AI Course Creation and the Future of Online Learning

How AI Could Change Online Courses and the Future of Learning

Imagine opening an online course after a long day at work. You have twenty minutes, a half-finished assignment and one question you are slightly embarrassed to ask. The video explains the topic again. You still do not understand it. Replaying the same explanation a third time probably will not help.

Now imagine that the lesson can respond. It asks you to show where you got stuck, notices that the difficulty comes from an earlier concept, and builds a small exercise around it. When you return tomorrow, it picks up that unfinished thread. Later, it gives you a problem without assistance to find out whether the explanation actually worked.

That is the version of AI in education I find worth paying attention to. Generating another set of slides is useful, but the larger possibility is an online course that can participate in the learning process. For someone studying alone, that could change what it feels like to struggle with a subject.

We are not yet at the point where an autonomous online school can reliably teach anyone anything. Research already offers reasons for both excitement and caution, though. Some carefully designed AI tutors have improved measured learning. Other experiments show how easy it is to produce better homework while leaving the learner less capable of working independently.

To understand where generative AI could take educational courses and online schools, we need to look at both sides. We also need to ask what happens after producing lessons becomes the easy part.

AI could reshape how courses are created and how students learn, with educators guiding the experience. Conceptual AI-generated illustration.

Why making more lessons is only the beginning

An online course has traditionally been something an instructor finishes and a student then consumes. There may be discussion boards, assignments and live sessions, but a recorded explanation usually stays the same regardless of who is watching. The person who missed a prerequisite and the person who already knows half the material can end up following the same sequence.

AI course creation introduces another possibility. Parts of the experience can be produced or adapted when they are needed: an alternative example, a practice conversation, feedback on a draft, or a short explanation of a forgotten concept. The author is designing how the course responds as well as deciding what it contains.

Consider a beginner who wants to learn spreadsheets. An explanation built around a corporate sales forecast may feel remote. A household budget could make the same formula easier to approach. Later, the course should change the example again to check whether the learner understood the formula itself, rather than memorising the budget exercise. Personalisation becomes useful when it helps someone reach a shared standard of understanding.

There are three different technologies hiding inside this discussion. A course authoring tool helps create materials. An AI tutor talks to the learner. A learning management system, often called an LMS, organises enrolment, lessons, submissions and progress records. Connecting them could make a course more responsive, but purchasing one does not automatically deliver the other two.

The distinction matters because a convincing demonstration often shows only content production. An outline appears, chapters fill with text, and a quiz materialises. What we have not seen is a confused student, a misleading answer, or a question the course designer never anticipated. Those moments reveal whether the system can teach.

What the research actually tells us

It is tempting to ask whether AI is better than a teacher. I find that question too broad to be useful. A chatbot that hands over solutions, a tutor designed around a specific lesson, and an assistant advising a human instructor are different interventions. Their results should not be treated as interchangeable.

The most useful studies compare learning under defined conditions. Even then, an improved test score immediately after a lesson cannot tell us everything about retention months later, confidence outside the platform, or the ability to use a skill at work.

A physics tutor with encouraging results

A 2025 study by Greg Kestin and colleagues in Scientific Reports involved 194 students in a Harvard undergraduate physics course. Across two lessons, students experienced a custom AI tutor and classroom active learning in a crossover design. The classroom comparison involved active participation, not simply listening to a lecture.

The AI condition produced stronger immediate test results. Median learning gains were more than double the classroom condition, while median time using the tutor was 49 minutes against an estimated 60 minutes of classroom learning. That does not mean students became twice as knowledgeable overall; the comparison concerns improvement from the baseline on those lesson assessments.

The design deserves attention. Researchers supplied structured problems and detailed solutions, rather than trusting an unrestricted chatbot to improvise the entire lesson. This was a short study of specific physics content, not a test of a complete automated degree. I read it as evidence that carefully built tutoring can work well in a defined setting, with longer-term questions still open.

Better practice scores can hide weaker learning

Hamsa Bastani and colleagues examined a different situation in a 2025 PNAS study involving nearly a thousand high-school mathematics students. One AI interface behaved more like a general chatbot; another was designed with tutoring safeguards. Both helped students perform better while assistance was available.

Once that access was removed, the unrestricted group performed worse than students who had never received it. The abstract reports a 17 percent reduction in grades for that group, while tutoring safeguards largely mitigated the negative effect. This is a relative result within the experiment, not a prediction that every student using AI will lose 17 percent of their knowledge.

The uncomfortable implication for an online school is straightforward. If its dashboard measures only completed assignments, it can mistake assistance for learning. A student may be moving quickly because the system has removed the very decisions they were supposed to practise. We need moments when the learner works alone, followed by support when the attempt reveals a real gap.

AI can also help the person doing the teaching

The Tutor CoPilot research takes a different route: AI provides suggestions to human tutors during live sessions. The researchers report a randomised trial involving 900 tutors and 1,800 school students. In the current arXiv abstract, students whose tutors had access to the system were four percentage points more likely to master topics. Gains reached nine percentage points for students of lower-rated tutors.

Percentage points describe a difference in rates: moving from 60 percent to 64 percent would be a four-point increase. That is an illustration of the unit, not the study's reported starting rate. Tutors using the system were more likely to ask guiding questions and less likely to give away answers, although some suggestions were inappropriate for the student's grade level.

For future online schools, this opens an attractive route. An instructor could receive help finding a useful follow-up question while remaining responsible for the conversation. The study supports that particular assisted-tutoring model; it does not show that the human relationship can be removed without changing the outcome.

Evidence beyond a university classroom

A World Bank working paper evaluated a six-week after-school programme in Edo State, Nigeria, using generative AI with teacher support. Reported gains were about 0.3 standard deviations overall and 0.24 in English. A standard deviation expresses a difference relative to the spread of scores; these figures are not 30 percent and 24 percent increases in grades.

This is valuable evidence from a different educational setting. It is also evidence about a programme: structured sessions, access to technology and support accompanied the AI. We cannot assume that giving students an account without those conditions would reproduce the results, or that a short intervention establishes lasting benefits over several years.

A further exploratory UK classroom trial, described in a December 2025 preprint, studied 165 students using the Eedi platform. It reported a 5.5 percentage-point improvement in solving a new problem with supervised LearnLM support relative to its human-tutoring comparison. Human tutors reviewed every drafted message and could revise it before delivery. The study came from the LearnLM and Eedi teams; its limited size and preprint status also belong beside the result.

Taken together, these studies give us something more useful than a verdict on “AI education.” They suggest several designs worth developing and testing. A future school should be able to explain which design it uses, what it measures and what happens when a learner needs something the system cannot provide.

Building a course that has something worth learning

Let us make this concrete. Imagine creating an online course for adults organising their first community event. This is an illustrative course, not a tested product. The final task is to produce a workable plan covering the venue, responsibilities, schedule, budget and a response to bad weather.

Starting there changes the instructions we give an AI course creator. “Write a project management course” invites a broad textbook summary. “Help a beginner plan an event and explain the choices” gives the lessons a destination. An introduction to task dependencies becomes useful because the venue must be confirmed before invitations go to print.

I would build one lesson before generating the whole course. It might begin with a flawed schedule in which publicity starts before anyone has booked the space. The learner explains what could go wrong, sees a worked example, and repairs a different schedule. Only then do we introduce the formal term for the relationship they have just encountered.

AI can help draft those scenarios, propose explanations and offer variations. The author still has to solve the exercises and check whether the feedback makes sense. A quiz with a plausible but incorrect answer key is particularly troublesome: it gives a student a reason to distrust their own correct reasoning.

Source material also needs a deliberate place in the process. A system can retrieve relevant passages from an approved set of documents before composing its response. This is commonly called retrieval-augmented generation, or RAG. In everyday terms, we ask it to consult the course library rather than rely entirely on what its model has learned. Retrieval can still select the wrong passage, and the model can still misunderstand a good one.

For the event course, that library might contain the instructor's examples, planning guidance and approved lesson notes. Each important explanation should remain traceable to something the author can inspect. Technical calculations are better checked with appropriate calculation tools than accepted because a paragraph sounds confident.

Then comes a small pilot. Watch someone who is genuinely new to the topic attempt the lesson. Where do they pause? Which instruction do they interpret differently from the author? A learner who asks an unexpected question is giving you information that another round of automatic proofreading may never reveal.

Creating a lesson is only the start. Educators need to check its accuracy, clarity and usefulness for learners. Conceptual AI-generated illustration.

What an AI tutor should do when a student is stuck

Our learner now asks, “Can you just fix my schedule?” The quickest response would be a corrected plan. A more useful response might begin, “Which task depends on knowing the venue?” The learner still makes the next decision.

That does not mean a tutor should endlessly refuse to explain. Repeated hints can become irritating when someone lacks the underlying knowledge. If the learner cannot answer, the system could show one dependency, explain it plainly, and ask them to find another. If that still fails, it could revisit the prerequisite or bring in the instructor. The challenge is to provide enough help for progress without doing all the thinking.

A good course design would specify this behaviour before launch. How many unsuccessful attempts should trigger a different explanation? When is a worked solution appropriate? Which questions need a person? These are teaching decisions that should be tested with learners, including people who try to obtain answers immediately.

The author also needs to distinguish practice from assessment. During practice, a worked example can be helpful. During an independent assessment, giving that example may invalidate what the task is supposed to measure. Private assessment keys should stay outside the material a student-facing assistant can retrieve; a prompt saying “do not reveal the answer” is not a dependable access control.

This is where the research becomes practical. An online school can record both assisted progress and independent attempts. A student who succeeds with hints but struggles alone needs a different next step from one who can explain the solution without help. Showing both is more honest than turning every completed exercise into another green tick.

A useful AI tutor offers enough guidance to help a learner progress while leaving room for independent thought. Conceptual AI-generated illustration.

The future of online education could be a course that keeps changing

The scenarios that follow are possibilities built from these ideas, not claims that one platform already delivers them or forecasts with guaranteed dates. Their value would depend on reliable systems, affordable access and evidence that students learn more than they would with simpler alternatives.

Still, it is worth imagining them in detail. Otherwise “the future of AI education” remains a paragraph about personalisation at the end of another software guide.

A tutor that remembers where your understanding breaks down

Imagine returning to mathematics after years away from school. You can follow an explanation of an equation, but fractions keep interrupting your progress. A future tutor could maintain a record of what you have demonstrated across sessions and notice that the same difficulty appears in several apparently unrelated topics.

Instead of restarting an entire beginner course, it could propose a short sequence of exercises on that missing piece. A few days later, it would revisit the idea inside a different problem. If you succeed, the system updates its estimate; if you do not, it tries another route. The record would need to capture evidence of understanding, not just pages visited and minutes online.

A stored learner model is a fallible estimate. The system might misread a careless mistake as a knowledge gap or mistake a copied answer for competence. Students should be able to see and challenge important conclusions. “You seem unsure about fractions; shall we check?” is a better starting point than silently labelling someone weak at mathematics.

The possibility I find compelling is continuity. An adult studying in short sessions could spend less time explaining their situation to a new tutor every evening. But continuity should come with control over the record: what is remembered, who can see it, and whether it can follow the learner to another service.

Lessons that can see the work and listen to the explanation

A text answer often conceals the mistake that produced it. Suppose a student arrives at the wrong total in a spreadsheet. The problem might be the formula, the selected cells, or a number entered as text. Asking a chatbot “why is my answer wrong?” without showing the work leaves out most of the useful evidence.

A multimodal tutor, meaning one that can work with more than text, could inspect a shared screen and listen as the learner explains their steps. In a future course, it might ask the student to select the relevant cells, notice the missed row and guide them toward the error. The learner gets feedback closer to the moment of confusion.

The same idea could extend to a handwritten geometry proof or a spoken language exercise. It would also introduce new failure modes. A camera can capture an unclear symbol, speech recognition can mishandle an accent, and a screen may contain unrelated private information. The interface should make it clear what the tutor is examining and let the student correct what it thinks it has seen.

I would be much more cautious about a system claiming to read motivation from a face. A learner looking away might be thinking, distracted or tired. A useful future tutor could ask a direct question instead of turning ambiguous behaviour into a confident psychological judgement.

Practice inside a situation that responds

Our event-planning course could eventually become an interactive simulation. The learner books a venue, allocates a budget and assigns responsibilities. Then a supplier cancels. A fictional colleague proposes an expensive alternative. The learner has to decide what to change and explain the consequences.

Generative AI could provide varied conversations and plausible complications while a separate simulation tracks the budget and schedule. Keeping those functions distinct matters. The numbers should follow consistent rules, even if the dialogue changes. Otherwise a persuasive virtual colleague might convince the system to ignore a cost, teaching a false lesson about how planning works.

This approach could make online practice more varied than repeating the same case study. A future language course might put the student in a conversation that takes an unexpected turn. A management course could let them rehearse a difficult discussion and then review the choices they made. The scenario can be paused, replayed and examined without involving a real customer or colleague.

Immersive headsets are one possible interface, but a browser may be enough for many tasks. The useful question is what the learner gets to practise. For laboratory or technical training, a simulation would also need validated subject rules; visually convincing output alone is no guarantee of physical accuracy.

A course that updates when the subject changes

Some courses age while students are still taking them. A software interface changes, a reference document is revised, or an example stops matching the tool students use. An instructor may have to locate the affected explanation across a lesson, video script, quiz and downloadable guide.

A future AI agent could help maintain those connections. An agent is software that carries out a sequence of tasks using tools. In this scenario, it notices an approved source has changed, identifies the linked course material and proposes revisions with the differences highlighted. The instructor checks the changes before publication.

That would require more than a good writing model. Each lesson needs a clear relationship to its sources and versions. Assessments must be checked again after a revision, and students should not find that the rules of an assignment changed silently while they were working on it.

For online schools, this could shift effort toward ongoing maintenance. A course becomes a service that stays useful over time. The challenge would be deciding which changes matter educationally, rather than flooding students with an update every time the source uses a new phrase.

A school organised around projects and human meetings

If students can get routine explanations and practice support at flexible times, scheduled sessions could serve a different purpose. Imagine an online school where learners work through adaptive exercises during the week, then meet a small group to build something, debate a decision or defend an approach.

In our course, each group might present an event plan while other learners look for weak assumptions. The teacher could ask why a particular expense is necessary or what happens if attendance doubles. An AI tutor may have helped each student prepare, but the live discussion reveals whether they can think through a change they have not rehearsed.

Teachers could also receive summaries of recurring difficulties before the meeting. Such summaries would need checking, but they might help an instructor spend time on the concept troubling six learners instead of repeating material everyone already understands.

This future would make human attention a deliberate part of the design. There is also a less appealing possibility: providers use cheap automated support to remove access to people, while charging a premium for a real instructor. Whether AI broadens opportunity or creates another division in educational quality will depend partly on institutional choices, not simply on model capability.

Future online schools could combine personalised AI practice with human-led projects, discussion and collaboration. Conceptual AI-generated illustration.

Smaller schools and more specialised subjects

If preparing and maintaining materials becomes less expensive, a small team could consider subjects that previously seemed too narrow to support a course. An experienced practitioner might build a focused programme around a particular trade, local process or unusual technical skill. AI could help structure their knowledge and prepare practice variations, while the practitioner checks what the course teaches.

Language adaptation could broaden that audience. But translating sentences is only part of localisation: examples, terminology and assumptions also need to fit the learner's setting. A course that explains budgeting through unfamiliar financial arrangements may remain difficult even when every sentence is grammatically correct.

This is a plausible economic opportunity, not a promise that online schools will become almost free. Editing, support, accessibility, platform operation and credible assessment still consume resources. Voice interaction and repeated AI tutoring also create ongoing usage costs. A small pilot should reveal these costs before a school scales its enrolment.

The scarce resource may become trustworthy expertise. When almost anyone can produce a polished curriculum, learners need better ways to tell who understands the subject, who maintains the material, and who will help when something goes wrong.

Could an AI school replace teachers or universities

Some instructional tasks are easier to automate than others. Drafting another practice exercise is a bounded job. Helping a discouraged learner decide whether to continue, judging an ambiguous project or managing disagreement among students involves a wider understanding of the situation.

I would expect the boundaries to vary by course. A narrow software lesson may need relatively little personal support. A programme built around discussion, practical supervision or professional judgement is harder to reduce to automated exchanges. The studies discussed here do not establish that a fully automated school can match all of those functions over an entire qualification.

There is also the question of what a certificate means. If a student can submit work largely produced by AI, schools may put greater weight on demonstrations, oral explanations and projects whose development can be examined. A future assessment might allow AI for preparation, then ask the learner to respond to an unexpected change and explain their decision.

Universities provide more than course content, including research communities and relationships with peers and instructors. More capable tutoring would put pressure on weak teaching and expensive content delivery, but it would not automatically reproduce those other functions. A believable future can contain excellent AI-supported independent learning alongside institutions that remain valuable for different reasons.

The difficult choices behind personalised learning

A tutor that remembers your weaknesses can be useful and intrusive at the same time. Learning records may reveal repeated failures, language difficulties or private circumstances mentioned in conversation. UNESCO's guidance emphasises human agency, privacy and institutional evaluation of educational AI. Those concerns become concrete when a provider decides what to store and who can inspect it.

My preference would be for systems that collect the information needed to support learning, explain the purpose and offer meaningful control. A school does not need an unlimited archive of everything a student has ever said to determine whether they understand a spreadsheet formula.

There is a second problem that is easier to overlook: a personalised course can become too comfortable. If the system always chooses familiar examples and avoids difficult material to keep someone engaged, the learner may progress through the platform without broadening their understanding. Good teaching sometimes asks us to persist through an unfamiliar problem.

Future online schools should therefore measure more than time spent and satisfaction. Can learners solve a new task? Can they still do it later? Can they spot a misleading suggestion from the tutor? Those questions are harder to turn into a cheerful progress animation, but they are closer to what students are paying for.

The future worth building

Return to the learner with twenty minutes after work. In the more promising version of AI education, they do not need a spectacular virtual campus. They need an explanation that meets them where they are, a useful next exercise and a way to reach a person when the problem goes beyond the lesson.

AI could make that kind of support available to more people. It could also fill the internet with courses that look complete and teach very little. The difference will show up in how those courses respond to confusion, how honestly they measure progress, and whether they help people become more independent.

For me, the most interesting future is a course that gradually needs to help less. The student encounters a new problem, recognises what matters and makes a reasonable first attempt. Perhaps they still consult the tutor. This time, though, they know enough to question the answer.

Frequently asked questions

Can generative AI create a complete online course

It can help draft an outline, lesson text and assessments, with additional tools supporting media and delivery. Publishing a useful course still requires subject review, working infrastructure and testing with learners. The amount of human work depends on the topic and quality standard.

Do AI tutors improve learning

Some controlled studies show gains with specific tutoring designs. Others show that unrestricted assistance can weaken later independent performance. Look for evidence about the actual system and teaching setup, including assessments completed without help.

How could online courses change in the future

Plausible developments include persistent learning records, exercises adapted to demonstrated gaps, feedback on speech and on-screen work, and interactive simulations. These are design directions rather than guaranteed features or timelines.

Will AI make online education cheaper

It may reduce parts of production and support, but the full cost includes review, technology, maintenance and human help. Lower operating costs also do not guarantee lower prices for students.

Will students still need human teachers

The need will vary by subject and learner. Human support remains important for complex judgement, discussion, practical supervision and situations an automated tutor handles poorly. Current short-term studies cannot settle the question for an entire education.