As the field of educational technology begins its next cycle of innovation, the needs of the learner and the infrastructure built to support them change rapidly. Interest, convenience, and instant feedback have become the necessary foundation of the successful EdTech solution. By adopting the principles of mobile gaming, collaboration software built on the model of messaging, and machine-learning-driven personalised learning, the learning environments of the near future mirror the seductive consumer uses that so effortlessly capture our imagination.
Join us for an exclusive interview with BigTech’s IT Director Mikhail Filimonov as we discover the critical lessons EdTech leaders need to learn from the architectures building today’s most immersive digital experiences. Spanning the range from event-driven systems and multi-player infrastructure to scalable personalised ethical AI, Mikhail provides an exhaustive roadmap to next-generation learning platforms, learning platforms that not only engage but also show exceptional resilience, ethical consistency, and future-readiness.
1. EdTech platforms often suffer from low user engagement. What can they borrow from the playbook of messaging apps and mobile games that master the art of hooking users?
One of the greatest takeaways from consumer apps is how responsive they are to user behavior. Take games or messaging apps – immediately, you get feedback and feel compelled to keep using them. EdTech sites can do the same by embracing real-time, event-driven systems. This would entail tracking the real-time user behavior, triggering smart responses, and creating fluid learning sessions. Technologies like Apache Kafka or Pulsar can be the foundation to this, powering features like notifications, badges, and adaptive content interfaces.
There are some general guiding principles that follow. First, define a shared event schema and use tools like Confluent Schema Registry to manage updates without breaking things. Second, don’t just glance at naked click data – consider more subtle signals, like when students hit little milestones, reflect cognitive overload, or, where appropriate, affective resonance. And third, build feedback loops that react to the path of the learner, perhaps using state machines or real-time reinforcement learning to drive progression through the curriculum.
Sure, it’s not all about engagement for engagement. We have to be ethical and transparent. Using explainable AI for suggestion, limiting the number of notifications, and even slowing down features for low-bandwidth users can help us keep the experience positive and learner-centric.
2. Asynchronous communication has changed how we work and how we stay in touch. Do you think we’ll see EdTech tools designed like chat-based apps, such as Slack or Discord, in the near future?
Absolutely, and about to become reality. To serve that location we need to design the tech stack from the ground up. In-real-time conversational learning experiences require uninterrupted two-directional communication channels—primarily facilitated by WebSockets or HTTP/2 push. And they require rock-solid offline capabilities with service workers and buffered queues in sync once the user’s online.
Scalability also comes into the equation. That involves horizontally cutting the WebSocket gateways and introducing a broadcast layer – such as NATS or MQTT for rapid efficient messaging.
Data modeling must also be updated. Rather than designing on top of hard files, we’d model the interactions and conversations as documents or as graphs. This helps us to follow discussion threads, make inferences about the learner’s intention, and tie questions semantically to content. A GraphQL interface makes all the above available to frontend teams and enables co-editing and co-authoring on the fly with the help of systems like CRDTs.
When we include the AI tutor and the chatbots, things get even more involved. Stateless or short-term techniques are usually applied to handle the interactions so we don’t bog the memory. Prompt engineering, token throttling, and minimal context storage (like the vector embeddings in FAISS or Weaviate) keep the dialogues snappy and to the point.
To provide related content on the fly while conversing, we create joint embeddings user history and message intent literally and fast background inferences with simplified APIs. The premise is to provide relevance on the spot while keeping the experience intact.
3. Gamification has been around for a while, but many platforms still fall short. What game mechanics are EdTech platforms underusing that could improve retention?
Games do an exceptional job of keeping people engaged over long periods of time, almost exclusively with clever system design. One of the most genius ideas is the skill tree—a dynamic branching structure that lets people progress based on what they’re interested in and capable of. In EdTech, we do the same and move from linear course progressions to graph models that constantly adapt and change and in every node represent a skill and every edge represents a prerequisite.
These graphs live on specific databases like Neo4j or ArangoDB and can be visually rendered interactively using traversal APIs that change dynamically based on the progress the learner makes.
Adaptive difficulty offers a tremendous opportunity. Games routinely vary their levels of difficulty to match individual play style and learning technology could do the same using a combination of Bayesian Knowledge Tracing and in-real-time reinforcement learning. This allows the system to react not only to quantifiable performance measures but also to more subtle measures of engagement like hesitance or frustration. To do so on the scale we need to support requires us to use a feature store like Feast accompanied by a streaming engine like Apache Flink and supplemented with low-latency inference software like NVIDIA Triton or KServe.
We need to rethink the multiplayer concept. And instead of just depending on leaderboards, live co-collaboration can inform a richer learning experience. This requires building environments with a common state—using WebRTC or custom signaling—alongside smart matchmaking systems to match students with complementary abilities. This promotes not only a higher level of motivation but also of peer mentoring and plays a crucial role in long-term engagement.
4. Personalisation is a major strength of consumer apps. How can AI take EdTech personalisation beyond adaptive testing to deliver immersive, learner-centric experiences?
Most adaptive systems today use traditional models like Item Response Theory. They’re useful, but limited. In other words, they don’t capture how people learn over time, across different media, or in various contexts. A better approach starts with creating rich user profiles using behavioural data, preferred formats, and interaction styles. These are turned into embeddings of high-dimensional representations of each learner, using contrastive learning techniques and stored in tools like FAISS or ScaNN for ultra-fast retrieval.
On the content side, we develop a knowledge graph covering not just topics but also the associated various media types, levels of difficulty, and teaching techniques. Utilizing transformer models that have been fine-tuned on the basis of user interactions, we can provide the most optimal next moves, rather than the most difficult ones.
Real-time functionality is necessary for proper personalisation. It involves striking the perfect balance between batch pipelines, using Spark or Ray to update the models, while keeping the features dynamically up to date using Kafka Streams or Flink. Architectures like Kappa or Lambda do especially well in striking the perfect balance.
Looking beyond the limitations of the conventional screen, the future is multimodal. Sites should also customize interfaces based on run-time engagement data, switching between text, speech, and simulations as the need arises. For students with differing needs, we can also integrate speech recognition, affect-aware avatars, and user interfaces designed for sensory preferences, all synchronized in real time by the front end.
5. As a CIO, what are the biggest architectural challenges in building scalable EdTech platforms that combine chat, gamification, and real-time learning?
One of the most challenging tasks is balancing the differing needs of consistency and latency. For the latency-sensitive features like live tutoring, collaborative work, or chat, we need latency of less than 20 milliseconds and practically perfect uptime. This usually requires the use of edge servers, in-memory data storage like Redis, and support from CDNs that favor WebSocket connections. At the same time, we need to support the hard consistency of critical components like grades, milestones, and audit histories; so we use durable databases like PostgreSQL or Google Spanner.
Aligning the requirements goes toward polyglot persistence, selecting the appropriate database for the particular task. We keep the data in sync between systems using event logs and eventually-consistent patterns like CQRS, where the command sends the updates to the events and the queries read the information from materialized views.
Gamification makes things even more complex, as learner progress needs to be tracked across devices and time zones. We handle this using external state storage – solutions like Hazelcast, Redis Streams, or Akka can help preserve session continuity without storing every tiny detail permanently.
These models need to be ready to respond immediately. To keep the cold-start latency as small as possible, we preload the quantized models and use model routers to manage the traffic efficiently. The most popular inferences are cached on the edge and the autoscaling happens through Kubernetes with the help of monitoring components like Prometheus.
6. When we look out five years into the future, will EdTech move more toward entertainment or enterprise platforms? And further, what impact does this forecast have on our design and security approach today?
A significant movement toward entertainment-like learning is also underway, especially among consumer-focused platforms and younger students. In five years or so, many EdTech platforms will assume the form of multiplayer games or virtual-mixed reality environments, and 3D engines like Unity or Unreal, agent-driven AI, and live collaboration will be the norm.
These experiences will demand low-latency infrastructure and high bandwidth – think sub-20 ms lag and 50+ Mbps per user, delivered through WebRTC, QUIC, and distributed edge nodes.
Security needs to keep pace with the fast-changing environment. All user interactions and microservices require uncompromising authentication and authorisation and thus maintain zero-trust models enabled by Open Policy Agent and mutual TLS. Since the data of the learners is highly complex and time-sensitive, we will deploy differential privacy on the edge and also make sure federated models comply with GDPR and FERPA standards.
Compliance and audit will be a necessity and not a choice. Immutable logs built into services like AWS QLDB or blockchain systems will be the standard. The privacy validation will be integrated into all deployment cycles effortlessly, and access controls will respond according to the real-time risk score.
These EdTech systems of the future need to offer immersive movie-like experiences while also being secure, ethical, and utterly responsible.
