Why AI Agents for Education Are Becoming the Top Solution in Learning Platforms

TL;DR

• The AI-in-education market is expected to grow from $3.43 billion in 2023 to $54.5 billion by 2032.
• We built an AI agent for an EdTech certification platform, and in three months, instructor onboarding time dropped from 8–10 days to under 3 days. Completion rates in the pilot track improved from 58% to 71%.

5 Ways to Use AI Agents:

• Personalized Learning: Adaptive lessons based on each student’s progress.
• Assessment Help: Eases grading and improves test quality.
• Engagement Recovery: Re-engages students who are losing focus.
• Instructor Copilot: Handles admin tasks so teachers can focus on teaching.
• Workflow Automation: Clears up slow processes across teams.

Real-World Examples of AI Agents

• Duolingo uses AI to personalize language lessons for over 300 million users, making learning faster and more fun.
• Coursera brings AI into peer reviews, helping make grading quicker, more consistent, and easier for both students and instructors.
• Virti built AI and no-code tools that let people create immersive learning content in just a few minutes.
• All Day TA offers college students an AI teaching assistant that answers their questions right away, whether they’re easy or tough.

How to Start Using AI Agents in Your Platform

• Pick areas where slow decisions are causing problems. Fix the biggest bottlenecks first.
• Set clear rules for the AI: when it should act on its own and when it should ask a human for help.
• Test your first AI agents inside your company, on back-office tasks, to catch and fix issues.
• During the test, track key things like time-to-feedback reduction, instructor satisfaction, churn prediction accuracy, and learning outcome delta.

How AI Agents for Education Work in Practice: Reducing Instructor Load in a Growing Online Certification Program

Slow and steady wins the race. Our client, an EdTech company from Canada, learned this lesson the hard way. Adding 20,000+ new learners each quarter, they seemed like a success story straight out of Forbes Magazine, but the reality was darker: broken instructor coordination, unkept commitments, and high dropout rates. Let’s see how we helped them fix it.

What Was the Struggle

Most of the company’s instructors were part-time or adjunct, balancing teaching with other jobs. Because of that, the HR team was spending days trying to onboard them, explain expectations, and somehow keep everyone on the same page across different modules and cohorts.

Although asynchronous delivery made it easier to scale, maintaining the quality of learning was hard. Instructors often didn’t notice disengaged students until final assessments. Unsurprisingly, completion rates dropped below 60% in two key tracks. We instantly knew this wasn’t a simple dashboard project – it called for a more sophisticated solution.

What We Built

After discussing resources, expectations, and key needs, we designed an instructor-facing AI and embedded it into their existing LMS. The team didn’t have to learn any new systems, so adoption was quick. Here’s what the agent did:

• Walked new instructors through a custom onboarding flow based on subject and learner profile.
• Sent a Monday morning “health check” showing how each cohort was doing (progress, message volume, quiz performance).
• Flagged students who were starting to disengage and suggested simple, low-effort nudges like a quick video message or a deadline extension.
• Auto-generated weekly check-ins for instructors with actionable tips, cutting down back-and-forth with ops.

What Happened Next

Three weeks in, the AI had already proven its worth. One instructor followed an AI-generated prompt, sent a short message to a group of flagged learners, and within 48 hours, 78% of them had logged back in. Everyone was impressed with how subtle the AI’s work was, yet how massive the impact turned out to be.

What We Achieved

It didn’t take long to see some real changes:

• Instructor onboarding time dropped from 8–10 days to under 3
• Completion rates in the pilot track rose from 58% to 71%
• Instructors spent 35% less time coordinating with ops
• Assignment submissions went up by 22% across three cohorts

Or, in the client’s own words:

“We didn’t need the AI to teach. We needed it to clean up our processes so we could concentrate on real work, and we got that.”

Want to see even better outcomes? Let’s connect and see what we can build together.

Why AI Agents Are Emerging as a Core Layer in Modern Education Systems

Still, in most EdTech platforms, artificial intelligence feels like an add-on: a chatbot answering FAQs, an autocomplete suggestion, or a simple dashboard with student activity. It’s helpful, but no more than that. Yet, progress is going forward, and AI is becoming more than features. Generative AI can work as a true operational system behind the platform, collaborating with students like a teammate or mentor.

But don’t think AI tools have suddenly become so advanced – several pressure points have been building for some time:

• Instructional overload occurs as educators try to manage larger classes with less help.
• Low instructor-to-student ratios erode feedback and engagement.
• High dropout risk without early signals.
• Disconnected tools that require educators to spend hours managing logistics.
  

To solve these problems and stabilize the system, educators started to adopt AI agents. Previously used only for automation, they can now coordinate: guide a new teacher through their first week, regulate a lesson’s pace, provide timely feedback, or create personalized learning experiences. As one of our EdTech specialists said:

“Teachers have to make hundreds of small decisions every day that aren’t even related to teaching. Instead, they can use AI agents, trained for the education sector, and save time for more enjoyable or productive tasks.”

5 Ways You Can Use AI Agents in Your Organization

We thoughtfully build AI agents to support students, teachers, and the wider system, each in their own but interconnected ways. Below are the core agent types that change how education works at scale, with examples from real-world setups.

Adaptive Learning Agents (For Students)

Simply put, they adapt to how each student learns, not sticking to universal methods and approaches. That’s how they work:

• Track mastery patterns to understand where students are getting stuck or flying ahead
• Reorder, skip, or scaffold content, depending on pace and retention.
• Sync across LMS, curriculum maps, and knowledge graphs to keep everything aligned with broader standards.
• Turn rigid lesson plans into flexible learning experiences.

Assessment Intelligence Agents (For Students and Teachers)

More routine tasks, like grading, can be overwhelming and take more time than teaching itself. And when educators have less time for their primary focus, feedback suffers, too. Assessment agents fix this:

• Сreate different quiz versions based on Bloom’s taxonomy and other models.
• Grade essays and open responses with an eye for tone and meaning, not just keywords.
• Show concept-level gaps, so instructors see not just what was missed, but why.

Engagement Recovery Agents (For Students)

Low retention may be the most complex problem educators and course creators are trying to solve. Even if AI agents don’t solve it completely, they visibly support the effort (most helpful in self-paced formats). Here’s how:

• Monitor digital activity (clickstream, long pauses, incomplete work) to spot risk signals.
• React (e.g., suggest a short recap video) if someone stops reading halfway.
• Feed what they learn into other agents, helping them make better decisions next time.

Instructor Copilot Agents (For Teachers)

Pedagogy comes first, and the process second, not the other way around. Copilot agents are designed to take the weight off teachers when it comes to administrative tasks:

• Draft lesson plans that match the course goals, different learning styles, the teacher’s experience, and prior delivery history.
• Auto-fill onboarding checklists, policy reminders, and update course versions (especially great for adjunct or part-time teachers). 
• Generate student insights (e.g., performance summaries for parent-teacher meetings).

Institutional Workflow Agents (Ops and Admin)

Working alongside ops and admin teams, workflow agents help with organization, management, and compliance:

• Sync calendars, flag overlapping deadlines across courses, teams, and academic units.
• Reroute tasks automatically (e.g., tracking down a form or flagging a registration issue).
• Integrate with SIS, LMS, and accreditation portals to support compliance without adding more tools.

Want to see how these agents could work in your setup? Let’s talk.

The Best AI Agents for the Education Sector: Examples from Prominent Companies

If you think functional AI agents are still years away, you probably haven’t heard about the successful cases we’re intending to share. What’s interesting is that they all have one thing in common: AI is the core technological innovation, not just a supporting feature. Let’s take a look.

Duolingo AI Roleplay: Turning Language Learning into a Dialogue

From young to old, we all know Duolingo – it’s been around since 2011. Built on the idea that education should be free and available to everyone, the app aims to make language learning an enjoyable process that doesn’t even feel like learning.

And they’ve already succeeded: more than 300 million people use Duolingo to study over 30 different languages every day. That wouldn’t be possible without some advanced AI and a smart strategy behind it.

Instead of traditional flashcards and grammar drills, the platform uses AI algorithms to create a personalized, game-like experience. An AI system decides where someone should start, what they’re likely to forget, and what kind of practice may work best.

Agents also analyze how people learn, customize lessons, and track millions of users, gathering student data to create even more tailored lessons. And the best part is: the more people use the app, the more AI learns, like a tutor that doesn’t miss a thing.

Coursera: AI for Auto-Grading and Peer Review

Grading often takes hours, sometimes even days, and waiting for test results can hold up the learning process. For many years, Coursera has been a leader in the online education market, and staying on top calls for modern approaches to learning. Last year, they decided to adopt generative AI to provide more consistent and useful feedback on written assignments.

Using rubrics set by instructors, AI agents provide almost instant feedback to hundreds of users or send the task to a teacher if they’re unsure. Even during beta testing, the results of this “cooperation” didn’t disappoint:

• AI graded 300,000 submissions
• AI grading took about 1 minute per submission (vs. 15 hours with human reviews)
• Feedback was 45 times more detailed, with an average of 326 characters per comment
• People started submitting more attempts and got more involved
• Only 7% of users chose to switch back to human reviews

And the most telling stat – 90% of learners said the AI feedback was helpful.

Virti: Immersive AI Agents for Real-World Skill Training

Not as famous as our previous cases, but no less important, Virti is a learning platform built around immersive XR, VR, and AI technologies. It helps create hands-on training programs, mainly in fields where real-world practice is hard to get.

Dr. Alex Young, a former surgeon, started the company in 2018, noticing how outdated and limited traditional training methods were, especially for people working in high-pressure jobs.

Real success came to Virti during COVID-19, when hospitals used its AI-generated virtual patients to train medical personnel, going through procedures and conversations without the usual risks. Later, Amazon, the NHS, HTC, and Cedars-Sinai have all seen the potential of Virti and began using it to train their staff. More recently, Virti teamed up with HTC and Taipei Medical University to develop VR programs that teach better food safety practices in Asia.

Tutor CoPilot: Leveling the Tutoring Field

Last autumn, Stanford ran a study with 1,000 students and 900 tutors to see how a new AI tool called Tutor CoPilot might help in the classroom. To get straight to the results, the study suggests that AI-assisted tutoring, when designed to support rather than replace humans, can have a moderately positive impact.

CoPilot supports tutors with AI suggestions on how to address student mistakes. Students working with less experienced tutors saw 9% better results in their math scores. Teachers also noticed how the tool changed the way they work, helping them guide students with questions rather than simply giving them solutions.

Although it still has many weak spots, most tutors leaned on the AI during tougher parts of the lessons. Overall, this study is another sign that generative AI might help teachers and tutors, especially when students are struggling.

All Day TA: Scalable Academic Support

Big companies usually create broad, one-size-fits-all courses, but most college classes don’t work that way. In 2022, two professors from the University of Toronto noticed this niche in the EdTech market and started an AI company.

All Day TA is meant to help students get quick, accurate answers about their course material anytime they need it. What makes it different from other AI tools is that it’s built specifically for education, giving answers based on how each professor teaches and what they want their students to understand.

Each professor can build their custom AI agent, uploading their course content and sharing a URL with students. Later, AI agents generate a dashboard showing the progress of each student and class. They’ve already tested the system with 5,000 students at top universities and got stronger educational outcomes; currently working on self-service AI features.

Scalable Educational AI Agents Deployment: Our Tips on How to Achieve This

One thing we’ve learned: AI only works well when it’s part of a system it lives in. We’ve seen a lot of cases where someone added an AI agent without much planning, and it didn’t deliver what they hoped. Innovative support in education needs real infrastructure. Here are the four layers we focus on when building AI agents, with explanations from our senior AI engineer.

Don’t Teach in a Vacuum

If you want an agent to be intelligent, it has to understand the context it’s working in: student records, course materials, learning history, accommodations, everything. For that to happen, you need to integrate AI with your systems, like your LMS, SIS, CMS, or anything else you use.

We build agents that speak LTI natively and make decisions based on live data. They get a clear picture of who the student is: how they’re doing, how fast they move, and whether they’re starting to lose focus. Without that kind of alignment, agents stay shallow and work without understanding the learner.

“Lots of projects have the right idea, but AI can’t personalize anything without institutional memory. AI agents should see more than just the question being asked; they need to know who they’re talking to and why that matters.”

Data Should Flow While the Session Lives

One of the things people get wrong about agent-style AI is feedback timing. A lot of teams collect data after the session, but if the AI is supposed to adapt in real time, that’s too late. Post-session data is fine for reports and dashboards, but during the session is when the learning needs to happen.

At Inoxoft, we build event-driven architectures so the AI gets signals as the learner is interacting. That includes engagement cues, hesitations, pacing anomalies, and error correction, which are used in the decision-making process.

“Reactive AI is better than responsive one in terms of data timing. If you’re looking at data after the session’s over, you’ve already missed your chance to help. Your AI needs to see what’s happening right now, not five minutes ago.”

Human-in-the-Loop Control Is Still Necessary

In mission-critical fields (like healthcare, aviation, or K-12 education), AI agents must be able to explain themselves and let people intervene when necessary.  But you can’t just slap a panic button on the interface.

What we build is an interactive UI that lets educators see inside the AI’s head, called override frameworks. They answer why your agent made a recommendation, what inputs shaped its logic, and what the agent “believed” about the student. You can click on a suggestion and trace it back to the reasoning.

Here’s how it works in practice: if the AI wants to skip a practice module, it should be able to say, “Here’s why.” And the teacher should be able to answer, “I don’t agree.” Like that, systems get more solid, trustworthy, and safe.

“Our agents don’t replace teachers, they’re copilots – smart, adaptable, but know they don’t have the final say. We also add explainability UX, so teachers can click in and see what led to a suggestion, protecting students from algorithmic bias. Without that kind of transparency, adoption would go way slower.”

Longitudinal Learning Graphs: The Memory Layer Most Platforms Forget

Many AI systems reset every session, which may be okay for chatbots, but not for learning assistants. We follow the approach of longitudinal learning graphs that track how students learn over time, where they struggle, which skills stick, and how they respond to mistakes. Basically, the AI is learning from the learner.

In one case, we noticed students using agents with such a setup finished project-based modules 23% faster and performed twice as well compared to others. No wonder, when a personal AI assistant gets to know the student, it can adjust its tone, slow down, or push forward when needed.

“Learning is a process, not a series of events, but a lot of developers don’t seem to know that. The longitudinal learning graphs are what makes a difference between tutoring for tomorrow’s quiz and mentoring for results.”

Making decisions that boost your metrics takes the right expertise. We’re here to help you succeed!

How to Evaluate and Launch AI Agents in Your Platform or Institution

To launch an AI agent, you need to find the right place for it, not just make it look impressive. Our team has helped many companies move from traditional platforms to AI-driven ones, so we know that real success comes from doing boring but important things. Let’s talk about them.

Start Pilots Where Decision Friction is Highest

Low-risk is also low-impact, so testing your AI on simple FAQs won’t tell you much. We usually start with the points where decision-making takes the most time and slows everything down, like:

• Student onboarding and advising (because one-size-fits-all doesn’t work)
• Grading process and feedback (where large volumes overwhelm consistency)
• Admin tasks like registration help, or checking forms

If you solve the visible bottlenecks first, it’s easier to see AI’s worth and get people on board. For example, one school we worked with cut onboarding time by 38% just by using an AI agent to gather data and do eligibility checks.

Set Confidence Thresholds and Escalation Rules

An AI agent with low confidence can do more harm than good. Before writing a single line of code, we work with clients to set clear agent confidence thresholds – how sure the agent needs to be before it acts on its own. For example:

• 90% and up → Act (e.g., confirm class enrollment)
• 70–90% → Act, but flag for review
• Below 70% → Escalate to a human

And escalation isn’t just a technical process; we design clear UX flows that show users what’s happening when they’re routed to a human, which helps build trust in the system.

“Confidence scores aren’t static. You’ve got to monitor them and tune the thresholds as agents learn and environments change.”

— adds our AI engineer. 

Train Agents on Internal Processes First

Looking good on a marketing deck, AI agents may tempt you to roll them out into student-facing roles right away. But wise organizations build up their AI maturity quietly, saving time to fix bugs and see how the agent behaves without risking a flop. Our experts advise launching your agents internally first, on back-office workflows:

• Organizing course content
• Cleaning up syllabus metadata
• Summarizing instructor feedback

Watch the Right Metrics from Day One

You don’t need perfect dashboards full of numbers; you need the right ones. Just measuring how fast the agent responds isn’t enough. Here are a few things you should watch early on:

• Time-to-feedback reduction: Are students and instructors getting faster, more actionable feedback?
• Instructor satisfaction: Are human users feeling supported or second-guessed?
• Churn prediction accuracy: Is the AI helping catch at-risk students earlier?
• Learning outcome delta: Are students reaching competency faster, deeper, or more consistently?

“If all you’re tracking is uptime and response rates, you’re just checking if the pipes work. What matters most is whether AI can help people do their jobs better.”

— says a project business analyst at Inoxoft. 

Launch AI Agents That Learn and Scale With Our Expertise

Want to create an AI agent? Say no more, we can help you complete a project of any complexity faster, cheaper, and avoiding the usual pitfalls. Here are the perks of working with our team:

• Using our own AI Cursor accelerator, we can launch pilots, student tools, and instructor support 2.5 times faster than the industry average, cutting development costs by 30%.
• No need to build everything from scratch. We already have working AI modules for onboarding, grading help, feedback summaries, and academic advising, which reduce delivery risk and time-to-impact.
• Our AI slots into your LMS, SIS, or CMS without disturbing your current setup.
• We customize AI agents to mirror your teaching standards, feedback styles, and escalation rules.

What else do we have? 10 years of experience, a team of 170+ experts, a perfect 5/5 Clutch rating, 230 successful projects, and endless dedication to our clients’ success.

Schedule a free consultation with our experts and find answers to all your questions

Conclusion

AI agents have so much to offer in education, from automating everyday tasks like grading and attendance tracking to improving student engagement and retention. As we get more comfortable with this new world order, more educational institutions, startups, and businesses are exploring new, creative ways to use these technologies, especially with the growing trend toward personalization in all aspects of the educational process.

If you want to be among those who are already reaping the benefits of AI, consider us as your partner. With 10 years of experience, hundreds of successful projects, and deep expertise in the education field, we’re ready to help.

Let’s connect and see what we can create together