What’s curious about the evolution of educational technology is that it happens in cascades. Whenever a new tech possibility appears, the adoption doesn’t happen in a linear way over a year or so – rather, it’s piloted, then the educators take some time to think how it really fits into the broader picture, and after that pause the methods of teaching get updated – with the tech entering a landscape it itself has helped shape.
Which is only logical – there are so many diverse types and kinds of learning, each with its own goals and learner personas, that any tech-related innovation launches a kind of cascading effect. When looking at EdTech trends for any year, you almost always encounter the second, third, or fourth wave of some tech developments from the previous years, amplified and made more meaningful. Currently, this is what’s happening to educational AI, virtual reality, and data-driven approaches.
Let’s look at the top 7 educational technology trends as of 2026 that we think will impact the very way we learn. Based on our experience with diverse projects like NIT and many others, we’ve singled out the trends that will most likely impact the public attitude and approach to education in a short-term perspective.
#1 Moving from content to dialogue
Arguably the most noticeable trend that emerged over 2025 and is likely to influence 2026 is the shift from content-based learning to AI-endorsed interaction-based approach. While AI assistants and tutors made their way into educational technology a few years earlier, so far it had been a period of experimentation and primary adjustment – like making sure the bots stay on the topic and don’t get distracted, etc.
Now that this is done, a big change is happening. While for a long time, e-learning was mostly about consuming pre-made educational content in a certain order, people are getting more comfortable with the idea of interacting with a virtual tutor that provides information based on immediate queries. In a way, the previous incarnations of digital learning were like record players where you could drop a needle on different records – and the new approach is more like a live band that does requests.
For several years, the main obstacle in personalizing eLearning experiences were the technological limitations: who was going to create personalized learning pathways? Content? Exercises? Now, though, with the advent of AI, the situation has changed. ML-powered “knowledge graphs”, now used by the likes of Squirrel AI, ALEKS, and Khan Academy, can perform tasks that look like continuous placement tests. While a traditional placement test (like determining your language level before placing you in an appropriate learner group) is done once, AI and ML-driven algorithms can perform adaptive tests as you go, constantly assessing your level of skill acquisition and fine-tuning your learning routine to address possible gaps.
This is not to say that content is gone, though. Anyone who’s ever taught something will confirm that there’s always this balance between the personalization and the need for laying out the material systematically, so it’s likely that in 2026, different e-learning tools will come up with their own balance between content and interactive AI dialogues.
#2 Dabbling in AI-generated learning content
Another trend related to AI in education that’s likely to define 2026 for many is AI-generated educational content. This is a somewhat controversial development, though – after all, we don’t allow just anyone to write textbooks in academia, and business learning is also driven by experts who can prove their credentials. So having an artificial intelligence who can’t be held accountable create learning materials is an understandably disputed move. But it’s happening – if only because it helps fuel the personalization expectations fast.
In a way, this started in traditional settings. After all, K-12 teachers and corporate learning course creators are already using tools like ChatGPT or Quizlet AI to generate things like summaries, flashcards, quizzes, and so on. The experience accumulated by this kind of sporadic use is now put to practice on a larger scale. For example, platforms like Oboe are emerging to build custom learning experiences, mixing text, audio, slides, and interactive mini-courses generated entirely by AI.
What’s going to happen in 2026 is likely that the industry will be addressing concerns about academic integrity and accuracy of the content generated by AI. Ethical frameworks and standards for academic use of AI will continue developing, too, with those winning the game who can find the right approach.
#3 Microlearning, hybrid learning, lifelong learning, microcredentialing
Everything is fast-paced now, and so is learning. Especially for adults, sitting through a large course where you actually need about 15% of the material and skills is not always affordable. Instead, learning in smaller chunks, combining in-person and remote learning, and using microcredentials for smaller courses is likely to become a major trend.
Microlearning also makes sense on its own. Some link it to GenZ’s alleged “communal ADHD”, what with every TikTok video ever and content with subs, but the reality is, it’s not about a single generation. Boomers, GenX, millennials all have awkward moments like commuting they wish they could use to some benefit. That is why eLearning courses that employ microlearning techniques have higher completion rates, from 75% to 80%.
There’s also a growing recognition of the importance of Social-Emotional Learning (SEL) in education. SEL focuses on cultivating essential life skills such as self-awareness, empathy, and relationship building, which are fundamental for academic success and personal well-being. As students navigate the complexities of hybrid learning environments, SEL initiatives equip them with the resilience and emotional intelligence needed to thrive. Moreover, community-building efforts play a pivotal role in promoting learning and platform engagement.
Finally, blockchain is now used more for keeping the learner’s credentials (we’ll return to that in a moment), and is likely to become part of the ecosystem once lifelong learning fully adopts the piecewise approach. In a way, it becomes like a busy flea market where skills are acquired and exchanged in casual transactions very fast.
#4 Gamification
Humans have liked games since days immemorial. The reasons are more or less simple: games allow us to play out practical scenarios in both technical and social areas, while removing the psychological pressure of consequences that comes with a real job.
This was tricky to implement in a learning setting without digital tech (although the proficient teachers managed to do that), but as of now, the global gamification in the education market is expected to grow from USD 860.13 million in 2021 to USD 11671.18 million by 2030.
The aspects of gamification currently used in EdTech are various and include things like
- rewards (triggering that dopamine that mitigates our urge for instant gratification)
- challenges (half of the people using Duolingo we surveyed admitted they’d stayed awake late at night to complete a challenge)
- badges (a social component)
- leaderboards (a component of competition)
- gamified content itself.
Apart from Duolingo, EdTech platforms that use gamification with great success include Brilliant, and Unacademy. One of our own projects at Lionwood was KIT, transferring gamification principles to mix them with hybrid learning and a wider community.
#5 VR, AR, AI: ethics and equity as a business factor
A good example of the cascading impact of new tech on education is how years after initial pilots, when a certain technology or format becomes mature, it’s time to address ethics and equity concerns. This is what happens with VR and AR in education – by now, their practical use has been more or less determined beyond the initial “flashy toy” stage. But as this becomes a theoretically feasible option for learners, the main question becomes practical affordability.
For example, there are still noticeable access gaps and digital divides by regions, rural vs. urban schools, and other categories. The effect being, there is a new, efficient way of learning that’s started to change the very methodology of teaching – but not everyone can afford using adaptive AI tutors or immersive VR labs. This creates a pull for new business models that make these technologies available to wider audiences, often in unique ways, with or without governmental support, with different subscription models, and so on.
This, in turn, opens a wider field of ethics, especially in AI for education: biases, representation concerns, data privacy (what with the fine-grained data that AI tutors collect), and, above all, accountability and explainability.
In 2026, we’re likely to see a rise in inclusive EdTech products, as well as the emergence of industry-specific AI ethic codes that will be viewed as a competitive edge.
#6 EdTech beyond the learning process
As much as knowledge acquisition per se matters, anyone involved in education of any kind knows there’s much more to the picture than what happens in a classroom or on a screen.
There’s also a lot of organizational, administrative, and other work that turns the gears of the learning process. What with the overall burnout problem and the proliferation of hybrid learning methods and personalized curricula, these aspects are going to make a lot more difference in the industry landscape.
One of such trends, as mentioned, is using blockchain for certifications. While previously, even when digital certificates were dealt, they could still be lost, forged, or difficult to verify. With blockchain, though, they are stored on a decentralized ledger. Examples include MIT’s blockchain-based digital diplomas and the European Blockchain Services Infrastructure (EBSI) initiative. And it’s not just about diplomas – blockchain also offers the possibility to issue stackable microcredentials for particular skills. At the same time, students can own and control their educational records and transfer them across institutions and employers without relying on middlemen.
Another big development is the focus on automation in the school administration process. Partially spurred by security concerns, and notably by the growing administrative burden, this trend now involves a lot of AI, integrations, and analytics. Schools are adopting AI for attendance and performance tracking (sometimes extended to monitoring well-being) to predict situations that warrant an intervention. Besides that, there are now more possibilities for automating the document flow and resource allocation, as well as coordinating the efforts of various departments.
#7 Rising role of data & analytics
It’s always tempting to talk of data supremacy in all things digital, but in EdTech it’s one of the major factors. We’ve already mentioned personalized learning scenarios, but they need data to function.
In fact, teachers have done data-driven tactics before digital: anyone involved can remember each individual student’s personal up- and downsides and adjust their teaching techniques accordingly. The big change is that technology can do that impersonally now, and with data and privacy regulations in place, too.
We are seeing a rise in predictive analytics used for creating learning pathways, as well as (in formal education) the rising use of blockchain for keeping student records.
Finally, a somewhat overlooked aspect that’s going to come into focus soon is how the EdTech platforms themselves have got a new ability to upgrade themselves based on anonymized data treated as statistics and insights into the market. In other words, aggregated insights about the platform users are going to be more of an asset than ever when it comes to updating the eLearning tech before we can say “2027”.
Conclusions
The evolution of educational technology over the past decade has been nothing short of remarkable, driven by the convergence of innovative technologies and evolving teaching methodologies. As we navigate the landscape of EdTech trends in 2026, it’s clear that AI has not just been a response to personalization demands, but has also launched a cascade of reflective reactions in the way learning is handled. Moreover, the rise of hybrid learning models, SEL initiatives, and chronic lack of time learners face is another driver of change. Together, these trends herald a future where education is not only accessible and engaging but also deeply meaningful and transformative. While we embrace the possibilities of EdTech, what’s really important is to continue to prioritize human connection, empathy, and lifelong learning as the cornerstones of education in the digital age.
If you have an EdTech platform of project in mind, it’s always a good idea to consult with the people who work at the intersection of education and technology. That’s exactly the kind of experts Lionwood can provide, building upon our reputation as one of Ukraine’s chief EdTech service providers. You can explore our educational software development services and have a free consultation regarding your project at any time.
