, study guides, or perfectly laid-out
slide decks and handouts. Others, such as Curipod, let you deliver
the lesson as an interactive presentation.
- 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?”
- Tools
need to get better at (1) being guided by the teacher — eg, x minutes
of group work, y minutes of class discussion, etc—(2)
forming a coherent part of the learning experience by, for example,
understanding what has gone before and what is coming after, and (3)
producing high-quality lessons as measured by Rosenshine or an equivalent yardstick.
- There
is an opportunity for an AI tool that evaluates generated lessons along
these dimensions.
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,
including Dora
Demszky at Stanford and, with a focus on mathematics, Abhijit Suresh at
the University of Colorado, Boulder.
Teaching advisor
An elementary school teacher finds that a subset of his
class did not understand negative numbers and suspects his curriculum’s
procedural approach is the problem. He uses AI to evaluate his current lesson
plan and students’ responses to in-class checks-for-understanding. The AI
advisor connects him with like-minded teachers, recommends articles and
research papers, and facilitates a discussion that includes an AI
subject-matter expert. He hits on a new, more conceptually grounded approach to
negative numbers.
- Tools
such as EduGPT are
beginning to emerge to play the role of a teaching advisor, or multiple
advisors, for different subjects and topics.
- 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.
- Other
tools including TeachingLab.ai can
ingest an existing lesson and advise the teacher on improvements to it,
such as changes of pace, embedded checks for understanding, scaffolds to
ensure foundational skills are in place, and connections to concepts
students have already learned.
- AI-powered
collaboration tools can inject new life into online Professional Learning
Communities (PLCs) that otherwise often don’t attract a critical mass of
educators. AI can understand each teacher’s focus and challenges, match
them with similar educators, and inject relevant research and blog posts
into the conversation.
Classroom management simulator (new)
A first-year middle school teacher realizes he needs to
hone his classroom management skills. He spends his evenings working through a
series of simulated classroom scenarios in which AI role plays students. The AI
can recreate situations and student personalities very similar to what he faces
during the day. He gets to try out different approaches and gets expert advice
he can try out, all in a low-stakes environment.
- Simulators
are increasingly widely used for training pilots, surgeons, and even CEOs; why not teachers? David Weston of the Teacher Development Trust in
the UK is working on exactly that.
- Future
versions could employ AI agents to role-play specific student
personalities that then interact with each other and with the (human)
teacher in real time.
- They
will also incorporate expert advice in various approaches including
specific behavior management, mentoring, and motivational techniques.
Competency-based teaching
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. They are provided with a rubric for the competencies and examples of
what proficiency looks like. An AI tool sends relevant snippets to the teacher
to support competency-based feedback later.
- Teachers
are unfamiliar with creating lessons that include opportunities for
students to learn transferable competencies in context—eg, building
empathy in an ELA unit on refugees or improving group work in a math
lesson.
- AI
tools could take a lesson or unit outline and suggest which competencies
it affords practice on. It could then help teachers embed
competency-specific activities in the lesson and create a rubric and
example student work as a guide.
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 such
as Project Leo 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 ©
alert the teacher when individual students need support.
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 grade-level team to optimize instruction and to
re-assign students to intervention groups that fit them better.
- Schools
collect a lot of data but make use of only a small part of it. AI can
ingest student data (eg, in CSV format or via API from an SMS, LMS, or
proprietary app) and perform analyses on it, suggesting optimal student
grouping, focus areas, and generating other insights.
- Tools
that can perform analysis—including sophisticated statistical methods
suggested by the tool itself—and generate data visualizations include
OpenAI’s Advanced Data Analysis and Fluent. Tools that
understand education data specifically, such as Doowii and Strived.io, are beginning
to emerge.
- Tools
allow multiple level of analysis, for example: you could upload NWEA MAP
data from several cohorts and ask the tool to identify areas with the
strongest and weakest growth; ask higher-level questions such as an analysis
of summer loss or whether a tutoring initiative made a difference; 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 and progress reports to colleagues and parents,
responding to parent inquiries, inputting grades to the SIS, creating
report cards, 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 such as Elicit, Humata, Scite, Consensus, and Genei can find
relevant papers, summarize them, and answer questions as if they were the
paper’s author.
- They
may soon 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.
Family connections (new)
A parent, worried about their child’s learning loss
during the pandemic, is trying to make sense of school report data. They click
on a chatbot icon alongside the report and begin an extended back-and-forth,
digging into data from a recent reading assessment, understanding their child’s
results, and getting suggested home activities. The chatbot even tells them
about an after-school program they qualify for and how to enroll. The entire
conversation is in Spanish.
- Families
have a tough time getting the most out of school ecosystems: the data,
available resources, options, how to advocate for their children, etc.
- Existing
platforms that help schools communicate with families, such as AllHere, and startups
such as Paloma,
are providing AI chatbots with access to a knowledge base assembled by the
school district together with curated resources (often resources that
exist but are underused today).
Team teaching with AI (new)
In a high school biology classroom, three educators — Mr
Smith, Ms Johnson, and an advanced AI model — collaborate to deliver a lesson
on the human heart’s structure and function. Mr Smith focuses on anatomy,
beginning with a 3D model. Ms Johnson, specializing in physiology, takes
students in groups through a hands-on activity simulating blood circulation
using colored water and a model heart. The AI model’s role is formative
assessment, checking for understanding and giving feedback on students’
self-explanations. It suggests real-time adjustments to the other teachers.
- One
way to think about in-class AI, according to Jean-Claude Brizard of
Digital Promise, is as “team teaching, with AI on the team.” AI tools can
play one of several roles: delivering first instruction, checking for
understanding, dealing with misconceptions, scaffolding and supporting
students during practice, etc.
- To
be an effective member of the team, and for the enacted lesson to be
coherent, the AI tool must have been given sufficient context: the purpose
of the lesson, the lesson plan, perhaps data on student background
knowledge, precursor skills, or interests.
Para Practitioners (new)
An elementary school paraprofessional, supporting
teachers in early reading, is trained to use specially designed AI tools to
take on more responsibilities. She interprets early reading data, decides which
students should receive additional support, helps plan lessons, and co-teaches
the ELA block every morning alongside the classroom teacher.
- There
is now a well-documented effect of AI tools on the performance of workers
in programming, customer service, managers’
writing, and even management consulting. The consistent finding is that
lower-skilled workers improve most, in some cases matching the performance
of experts.
- Can
the same thing happen in education? Could access to AI capabilities allow
paraprofessionals to take on some of the responsibilities of full
teachers? There is precedent in healthcare where nurses can become nurse
practitioners, able to interpret test results, diagnose, and prescribe,
under the supervision of a doctor.
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.
- Apps
like Teaching
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). AI tools can also play one of the roles. For instance, Mizou and the
enormously popular character.ai let you go on a quest with Einstein
or interview Napoleon.
- 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. Tools
such as Mindjoy—which
lets you create STEM AI-based tutors—can generate materials to support
flipping 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 rewrite 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.
- A
big area of growth in 2024, as AI video-generation becomes real, will be
creating animations, YouTube-like videos (Prof Jim), or
augmented reality simulations (Ludenso) 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_, Conker, Formative, QuestionWell, Mindgrasp, Quiz Makito, WorksheetsAI and
many others can take a content area and generate questions together with
rubrics and model answers.
- Popular
classroom response tools such as Kahoot and Quizizz have
added the ability to generate questions with AI, though they are still at
the stage where you should check the accuracy of questions before using
them.
- Tools
can generate a variety of questions: multiple choice, short answer, essay
prompts, exit tickets, etc.
- EdPuzzle can
generate questions for a video. Students encounter the questions as they
watch.
- Some
are able to generate questions customized to student interests
and—soon—different levels of challenge, different levels of Bloom’s
taxonomy, open-ended Fermi
problems, and mini-projects.
- (Importantly,
they can also automatically grade these types of problems and give
students detailed feedback. See Feedback on student work, below.)
ESL student content
An elementary school teacher uses an AI tool daily to
create content specifically to augment her existing lessons for the ESL
students in her class. The tool knows each student’s home language and
traditions so it can ingest any lesson and build supports such as sentence
stems, translations of vocabulary words, explanations of background knowledge,
etc.
- Some
core curricula include specific supports for students learning English as
a Second Language, but they are often uninspiring or missing altogether.
An AI tool such as Twee or Speakable can
fill the gap.
- AI
language models can “speak” dozens of languages but, unlike existing
translators, they can combine that ability with context such as the lesson
being taught and, perhaps, knowledge of how best to support ESL students
at different levels.
- Providing
tools to support different student populations—a requirement often
enshrined in state frameworks but difficult to enact in class—is a
use-case that seems likely to grow.
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 like Sizzle can
generate worked examples (both correct and incorrect — students identify
the misstep) based on a problem you take a photo of. 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. Podsie, for instance,
applies the method to classroom content, often learned and forgotten
quickly. Kinnu does
it for curated topics.
- Flashcard
apps like Quizlet and Anki,
new AI-first flashcard apps such as Gizmo and Wisdolia, 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.
Culturally responsive content (new)
In a high school math class, students engage with
statistics using a lesson adapted by AI to examine real-world datasets on
racial profiling in neighborhoods, including their own, alongside other
community datasets that spotlight issues of justice and injustice. For
instance, students analyze traffic stop data, comparing the frequency of stops
by race and gender and the outcomes of these encounters.
- There
is evidence that adapting lessons to incorporate
culturally responsive content—both window and mirror—by centering students’ customs,
experience, and perspectives, can improve engagement and learning.
- Emerging
tools from Reconstruction Onyx and Planning Period use
AI to offer teachers help in revising lessons and activities to be based
on frames suggested by the teacher—or by students (or student interests
gleaned by AI from their work). Tools could also suggest culturally
responsive ways to approach a topic such as statistics or genetics.
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 LLM plug-ins like Show
Me and apps like Algor and Heuristica, AI tools are already capable of rendering
diagrams, such as concept maps or graphic organizers 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.
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. Their teacher “tunes” the feedback to match his own
style, for instance, saying “provide evidence” rather than “citations”.
- 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. Even when
they do, they may check their grade and ignore the 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 (Grammarly, Ethiqly, Pressto, Writable, Class Companion, Vexis, CoGrader) is already
strong.
- Some
tools (Brisk)
that can grade across subjects, drop feedback directly into a Google doc
essay. Others (AutoMark)
let you upload a specific rubric for the AI to use. Still others (EnlightenAI) learn to
mimic the teacher’s feedback style. Tools vary on whether teachers must
first review feedback before students see it.
- 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 (sAInaptic), though
hallucinations sometimes occur. Recent
research has shown AI to be as good as humans at grading student
short answers to reading comprehension questions.
- Feedback
on high-rigor, open-ended math problems is less advanced (Mathnet) since student work often takes the form of
sketches and handwritten computations.
- Tools
can also evaluate student self-explanations (Snorkl), a powerful research-based learning technique.
For instance, after working in a simulation to produce proteins in a cell,
students could talk aloud about what they just did, get an automatic
transcription from Whisper, and instant feedback from an AI including
terminology such as transcription versus translation, and a mnemonic (‘c’
comes before ‘l’).
- More
frequent feedback may allow teachers to separate it from grading. This can
be beneficial because (a) students tend to focus on the grade and ignore
the feedback and (b) teachers may provide feedback more as a justification
for the grade than as a vehicle for improvement.
- A
future goal for feedback tools is to shift student- (and teacher-)
thinking even further: from what-you-need-to-improve toward a process in
which students seek and use feedback as a habit and that incorporates peer
feedback (giving and receiving) and self-reflection (see, for
example, Floop).
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, (c) 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.
- We
have yet to see tools that evaluate student thinking based on having them
draw, for instance an animal cell. There are pedagogical benefits of drawing-to-learn.
- Sorcerer is
a tool in beta that engages students in a dialogue on a topic and
gradually pushes them towards deeper conceptual understanding. It, or
similar tools, could reveal 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.
- Tools
such as NXTLVL 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
tools could analyze student written work or presentations to generate
feedback on specific 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.
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, such as StudyHall, 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.
Interest-based networks (new)
A high school student has begun to build a following for
her video channel on pre-Columbian cultures. But she realizes she has a lot to
learn on topics as diverse as video editing, rights management, writing
powerful headlines, and social media. Her school doesn’t offer any of those but
she joins an online community designed to help her build her network of
like-minded entrepreneurs and experts.
- A
growing number of young people develop a passion for learning, but not for
traditional school topics. They want to build a following online (and
perhaps earn money from it), a video game, a music album, a new way to
learn a language, interactive stories, or an animal sanctuary.
- Communities
like buildspace are
developing AI tools to support these learners, for instance matching them
with like-minded network members and bringing aggregating demand for
outside experts.
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, StoryBird.ai, Caktus, StorySeed)
are already here. Foreign language tutors are also available today (Duolingo, LangoTalk, Iago, Supernova).
- AI
models are also strong at coding. Tools to support students learning to
code (CodeSignal Learn, 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. Chatbot science
tutors have not yet arrived.
- There
is still much to solve: costs for tools like Khanmigo are
prohibitively high, though certain to come down—CK-12’s Flexi is
less powerful but free. And the user experience for so-called tutors often
feels more like a treadmill than the trusting relationships that can
develop with human tutors. For instance, they use a text chat interface,
in part because text-to-speech is still too slow to feel natural.
- Existing
tools in the “homework help” category—such as Brainly, CourseHero, Project Chiron, Studdy, CheggMate, Symbolab, and many more—practice
apps like edia and
test prep, such as r.test and Archer, offer
step-by-step solutions (and sometimes access to a live human) but are not
yet close to an authentic tutoring experience since they deny the learner
a 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) and
narrative-based learning scenarios (eg, EngageAI Institute) that
are highly engaging and better reflect the real world.
- Avatar-based
tools are beginning to emerge, such as Kyron which
lets teachers create a tutor from video of themselves, so your students
need never be without your dulcet tones.
- Chatbots
can’t draw or see, so they lack the ability to respond to a student
sketch, which makes many topics challenging. This is likely to change in
2024 since multi-modality is a central focus of innovation in AI models.
Early reading coach (new)
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 can either print the books or have children read them aloud
with instant feedback from the tool.
- 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 LitLab, Project Read,
and Storywizard.ai can
identify suitable texts in the classroom library or generate new texts
that fit. 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.
- As
speech-to-text capabilities have dramatically improved, a gaggle of tools
such as Microsoft
Reading Coach, Ello, Edsoma, and Amira have
incorporated the ability to listen to a student read, give real-time
feedback, and build a learning path based on the science of reading.
- Children
can customize characters, put themselves in the story (NeonWild), choose how
the plot unfolds (learning about story structure), and even change
illustration styles.
Curiosity coach (new)
A student has just finished a classroom lesson on radio
transmission. She is curious why antennas broadcast signals but wires in DC
circuits do not. She launches her knowledge explorer app and asks. That leads
to questions about frequency: radio-frequency waves travel long distances, but
others do not. Why? As she continues, the app creates an illustrated concept
map of where she has been and adds suggestions for other directions. She winds
up, by way of gamma rays, at black holes, linking up with an exploration from
two weeks ago that ended in the same place. She was going to have to look more
into black holes.
- There
is little time in the life of a K-12 student to be curious, to explore on
your own. The world of knowledge has been carefully curated for you.
Occasionally, an adventurous teacher will notice an interest and suggest a
resource; something that isn’t covered in the state standards. AI tools
will excel at doing the same: they already have memories that far exceed
any human.
- HelloWonder is
aimed at the curiosity of little kids, though its current form is a safe
browser with a chatbot. Curio and Moxie are similar but in the form of a
voice-enabled robot. Portola, also for little kids, focuses more on
creativity.
- Some
early attempts ath a true curiosity coach, such as SocratiQ, a sort of Miro +
Wikipedia, show promise. They feel rather like wandering the halls of a
large library, or perhaps the Young Lady’s Illustrated Primer in
Stephenson’s The Diamond Age.
- They
could also become social tools—connecting you with kids your age
interested in the same topics (see Interest-based networks,
above)—spark motivation for extended research or a new project that could
become a life-long passion, feed summaries to parents, teachers and
experts so they can extend your curiosity, and connect you with
certifications and experiences that build toward a career or induct you
into the “World of X”, whatever your X is.
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 student’s
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. Goblin.tools,
for example, uses AI to help neurodivergent people with tasks such as
writing a book report, which it can break down into a sequence of simpler
steps.
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 and Koko can provide always-available, anonymous
mental health support to students who may be hesitant to reach out to a
human counselor.
- AI-powered
chatbot tools such as Edsights and Mainstay 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.
- Tools
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 become a tattoo artist
has always thought of it as art. But she encounters an AI tool one day that
helps her see what specific credentials she will need including, for instance,
an understanding of infection. She suddenly develops an interest in biology
that shocks—and delights—her teacher. The tool is able to recommend
extracurricular activities and suggests a local internship to help bolster her
credentials and introduce her to like-minded people.
- 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, Coach), 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,
help with essay writing (ESAI),
identifying scholarships and financial aid opportunities, and guidance on
essays.
Smart student portfolio (new)
A high school student installs a new app that allows her
to send copies of her work in every course to a digital portfolio she controls.
Using the app in an earth science class on natural hazards, she can also
capture notes, add highlights, and build her own collection of hazards to use
as a jumping-off point for a project on researching hazard prediction
technologies. The app employs AI to help her track progress toward her college
application, identify gaps and help her fill them, celebrate her progress over
time, and curate her best work to share as she chooses.
- An
increasing amount of student work, especially in middle and high school,
is digital. But it is often stored in siloed systems and quickly
forgotten. An AI tool could capture it all and begin to build a portfolio,
not just to show off the best work, but to document the student’s journey
through knowledge structures.
- Students
could see their journey, and their growth, more coherently. The tool might
help them uncover recurring patterns and trajectories on which to build.
- Students
could add their own notes and sources—as tools such as Google’s NotebookLM are
beginning to do—ask questions, discover related ideas and resources, and
connect ideas from different areas in order to deepen understanding.
- Though
recorded lectures are not common in high school, as they are in college,
especially since mobile phones are ever less welcome, tools like Jamworks may begin
to help students mark, learn, and inwardly digest each lesson.
- The
portfolio could also become a source of holistic formative assessment
(see Holistic assessment, above), generating an evaluation of
their work without the need for a set-piece test.
Meta-cognitive support (new)
A high schooler realizes, to get where he aims to get, he
needs to improve his self management. Lacking someone in his life to mentor
him, he turns to an AI tool to help him assess his current situation, decide
what’s important, and learn skills to make a plan and monitor his way toward
it. The tool assesses his study habits, for instance, and sets weekly goals
that start easy to build his confidence. He learns how to manage his assignment
load and how better to review and retain new knowledge.
- There
are a set of meta-cognitive skills that are essential to academic success
and yet are seldom addressed directly in school. They include
self-awareness, growth mind-set, seeking and absorbing feedback,
identifying goals and making plans to achieve them, monitoring performance
toward them, persisting in their pursuit, and more. (See, for
example, XQ Institute’s Competency framework.)
- AI
tools are emerging to support students by understanding their specific
context and making gradual, actionable recommendations, like an empathetic
mentor. An early example is SchoolAI’s “Explore Spaces” that incorporate
study plans and exam-taking strategies. More such supports will grow out
of to-do-list managers and study tools.
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 current 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.
Laurence Holt
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