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Student reading a picture book aloud while voice AI on a tablet listens and visual AI scans the page in a K-12 classroom
Artificial IntelligenceK-12

How Voice and Visual AI Are Transforming K-12 Classrooms

by EdCircuit Staff
written by EdCircuit Staff 9 minutes read

For most of the past few years, the conversation about artificial intelligence in schools has been a conversation about text: students typing prompts, chatbots writing essays, teachers wondering what to do about it. A quieter change has been happening alongside it. AI tools that listen and AI tools that see are now part of the school day, often in places where the typed chatbot never reached.

A first grader reads a short story aloud to a tablet, and software marks which words she stumbled on. A student who is blind points a phone at a worksheet and hears it read back. A science teacher photographs a student’s hand-drawn diagram and asks an AI model what the student seems to misunderstand. Each of these uses a different kind of AI than the chatbot, and each raises its own questions about evidence, accuracy, and student privacy.

This article looks at what voice and visual AI actually do in K-12 classrooms today, what the research supports, where the tools fall short, and what district and school leaders should check before adopting them.

What “voice AI” means in a classroom

Voice AI in schools mostly means automatic speech recognition: software that turns spoken words into data it can compare against a text or a task. The most established classroom use is reading practice.

Reading tutors built on speech recognition listen as a child reads aloud, follow along word by word, and respond when the child hesitates or misreads, much as a patient adult would. The idea is not new. Amira Learning, one of the better-known products, traces its foundations to Carnegie Mellon University’s Project LISTEN, a research program that spent years building a computer tutor that listens to children read.

What has changed is scale and cost. Google released Read Along, a free reading app for young children, in 2020. Google said at launch that the Android app processes the child’s speech on the device itself, works offline once stories are downloaded, and does not require an account. In 2022 the company added a web version that runs its speech recognition in the browser.

Beyond reading, voice tools appear in quieter ways: dictation for students who struggle to write by hand, spoken directions for younger children who cannot yet read them, and language practice tools that listen to pronunciation.

What the evidence shows, and what it does not

School leaders should be careful with the research behind voice tools, because much of it comes from the companies that sell them.

The Florida Center for Reading Research reviewed Amira and reported that it is rated “Promising” by Evidence for ESSA for grades K through 5. In the study behind that rating, students who used the tutor scored significantly higher on a standardized reading test than students who did not, with an effect size of +0.64. The center also noted the limits: the sample was small and came from a single affluent, largely white suburban district.

That is a meaningful result, and it is also a narrow one. Larger claims, such as a tutor matching human tutoring after a set number of sessions, appear in vendor and publisher materials, and the underlying studies are not always easy to examine. A district does not need to dismiss those claims, but it should ask for the full studies, look at who was in them, and compare those students with its own.

Where speech recognition struggles

Children are harder for speech recognition than adults. Researchers point to children’s shorter vocal tracts, their slower and more variable speaking rate, and pronunciation that is still developing.

The gap can be large. Researchers at the University of Colorado’s AI institute reported that a model trained on elementary school speech had a word error rate of about 9 percent on children of the same age, but about 46 percent when it was tested on ninth graders. A tool tuned for one age group cannot be assumed to work for another.

Accent matters as well. A study commissioned by The Washington Post found that popular smart speakers were about 30 percent less likely to understand people with non-American accents. In a classroom, that kind of error does not stay inside the software. If a reading tool marks a correctly read word as wrong because of a child’s accent, the child, and possibly the teacher, may conclude the child read it wrong.

The practical lesson for schools is to test a voice tool with their own students before relying on its scores, especially for English learners, students with speech or language needs, and the youngest readers, and to treat its output as one piece of information rather than a verdict.

What “visual AI” means in a classroom

Visual AI covers tools that interpret images: photos, scanned pages, handwriting, diagrams, and live camera views. Two uses stand out in schools.

The first is accessibility. Microsoft’s free Seeing AI app reads printed text and handwriting aloud and describes images for people who are blind or have low vision. Be My Eyes, an app long used to connect blind users with sighted volunteers, added an AI feature, Be My AI, that describes photos and reads labels and documents. For a student with a visual impairment, these tools can turn a paper handout, a poster on the classroom wall, or an image in a slide into something they can use, without waiting for an accessible version to be prepared. Schools should note the age terms: Seeing AI’s terms require users to be at least 13.

The second is feedback on student work. Newer AI models can take an image and text together, which means a teacher can share a photo of a student’s graph, lab sketch, or worked math problem and ask what it shows. Research here is early. A 2024 research dataset of high school physics, chemistry, and biology answers that included images found that some models judged the correctness of answers and the relevance of the images with impressive accuracy, but the student responses in it were synthetic, not real student work. A 2025 study that asked K-12 teachers about these tools noted that much of the field still reflects what researchers think the opportunities are, rather than what teachers and students need. The National Science Foundation is funding work on using these models for feedback in formative science assessment.

A third use deserves a separate word of caution. Some research and some products apply image recognition to students themselves, estimating attention or engagement from classroom video. That is a different matter from describing a worksheet. It involves continuous monitoring of children, and districts should treat it with the care they would give any camera-based surveillance.

The privacy rules have changed

Voice and visual tools collect a kind of data that text tools usually do not: recordings of children’s voices and images of children and their work. Federal rules now speak to that directly.

The Federal Trade Commission’s amended Children’s Online Privacy Protection Act (COPPA) rule was published in April 2025, and companies had until April 22, 2026 to comply with most of it. The amended rule adds biometric identifiers, including voiceprints, to the definition of personal information. For apps that serve children under 13, that brings voice data clearly inside the rule.

The Family Educational Rights and Privacy Act (FERPA) still governs student education records, and voice recordings or images tied to a student can fall within it, depending on how they are kept and used. When the U.S. Department of Education issued guidance in July 2025 on using federal grant funds for AI, it listed AI-supported tutoring and AI-based instructional materials among the allowable uses, and it was explicit that those uses must comply with federal privacy law, including FERPA.

That guidance followed an April 2025 executive order, “Advancing Artificial Intelligence Education for American Youth.” Together they signal federal support for classroom AI, paired with an expectation that schools protect student data while they use it.

Questions to ask before adopting a voice or visual AI tool

The same questions that apply to any AI purchase apply here, with a few that are specific to audio and images. edCircuit’s AI procurement questions for schools cover the general ground. For voice and visual tools, leaders should also ask:

  • Where is the audio or image processed? On the device, in the browser, or on the company’s servers? Processing on the device keeps recordings off outside systems entirely.
  • Is anything stored, and for how long? Ask whether recordings and images are kept, who can access them, and how they are deleted.
  • Are students’ voices or images used to train the company’s models? The answer should be in the contract, not only the marketing.
  • Who was the tool tested with? Ask for accuracy results by age, by accent and home language, and for students with speech or visual disabilities.
  • What does the teacher see? A good tool shows the teacher what it heard or saw, so an adult can check the result before it affects a grade or a placement.
  • Does it meet the amended COPPA rule and FERPA? Especially for students under 13, and especially where voiceprints or images are involved.

What this means for schools

Voice and visual AI can do things that matter for students who are often underserved: the early reader who needs far more read-aloud practice than one teacher can listen to, the student who is blind and needs printed material now rather than next week, the student who can explain an idea far better than they can write it down.

Those benefits are real, and so are the limits. Speech recognition is least reliable for exactly the students it often aims to help, and research on image-based feedback is still young. The tools collect sensitive data, and federal rules now treat a child’s voice as personal information.

The districts that get the most from these tools will be the ones that adopt them deliberately: tested with their own students, used to support a teacher’s judgment rather than replace it, and covered by an AI policy that has kept up with technology that now listens and sees. If a district’s guidance still treats AI as a typing tool, it may be time to revisit that AI policy. For a broader view of where artificial intelligence fits in teaching and learning, see edCircuit’s AI in education guide.

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Transforming student media with AI: a newspaper, microphone, video camera, play button and phone each dissolve into glowing particles that become the next
Artificial IntelligenceK-12Hot Topics - controversial

How AI Is Transforming Student Media Programs

by EdCircuit Staff
written by EdCircuit Staff 5 minutes read

How AI is transforming student media programs is one of the most important shifts happening in education today, as students learn to create content faster while also learning when and how to use these tools responsibly.

Walk into a high school media classroom today, and you will still see cameras, microphones, editing stations, and students working in teams. But look a little closer, and something new is happening.

Artificial intelligence is quietly becoming part of the workflow.

In one classroom, a student records an interview, uploads the audio, and within seconds has a full transcript ready for editing. Another student generates captions for a video before it is even exported. At the same time, a third uses AI tools to clean audio and prepare the final cut—all before the end of the class period.

For many students, AI is not just another tool. It is the first time they realize how quickly they can turn an idea into something real.

AI as a Creative Assistant

In many classrooms, AI is not replacing student work; it is supporting it.

Students use AI tools to transcribe interviews in seconds, turning recorded conversations into editable text. What once took hours can now be done almost instantly. That allows students to spend more time shaping the story rather than simply organizing it.

They also use AI to generate captions for videos, making content more accessible to a wider audience. For schools that serve diverse communities, translation tools are helping students reach viewers who speak different languages.

Audio tools powered by AI can clean up background noise, enhance voice clarity, and improve overall sound quality. Video tools can assist with identifying clips, organizing timelines, and speeding up editing workflows. AI can also support script development, helping students brainstorm ideas, refine structure, and experiment with tone.

These tools are not doing the creative thinking for students.

They are giving students more time to focus on it.

Speed vs. Responsibility

With AI, content can be created faster than ever before.

But speed introduces new challenges.

Students must learn that just because something can be published quickly does not mean it should be. Accuracy, context, and fairness still matter. In some ways, they matter even more.

A caption generated by AI still needs to be reviewed. A transcription still needs to be checked. A script still needs human judgment. Students must ask important questions:

Is this accurate?
Is this fair?
Does this represent the story correctly?
Should this be shared at all?

These are not technical questions.

They are ethical ones.

Media classrooms are becoming spaces where students learn to slow down, think critically, and take responsibility for what they create.

AI and the Evolution of Storytelling

AI is also influencing how stories are told.

Students can now experiment with new formats and approaches. They can quickly test different versions of a video, explore alternative headlines, or adjust pacing and tone with greater flexibility. AI can assist with brainstorming ideas or organizing content, helping students move from concept to execution more efficiently.

But storytelling itself remains deeply human.

The best stories still come from curiosity, empathy, and the ability to ask meaningful questions. AI can help structure a story, but it cannot replace the experience of sitting down with someone, listening carefully, and understanding what matters.

That balance is what media programs are teaching.

Use the tools.
But do not lose the story.

Preparing Students for an AI-Driven World

For today’s students, AI is not a future concept.

It is part of their present.

By learning how to use these tools in a structured environment, students gain an advantage. They are not just consumers of AI—they are learning how to use it thoughtfully and effectively.

This preparation extends beyond media careers.

Students who understand AI will carry that knowledge into business, healthcare, education, engineering, and countless other fields. They will be better equipped to evaluate information, question outputs, and make informed decisions.

In a world where AI-generated content is becoming more common, that ability is critical.

The Role of the Teacher Has Evolved

As AI enters the classroom, the role of the media teacher is evolving as well.

Teachers are no longer just instructors of technical skills. They are guides who help students navigate a rapidly changing landscape. They help students understand when to use AI, when not to use it, and how to maintain authenticity in their work.

They also create space for conversations about ethics.

What happens when AI generates incorrect information?
How do we verify sources?
What does originality mean in an AI-assisted world?
How do we give credit when tools are involved?

These are complex questions, and students need support in exploring them.

A New Layer of Media Literacy

The rise of AI has added a new dimension to media literacy.

Students are not only learning how to create content—they are learning how to evaluate it.

They must consider whether something was created by a human or generated by AI. They must think about bias, accuracy, and intent. They must understand how algorithms influence what people see and how quickly information spreads.

This awareness is essential.

Students are not just the next generation of content creators.

They are the first line of defense in a world filled with information.

Opportunity, Not Replacement

There is often concern that AI will replace human creativity.

Inside high school media classrooms, the opposite is happening.

AI is expanding what students can do.

It allows them to experiment more, produce more, and refine their work more efficiently. It gives them tools that were once only available in professional environments. It opens doors to new forms of storytelling and communication.

But the most important skills remain unchanged.

Creativity.
Curiosity.
Judgment.
Ethics.
Voice.

Those cannot be automated.

The Future Is Already Here

Artificial intelligence will continue to evolve, and so will the way students use it.

New tools will emerge. Workflows will change. Expectations will shift.

But the goal of media education will remain the same: helping students communicate clearly, think critically, and tell stories that matter.

They are not waiting to see how AI will shape the future.

They are already learning how to use it—and how to lead with it.

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AI and student learning measured: analytics charts drawn in glowing blue light on a classroom blackboard
Hot Topics - controversialArtificial IntelligenceK-12

AI and Student Learning: How Schools Should Measure It

by EdCircuit Staff
written by EdCircuit Staff 10 minutes read

AI and student learning are supposed to be the focus of the school board presentation.

Instead, the first slides show how many accounts have been activated, how often the new platform is used, and how many lessons, quizzes, summaries, and tutoring sessions it has generated. Another slide estimates that teachers have saved hundreds of hours.

The numbers look impressive.

Then a board member asks the question that is missing from the presentation:

“Are students learning more?”

The room goes quiet.

The district knows how many people logged in and which features they used. It may even know how much time teachers believe they saved. What it cannot clearly explain is whether students understand more, think more deeply, work more independently, or receive stronger instruction because the technology is there.

That is the measurement problem schools now face.

AI activity is easy to count. Educational value is harder to establish.

As districts move from experimentation to long-term implementation, enthusiasm must be matched by evidence. The central question cannot be whether AI is being used. It must be whether the technology is improving teaching and learning—and at what cost.

Start With the Question, Not the Dashboard

Usage data has a natural appeal. It is immediate, precise, and easy to place in a report.

A district can count active users, prompts, completed tutoring sessions, and minutes spent on a platform. Those numbers help leaders understand whether accounts work, whether training reached the intended staff, and whether people are using the features the district purchased.

They do not demonstrate learning.

A student can complete fifty tutoring sessions without mastering the targeted skill. A teacher can generate dozens of lesson plans that still require significant correction. A writing assistant can produce more polished essays while students become less capable of drafting independently.

According to National Center for Education Statistics data released in 2025, 73 percent of public schools reported that at least some teachers were using AI for lesson planning, administration, instructional materials, assessments, grading, or feedback. Yet only 38 percent of school leaders agreed that integrating AI would lead to better educational outcomes for students.

That gap matters. Schools are already using the technology, but its educational value remains unsettled.

A 2026 resource from Regional Educational Laboratory Central similarly notes that research has not established which teacher uses of AI translate into improved student learning outcomes.

Districts should not interpret that uncertainty as a reason to avoid every AI tool. They should treat it as a reason to define success before implementation.

Suppose a district adopts an AI tutoring system for middle school mathematics. “Improving math” is too broad to evaluate. Leaders need to identify which students will use the tool, which skills it is expected to strengthen, how teachers will use the information, and what improvement should be visible.

Will students solve more multistep problems correctly? Can they explain their reasoning? Will they retain the skill after the tutoring support is removed? Can they apply what they learned to an unfamiliar problem?

Those questions point toward evidence of learning. Account activations do not.

Without clear goals, success is easily redefined after the fact. A platform purchased to improve achievement may later be defended because students enjoyed using it. A tool intended to save time may be called successful because it produced more materials.

Enjoyment and productivity may be worthwhile. They are not interchangeable with learning.

Efficiency Matters—But So Does What Students Can Do

Teacher time is valuable.

If AI can reduce repetitive administrative work, provide a usable first draft, or make routine communication more efficient, districts should measure that benefit. Time returned to instruction, feedback, collaboration, or individual student support has real value.

But a tool that saves a teacher twenty minutes has not automatically improved a student’s education.

Leaders need to understand what happened during those twenty minutes. Did the teacher provide more feedback? Meet with a struggling student? Strengthen the lesson? Or did the time disappear into correcting errors the AI introduced elsewhere?

Efficiency measurements should include time added as well as time saved.

A quiz may be generated in five minutes but require twenty minutes of fact-checking and revision. A counselor may receive a faster summary of student information but need additional time to verify what was omitted. A translated family message may appear instantly, yet someone still needs to confirm that it is accurate and appropriate.

During a pilot, participating educators can track a small sample of tasks before and after using the tool. How long did the work take? How much correction was required? Was the final result stronger, weaker, or essentially the same?

The same care is needed when examining student work.

AI can improve the appearance of an assignment without strengthening the thinking behind it. An essay may sound more polished. A science response may use advanced vocabulary. A presentation may be better organized. None of those changes proves that the student understands more.

For AI-supported writing, teachers might compare a student’s ability to plan, draft, revise, and explain key choices. An independent writing sample can reveal whether those skills transfer when the technology is unavailable.

For an AI tutor, districts should look beyond performance inside the platform. Can students solve a new problem during class? Can they explain why the solution works? Do they remember the skill several weeks later?

Student independence deserves attention. If performance improves only when the tool is present, the system may be supporting completion rather than durable learning. That support may still have value, but schools should be honest about what it accomplishes.

Ask Who Benefits

A districtwide average can hide very different experiences.

An AI platform might improve outcomes overall while producing weaker results for English learners, students with disabilities, students using certain dialects, or children whose backgrounds are poorly represented in its training data.

Access may create another divide. Students with reliable devices, strong internet connections, and quiet places to work can use a platform differently from classmates who have access only during school hours. Students who already know how to ask detailed questions may receive stronger responses than those who need more guidance.

Districts should review participation, achievement, error reports, accessibility concerns, and teacher overrides across relevant student groups while protecting individual privacy.

They should also ask whether the tool expands opportunity or simply serves students who were already succeeding.

Imagine an AI-supported advising platform that increases the number of students receiving college and career information. That sounds promising. But are first-generation students receiving the same quality of recommendations? Are students with disabilities being directed toward a narrower set of options? Can multilingual families understand and question the guidance?

The average outcome will not answer those questions.

Neither will a vendor dashboard. Teachers, students, and families must be part of the evaluation.

A teacher may notice that a platform performs well in one subject but repeatedly generates inaccurate material in another. Students may report that an AI tutor gives away answers too quickly, making it easy to avoid productive struggle. Families may be uncomfortable with how a system describes or classifies their child.

Those experiences are evidence.

The NIST AI Risk Management Framework’s measurement guidance emphasizes that AI benefits and risks emerge from the interaction between technology, people, and the context in which a system is used. NIST recommends testing whether systems function as claimed, tracking errors and negative effects, comparing performance before and after deployment, and involving users in evaluation.

For schools, that means looking inside classrooms rather than relying entirely on platform analytics.

Measure Value and Harm at the Same Time

No single number can capture the educational impact of AI.

A useful district evaluation brings together several kinds of evidence: student performance, the quality of student reasoning, ability to complete independent tasks, teacher workload, differences among student groups, reported errors, accessibility concerns, privacy incidents, and feedback from the people using the system.

The evidence should also be compared with a meaningful starting point. What happened before the platform was introduced? What happens in similar classrooms that are not using it? Could an existing intervention address the same need at a lower cost or with less risk?

Perfect research conditions are rarely available in a school district. Thoughtful comparisons are still possible.

A limited pilot across several classrooms can provide useful information when the goal is clearly defined and starting conditions are documented. The district is not trying to publish a national study. It is trying to make a responsible local decision.

The review should begin early.

During the first month, leaders can focus on access, technical problems, training needs, privacy questions, and whether the tool functions as promised. After several months, they can examine teacher workload, student participation, output quality, accessibility, and early evidence of learning.

By the end of a semester or school year, the district should be able to compare academic outcomes, student independence, equity, cost, teacher practice, and unintended consequences with the original goals.

Harms and near misses belong in that review.

Did the platform produce inaccurate instructional content? Were students incorrectly flagged for misconduct? Did teachers enter information that should not have been shared? Were inaccessible materials generated? Did the system underestimate certain students and recommend lower-level work?

A teacher who catches a dangerous science error before a lesson has identified a weakness worth documenting. So has a counselor who finds that an automated summary omitted important context.

The NIST guidance on managing AI risk recommends continued monitoring because a system’s performance can change after deployment. It also calls for feedback, incident response, appeals, overrides, and the ability to discontinue systems that exceed an organization’s tolerance for risk.

Approval should begin the monitoring process, not end it.

Let the Evidence Change the Decision

An evaluation has little value if the district has already decided to renew the contract.

Leaders should determine in advance what would justify expansion, what would require modification, and what would cause the district to stop using the system.

Some tools will deserve broader use. Others may work only in certain subjects or grade levels. A district might retain one feature, disable another, strengthen its training, or revise the rules governing student use.

Sometimes the evidence will show that the platform should be removed.

Ending a pilot is not a failure when the pilot was designed to answer a question. Continuing an ineffective system because the district has already invested money and time is the more expensive mistake.

School boards and communities deserve an honest account of what was learned. Leaders should explain why the tool was introduced, who used it, which outcomes were examined, what improved, what did not, and what concerns emerged. Limitations in the evidence should be acknowledged rather than hidden behind early statistics.

The U.S. Department of Education’s guidance on AI in schools emphasizes responsible adoption, privacy, and engagement with affected stakeholders, especially parents.

Transparent evaluation is part of that responsibility.

Families do not need to be told that a platform is transformational. They need to understand what it does, why the district uses it, what evidence supports that use, how students are protected, and how concerns can be raised.

Teachers should see that their feedback influenced the decision. Students should understand how the technology affects their learning and what they can do when something goes wrong.

Trust grows when districts are willing to discuss uncertainty rather than hide it behind a dashboard.

Measure What Matters

AI may help educators work more efficiently, give students faster feedback, broaden access to support, and create learning experiences that were previously difficult to provide.

Those possibilities are worth exploring.

They are not self-proving.

A tool should earn its place in a school by serving a clearly defined educational purpose, producing benefits that can be observed, and operating within boundaries the community understands.

The school board presentation should include more than accounts activated, prompts submitted, and hours reportedly saved.

It should show what students understand now that they did not understand before.

The number of times a tool is used tells a district whether it has been adopted.

It does not tell leaders whether it deserves to remain.

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Editor’s note: edCircuit is vendor-neutral and does not endorse products or services. Any mention of a specific solution is included for contextual and informational purposes only.

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Teacher in-service and AI: a tangle of glowing search windows untangles into one bright ribbon of lesson cards on a teacher's desk at 7:30 in the evening
K-12 TeachersProfessional DevelopmentHot Topics - controversialArticle SeriesArtificial IntelligenceK-12

Teacher In-Service and Artificial Intelligence

by EdCircuit Staff
written by EdCircuit Staff 6 minutes read

Teacher in-service has always been about helping educators grow, but artificial intelligence is changing how that growth happens.

Imagine it is 7:30 on a Tuesday evening.

A fifth-grade teacher is preparing tomorrow’s reading lesson. She wants fresh ideas to support struggling readers while challenging students who are already excelling. In the past, she might have spent an hour searching websites, watching videos, or digging through professional development materials.

Instead, she opens an AI assistant.

Within minutes, she has differentiated discussion questions, vocabulary activities, parent communication ideas, and several instructional strategies she can adapt for tomorrow’s class.

That isn’t replacing professional learning.

It is accelerating it.

Artificial intelligence is transforming teacher in-service from something educators attend into something they can access whenever they need support. Rather than waiting for the next workshop or district professional development day, teachers now have access to tools that help them learn, reflect, and improve in real time.

The future of professional learning is becoming more immediate, more personalized, and more responsive than ever before.

From Searching for Answers to Finding Solutions

For years, educators relied on search engines to answer professional questions.

Need a new formative assessment?

Search.

Trying to understand an unfamiliar instructional strategy?

Search.

Looking for classroom discussion ideas?

Search again.

The challenge was never a lack of information.

It was finding the right information quickly.

Artificial intelligence changes that experience.

Instead of browsing dozens of websites, educators can ask specific questions, compare instructional approaches, summarize research, generate examples, or request classroom-ready resources in seconds.

Professional learning becomes less about finding information and more about understanding how to apply it.

That shift allows educators to spend more time thinking critically about instruction instead of simply searching for resources.

Personal Learning for Every Educator

Perhaps the greatest opportunity AI offers teachers in-service is personalization.

Every educator follows a different professional journey.

A first-year teacher needs coaching and classroom management support.

A veteran science teacher may want updates on laboratory safety, emerging technologies, and advanced instructional practices.

A principal may focus on leadership development, staff coaching, and school improvement planning.

Artificial intelligence can help create individualized learning experiences for each of them.

Imagine logging into your district’s professional learning platform.

Instead of scrolling through hundreds of available courses, AI recommends learning experiences based on your teaching assignment, certifications, previous professional development, district initiatives, classroom observations, and long-term professional goals.

The platform understands where you’ve been.

It helps guide where you’re going next.

Professional learning becomes a personalized pathway instead of a standardized checklist.

Giving Teachers Their Most Valuable Resource

Ask almost any educator what they wish they had more of.

The answer is almost always the same.

Time.

Artificial intelligence cannot create more hours in the day.

It can help educators make better use of the hours they already have.

Teachers are using AI to summarize lengthy research articles, organize meeting notes, generate lesson ideas, create differentiated instructional activities, draft family communications, brainstorm assessment questions, and explore new classroom strategies.

Instead of replacing professional learning, AI removes many of the time-consuming tasks surrounding it.

Educators spend less time gathering information and more time applying new ideas to improve student learning.

AI as a Professional Learning Coach

Perhaps the most exciting opportunity is AI’s ability to become a learning partner rather than simply a content generator.

Imagine receiving recommendations based on the professional learning you’ve already completed.

Imagine AI suggesting a five-minute refresher before introducing a new instructional strategy.

Imagine a district platform identifying areas where additional coaching might strengthen classroom practice.

Professional learning becomes continuous instead of occasional.

Artificial intelligence does not simply answer questions.

It helps educators ask better questions.

That may prove to be its greatest contribution.

Supporting District Leaders

Artificial intelligence is also changing professional learning behind the scenes.

District leaders face the enormous challenge of supporting hundreds or even thousands of educators with different responsibilities, certifications, and professional goals.

AI can help organize that complexity.

Learning platforms increasingly automate course assignments, monitor certification requirements, identify professional learning gaps, recommend future training, and provide leaders with real-time insights into districtwide progress.

Instead of manually assigning the same learning to everyone, administrators can provide targeted opportunities that better reflect individual needs.

Personalization becomes possible at scale.

Human Judgment Still Matters

As artificial intelligence becomes more capable, one truth remains unchanged.

AI generates.

Educators evaluate.

Artificial intelligence can organize information, summarize research, recommend resources, and provide ideas.

Teachers determine what is instructionally appropriate.

Instructional coaches provide context.

Principals understand school culture.

Experienced educators apply professional judgment.

The future of teacher in-service is not about replacing human expertise.

It is about strengthening it.

The strongest professional learning systems will combine the efficiency of artificial intelligence with the experience, relationships, and wisdom that only educators can provide.

Teaching Educators How to Use AI

Artificial intelligence is also creating entirely new professional learning priorities.

Educators are learning far more than how to generate lesson plans.

They are learning how to write better prompts.

How to verify AI-generated information.

How to recognize bias.

How to protect student privacy.

How to use AI ethically and responsibly.

How to decide when technology adds value—and when human expertise should lead.

Those conversations are quickly becoming an essential part of teacher in-service.

Looking Toward the Future

Artificial intelligence is still in its early stages, yet its impact on professional learning is already significant.

Imagine a teacher in 2030 beginning the school day with an AI-powered professional learning assistant.

It knows the curriculum being taught that week.

It understands previous professional development experiences.

It recognizes district priorities.

It recommends a five-minute learning module before a new lesson.

It suggests coaching questions after classroom observations.

It identifies opportunities for growth while connecting educators with mentors and colleagues who have successfully addressed similar challenges.

That future is no longer difficult to imagine.

Technology will continue evolving.

Professional learning will continue evolving with it.

The future of teacher in-service is not about educators learning from artificial intelligence.

It is about educators learning with artificial intelligence.

When AI removes barriers, saves time, and personalizes learning, educators are free to focus on what matters most—building relationships, inspiring students, and creating meaningful learning experiences inside every classroom.

That is the real promise of artificial intelligence.

Not replacing great teachers.

Helping them continue to become even better.

The Future of Professional Learning Series

Part 1: Teacher In-Service Has Changed

Part 2: Teacher In-Service Beyond the Auditorium

Part 3: Teacher In-Service Is No Longer One Size Fits All

Part 4: Teacher In-Service Meets Microlearning

Part 5: Teacher In-Service and Artificial Intelligence (Current Article)

Coming Next: Teacher In-Service Focuses on New Priorities — exploring how professional learning has expanded beyond curriculum to include cybersecurity, student data privacy, school safety, mental health, accessibility, digital citizenship, and other emerging priorities shaping today’s schools.

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AI training for teachers: an AI prompt window asks how do I check this before I use it in class, with symbols for verification, privacy, bias, accessibility and assessment rising from it
K-12Assessment and TestingK-12 TeachersHot Topics - controversialArtificial Intelligence

AI Training for Teachers: Beyond Prompts and Tools

by EdCircuit Staff
written by EdCircuit Staff 10 minutes read

AI training for teachers too often looks like this: a presenter opens a slide deck, demonstrates a collection of impressive tools, shares several prompts, and sends everyone back to their classrooms with a list of links.

The session may be energetic. Teachers may leave with new ways to create quizzes, draft emails, adjust reading levels, or plan lessons.

Then Monday arrives.

A student asks whether she can upload her essay to a public chatbot for feedback. A teacher wonders if he can paste part of an individualized education program into an AI tool to create modified instructions. A department chair wants to know whether an AI-detection score can be used as evidence of cheating. Another teacher discovers that an AI-generated reading passage contains invented facts.

The professional development covered what the tools could do. It did not prepare educators for the decisions they would have to make.

That is the gap districts must close.

Schools do not need teachers who can name every new AI platform. They need teachers who can decide when AI is appropriate, recognize when an output is unreliable, protect student information, establish clear classroom boundaries, and remain responsible for work produced with technological assistance.

Prompt writing may be part of AI professional development. It cannot be the entire program.

Exposure Is Not the Same as Readiness

AI is already part of teachers’ work.

According to National Center for Education Statistics data released in 2025, 73 percent of public schools reported that at least some teachers were using AI for tasks such as lesson planning, administration, instructional materials, assessments, grading, or feedback.

About two-thirds of schools said they had provided AI training to at least some teachers, staff members, or administrators. Yet only 26 percent had trained all teachers, and just 31 percent reported having a written policy governing student AI use.

The result is an uneven landscape. One teacher may prohibit AI entirely. Another may encourage it without requiring disclosure. A third may enter student work into an unapproved platform because no one has explained the privacy implications.

Teachers should not have to build district AI policy one classroom at a time.

The U.S. Department of Education’s 2025 AI guidance identifies professional development as an important part of responsible AI integration. But providing a workshop and preparing an educator are not the same thing.

The content of the training matters. So does what happens after it ends.

Teachers Need a Working Understanding of AI

Educators do not need to become computer scientists. They do need enough foundational knowledge to understand why an AI system can produce a fluent, confident, and completely inaccurate response.

Generative AI identifies and extends patterns. That allows it to create useful material quickly, but it can also fabricate sources, misstate historical events, mishandle mathematical reasoning, oversimplify scientific concepts, and reproduce biases found in its training data.

Teachers should encounter those failures during professional development rather than discover them in front of students.

Ask an AI system to create a reading passage with citations, then check whether the sources exist. Give it a math problem and examine the reasoning, not only the answer. Request a summary of a district policy and compare the result with the actual document. Generate material about a culture, disability, or historical event and look for stereotypes or missing perspectives.

The purpose is not to prove that AI is unusable. It is to build a healthy level of skepticism.

Educators who understand the limitations of the technology are more likely to verify an output before using it. Those who have seen only carefully selected demonstrations may assume the system is more reliable than it is.

Privacy and Verification Must Be Practical

“Do not enter sensitive information” sounds clear until a teacher has to decide what counts as sensitive.

Is it acceptable to upload a student essay if the student’s name is removed? Can assessment results be entered without identification numbers? What about behavior notes, parent emails, accommodation plans, recorded voices, or photographs of classroom work?

Training should answer these questions with specific examples and clear district expectations.

The Department of Education’s Student Privacy Policy Office advises teachers to determine whether an application has been approved by their school or district before using it. Its guidance also notes that online services can introduce privacy and security vulnerabilities.

Every educator should know which AI platforms are approved, what information may be entered, which uses require additional review, and whom to contact when a situation is unclear. A district also needs a visible approval process and an accessible list of reviewed tools. “Ask IT” is not much of a safeguard when no one knows whom to ask or an answer takes several weeks.

The same practicality must shape training on accuracy.

AI can create a polished lesson, rubric, parent message, or assessment in seconds. That speed can make review feel optional, especially when an educator is short on time.

It is not optional.

Teachers remain responsible for the accuracy, appropriateness, accessibility, and instructional quality of the materials they use. Before placing generated content in front of students, an educator should check the facts, confirm alignment with the learning objective, look for bias or missing perspectives, verify accessibility, and revise the material for the actual classroom.

That review may reveal that the AI saved time. It may also reveal that starting from scratch would have been faster.

Either outcome is useful. Teachers need permission to reject an AI-generated response entirely.

Student Expectations and Assessment Belong Together

A district policy may identify what is permitted, but teachers still have to translate that policy into daily classroom practice.

Students need more guidance than “AI is allowed” or “AI is prohibited.” Expectations should change according to the purpose of the assignment.

Using AI to brainstorm research questions may be appropriate. Asking it to write the final response may not be. Translation support might be permitted during one activity but restricted during a language assessment. AI feedback could support revision, while submitting a generated draft as original work would violate expectations.

Teachers should be ready to explain what assistance is allowed, what students must complete independently, when AI use must be disclosed, what information should never be entered, and how generated content should be verified.

Those expectations should also influence assessment design.

AI has exposed weaknesses in assignments that were already easy to complete without much thinking. That does not mean every essay, worksheet, or take-home task has lost value. It does mean educators need time to examine what their assessments actually measure.

A useful training session might ask teachers to bring an existing assignment and consider: What is the intended learning? Which portions could AI complete easily? Where must the student demonstrate original reasoning? What evidence of the learning process could be captured?

The goal is not to make every assignment “AI-proof.” New tools will continue to appear, and attempts to defeat each one can push classrooms toward surveillance and suspicion.

The better goal is to make learning visible.

Students might explain a decision, defend a conclusion, compare sources, document revisions, apply knowledge to a local situation, or reflect on how they evaluated an AI response. An oral conference or in-class draft may reveal more than another piece of detection software.

An AI-detection score can raise a question. It should not be treated as proof. Educators need fair ways to examine drafts, review previous work, speak with students, and consider other explanations before reaching a conclusion.

Bias and Accessibility Are Core Skills

Bias and accessibility should not be saved for an optional advanced session.

AI systems can reproduce stereotypes, produce weaker results for certain languages or dialects, misunderstand disability-related behavior, and generate materials that appear polished but are instructionally inappropriate.

Teachers need practice recognizing those problems.

An educator might compare the career recommendations an AI system produces for students with different names or backgrounds. A history team could review generated content for missing perspectives. Special educators might examine whether an AI-created modification preserves the intended learning standard or quietly lowers expectations.

Accessibility involves both the platform and the material it produces. Can students navigate the tool with a keyboard or screen reader? Are captions accurate? Do images include meaningful descriptions? Does a simplified reading passage preserve the essential content?

UNESCO’s AI Competency Framework for Teachers places educator development within five areas: a human-centered mindset, AI ethics, AI foundations and applications, AI pedagogy, and AI for professional learning.

That is a much broader vision than showing teachers how to write a better prompt. It recognizes that AI competence involves values and judgment as much as technical skill.

Training Must Fit the Role—and Continue

A districtwide introduction can establish common language, but it cannot meet every professional need.

Classroom teachers need help with lesson design, feedback, assessment, student expectations, and family communication. Counselors need guidance on confidentiality and the limits of predictive recommendations. Special educators must examine accommodations, accessibility, and individualized decision-making. Administrators need to understand discipline, staff evaluation, policy enforcement, and how to respond when families raise concerns.

Librarians and media specialists may lead work involving source verification, citation, research, and digital literacy. Technology staff need deeper knowledge of security, data governance, integrations, and vendor practices.

Grade level changes the conversation as well.

Elementary educators may focus on teacher-directed uses, privacy, and age-appropriate AI literacy. Middle school teachers need strategies for establishing boundaries as students begin experimenting independently. High school educators may address research, citation, advanced coursework, career preparation, and responsible use beyond the school network.

The principles remain consistent, but the situations should reflect the people in the room.

Training also has to continue after the first session. AI changes too quickly for one annual presentation to be enough, yet teachers cannot attend a new product demonstration every month.

A better structure might include short scenario-based sessions, department discussions, coaching, model lessons, and office hours with technology staff. Educators should have time to test approved tools, examine failures, revise their own assignments, and share what is working.

Teacher feedback should also shape future policy and purchasing decisions. The people using a platform every day will often recognize problems long before they appear in a formal review.

Most importantly, teachers need time to learn. Asking them to experiment independently after hours sends the message that implementation is an individual responsibility rather than a district initiative.

When schools expect professional use, they should provide professional learning time.

How Will Schools Know the Training Worked?

Attendance at a workshop does not demonstrate readiness.

Districts should look for changes in practice. Are educators using approved platforms? Can they explain what student information may not be entered? Are generated materials reviewed before use? Do classroom expectations align with district policy? Are assignments changing in ways that make student thinking more visible?

Teacher confidence matters, but it should not be the only measure. Leaders should also examine support requests, privacy incidents, academic-integrity disputes, student and family questions, and examples of successful classroom use.

A rise in questions does not necessarily mean the training failed. It may indicate that educators are thinking more carefully and recognizing situations they previously overlooked.

The objective is not perfect confidence. Overconfidence may be the greater risk.

What schools need is informed judgment.

Keep the Teacher at the Center

AI may help a teacher create a starting point, notice a pattern, adapt a resource, or reduce repetitive work. Those benefits are meaningful, especially in a profession where time is always limited.

But the technology cannot know a classroom the way its teacher does. It does not understand that a student has recently lost a family member, that an example will not connect with the local community, or that a technically correct explanation will confuse this particular group of learners.

The teacher supplies that knowledge.

An effective AI session should not end with educators saying, “I learned about thirty new tools.” It should leave them prepared to determine whether a use is allowed, whether information is protected, whether an output is accurate, and whether the technology improves the learning.

Schools do not need educators who know every AI product.

They need educators who know what to do when AI enters the classroom—and when it should be asked to leave.

Editor’s note: edCircuit is vendor-neutral and does not endorse products or services. Any mention of a specific solution is included for contextual and informational purposes only.

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AI procurement for schools: glowing gold question marks over coins, dollar bills and an unsigned contract, with an AI neural network behind
Market TrendsHot Topics - controversialArtificial IntelligenceK-12

AI Procurement for Schools: 10 Questions to Ask

by EdCircuit Staff
written by EdCircuit Staff 11 minutes read

AI procurement for schools often begins with an impressive demonstration.

A platform creates a lesson in seconds, summarizes student performance, translates a family message, recommends an intervention, and predicts which students may need additional support. The presentation is polished. The promised savings—in both time and money—are difficult to ignore.

Then someone asks a simple question:

“What happens to our students’ information after they use it?”

The room changes.

The sales team may offer a general assurance that the system is secure or “compliant.” But the superintendent, technology officer, curriculum director, and legal counsel need more than reassurance. They need to know what information the platform collects, where it goes, who can access it, whether it is used to improve an AI model, and how the district can remove it.

Those questions should not be saved for the final contract review. They belong at the beginning of the conversation.

AI purchasing is not simply a technology decision. It is also an instructional, financial, privacy, security, accessibility, and governance decision. A platform may influence what teachers create, how students receive feedback, what families are told, and which students are identified for additional services or opportunities.

Before approving an AI platform, pilot, or renewal, district leaders should be able to answer ten questions.

1. What problem are we trying to solve?

A district should begin with an educational or operational need, not a product demonstration.

Is the goal to reduce the time teachers spend creating routine materials? Improve access to tutoring? Translate family communication? Help counselors organize information? Provide students with faster feedback?

“Bringing AI into the district” is not a goal.

When the problem is poorly defined, schools are more likely to purchase a broad platform and then search for ways to use it. The result can be overlapping subscriptions, unnecessary data collection, inconsistent classroom practices, and tools that create as much work as they eliminate.

Start by describing success without mentioning the product. What should improve for students, educators, or school operations? How will the district know whether that improvement occurred?

The NIST AI Risk Management Framework encourages organizations to consider an AI system’s purpose, context, benefits, and potential harms before deployment. That process may reveal that AI is the right solution. It may also reveal that a simpler option would work just as well.

2. What information does the system collect?

The answer may go far beyond names, identification numbers, and grades.

An AI platform may collect prompts, assignments, voice recordings, images, browsing activity, device information, response times, and patterns of use. It may also create new information about a student, including proficiency estimates, risk scores, behavioral classifications, or recommended interventions.

The district needs a plain-language data inventory that identifies:

  • Information collected directly from students and employees
  • Data imported from district systems
  • Metadata created through use of the platform
  • Scores, inferences, and classifications generated by the AI
  • Where the information is stored
  • How long each category is retained

Schools should also ask whether every data element is necessary. A writing assistant does not need a student’s disciplinary history. A scheduling tool may not need special education information. Giving a platform access to additional data may make integration easier, but convenience alone does not justify the exposure.

If a provider cannot clearly explain what its system collects, district leaders cannot meaningfully evaluate the risk.

3. Will district data be used to train an AI model?

This question requires a direct answer.

“Your data is not sold” does not necessarily mean it is excluded from model training, product development, evaluation, or other secondary uses. The platform may also rely on an outside model provider or subcontractors whose data practices differ from those of the company signing the contract.

The agreement should address student and educator prompts, uploaded documents, generated responses, feedback, and usage information. It should state whether any of that material may be used to train, fine-tune, test, or improve a model.

If some form of training is permitted, leaders need to understand what happens first. Is the information identifiable, de-identified, or aggregated? Can the district decline the use without losing essential functions? Could information submitted by a student later appear in a response provided to someone else?

NIST’s Generative AI Profile recommends examining training-data practices for privacy and other risks. For schools, that review must include every organization that may handle district information, not only the company whose name appears on the platform.

A current list of subprocessors should be part of the review, along with a requirement that the district be notified when that list changes.

4. Who controls the data, and how is it deleted?

Schools sometimes assume that because they supplied the information, they retain complete control over it. Contract language may tell a different story.

Terms that give a provider broad, permanent, or transferable rights to district content deserve close scrutiny. The agreement should preserve district control, limit use to authorized educational purposes, and prohibit unauthorized sale, disclosure, licensing, or commercial exploitation.

Under certain FERPA arrangements, third-party providers must remain under the school’s direct control regarding the use and maintenance of personally identifiable information from education records. The Department of Education’s student privacy guidance for online tools also explains that education data may not be redisclosed or used for unauthorized purposes.

The district also needs to know what happens when a student leaves, an employee account closes, or the contract ends. Can the information be exported in a usable format? When will remaining copies be deleted? Are backups included? Will the provider confirm that deletion has occurred?

The Department’s model terms-of-service guidance identifies the return and destruction of data as important practices for limiting unnecessary exposure and maintaining control over student information.

5. What evidence supports the product’s claims?

A demonstration shows what a platform can do under favorable conditions. It does not show how reliably it will perform in a real district.

If a company claims its system improves achievement, reduces teacher workload, identifies struggling students, or personalizes learning, ask to see the evidence. Who conducted the evaluation? What students and schools participated? How long was the tool used? Were the findings independently reviewed? Did the study measure educational outcomes or only user satisfaction?

Accuracy claims need the same scrutiny. A system described as “95 percent accurate” may perform differently across grade levels, subjects, languages, disabilities, and types of assignments. The remaining 5 percent also matters. A modest error rate can still produce significant harm when a tool is used across thousands of students.

Providers should be willing to discuss known limitations, common errors, and situations in which their product should not be used. That conversation is often more revealing than the demonstration itself.

6. Could the output influence a high-impact decision?

A platform may be described as providing recommendations rather than decisions. The distinction becomes meaningless if employees routinely accept its recommendations without further review.

Stronger safeguards are necessary when an AI system could influence:

  • Grades, promotion, or graduation
  • Academic-integrity allegations
  • Discipline or behavioral consequences
  • Special education identification or services
  • Mental health or safety responses
  • Course placement or advanced opportunities
  • College and career recommendations
  • Hiring, evaluation, or employment decisions

The system should never serve as the sole evidence for a consequential decision. A qualified person must review the underlying information, consider the surrounding circumstances, and have the authority to reject the recommendation.

Students, families, and employees should also have a clear way to learn that AI contributed to a decision and to question the result. A person clicking “approve” at the end of an automated process does not create meaningful oversight if that person lacks the information or time to examine what the system produced.

7. Has the system been tested for bias and accessibility?

A product can perform well overall while creating persistent problems for particular students.

Was the system tested with English learners, students with disabilities, students from different racial and cultural backgrounds, and children across the grade levels the district intends to serve? A single overall accuracy rate may conceal wide differences among groups.

Accessibility also needs to be tested in practice. Can students navigate the platform with a keyboard or screen reader? Are captions accurate? Does the interface support magnification and alternative input methods? Can students with disabilities use the central features without being pushed into a separate or inferior experience?

AI-generated translations, captions, reading-level adjustments, and suggested accommodations can be helpful, but they still require appropriate review.

The district’s responsibility continues after launch. Complaints, errors, overrides, and outcomes should be monitored for patterns. If one group of students experiences significantly more problems than another, leaders need the information and authority to investigate.

8. How is the system secured, and what happens after an incident?

“Industry-standard security” is too vague to guide a purchasing decision.

Technology leaders should examine encryption, authentication, access controls, employee permissions, vulnerability management, system logging, backups, penetration testing, and independent security assessments. They should know where information is stored and which outside companies can access it.

The contract should define a security incident and establish how quickly the district will be notified. It should also identify who investigates, who communicates with families, and what documentation the district will receive.

This becomes more complicated when several companies support one platform. A breach may occur through a hosting service, model provider, analytics company, or another subprocessor the district never evaluated directly.

The Federal Trade Commission’s updated Children’s Online Privacy Protection Rule limits indefinite retention of covered children’s information and reinforces requirements related to data minimization and protection. COPPA applies to covered online services involving children under 13. Districts must also consider FERPA, state student-privacy laws, local policies, and the specific commitments included in their contracts.

9. What will implementation actually cost?

The subscription price rarely represents the entire investment.

Districts may need to pay for integration, account management, data migration, professional development, accessibility testing, cybersecurity review, technical support, and ongoing evaluation. Staff time should be part of the calculation as well.

If teachers must constantly correct unreliable outputs, counselors must investigate excessive alerts, or technology staff must manage recurring account problems, the platform may consume the time it promised to save.

Introductory pricing and free pilots deserve a careful look. What will the system cost in its second or third year? Are privacy controls, administrative reporting, or technical support limited to premium plans? Will the district pay more as usage grows? Are there separate charges for storage, integrations, or exporting district data?

A free tool is not free if students pay for it with their data or educators pay for it with their time.

10. What happens when the product changes—or the district leaves?

AI products can change quickly. A platform approved in September may use a different model, subprocessor, or data practice by January.

The contract should require notice of material changes involving privacy, security, model providers, data use, core functionality, or known limitations. The district should have an opportunity to review significant changes and, when necessary, end the agreement without penalty.

An exit plan is equally important. Can the district retrieve its information? How long will access remain available? What happens to student accounts, prompts, outputs, analytics, backups, and information held by subprocessors?

NIST’s AI Risk Management Framework Playbook recommends continued monitoring because the performance and trustworthiness of AI systems can change after deployment. Its guidance also addresses user feedback, appeals, overrides, incident response, change management, and the need to decommission systems that exceed an organization’s risk tolerance.

A district should know how it will leave a platform before it becomes dependent on it.

Procurement Is the Beginning

Answering these questions does not guarantee that an AI system will work as promised. It gives the district a disciplined way to decide whether the likely benefit justifies the cost and risk.

Whenever possible, implementation should begin with a limited pilot built around a defined purpose. The district should identify who may use the tool, what information may be entered, what training is required, and how success will be measured. Teacher, student, and family feedback should help shape the final decision.

At the end of that pilot, leaders should be willing to walk away.

A platform may be innovative but unnecessary. It may save time while creating privacy concerns the district cannot resolve. It may perform well in a demonstration but poorly with actual students. It may address a real need but require safeguards the provider is not prepared to offer.

The U.S. Department of Education’s guidance on AI use in schools supports responsible adoption while emphasizing privacy, existing legal requirements, and engagement with affected stakeholders, especially parents.

That engagement should happen before a contract is signed—not after families begin asking what happened to their children’s information.

A polished demonstration can show what an AI platform is capable of doing. Responsible procurement determines what the district will permit it to do with students, educators, and their data.

Editor’s note: edCircuit is vendor-neutral and does not endorse products or services. Any mention of a specific solution is included for contextual and informational purposes only.

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