AI and EdTech: The crisis of coherence
Every time UNESCO sets new priorities, challenges begin to catch up. There has been a recent focus on AI competencies, encouraging educators around the world to incorporate artificial intelligence into their classrooms to match the pace of global transformation. Even in rural communities, AI tools are being added to daily routines, changing the way students learn and interact with information. But there’s something around the next corner that most people don’t expect, and its failure is almost certain. Now is the time to get ahead of what is becoming an education crisis of consistency. Without a consistent approach that marries neuroscience with EdTech and AI, we risk designing systems that optimize short-term technical efficiency and long-term human problems.
greedy algorithm
Computer science provides a useful analogy. A “greedy algorithm” makes the best immediate choice at each step without evaluating long-term consequences. While it provides quick, visible results, it rarely provides an optimal solution. In large enterprise environments, product teams may appear to be modifying a single interface without considering the impact on the broader system. In education, this is a common pattern. Schools and software developers pursue short-term gains (faster grading, higher test scores, the promise of personalization, better overall reporting) while ignoring the deeper mechanisms that shape how individuals adapt and grow.
When technology, policy, and pedagogy move independently and at their own pace, the results may appear efficient on the surface. But behind the dashboards and data, budgets are tight, educators are exhausted, and students are at risk of becoming a number in the optimization loop. When left unchecked, the promise of AI begins to resemble a greedy algorithm. That is, a system trained to get to the next checkpoint faster without stopping to ask if it is going in the right direction for the learner.
EdTech continues to produce new tools, but the systems designed to connect “Tech” and “Ed” rarely match. The forces shaping how students learn are increasingly dominated by discrete logics: policy goals set by organizations like UNESCO and local governments, product development cycles driven by industry far outside the classroom, and, to be honest, cognitive realities that are largely absent from any of those conversations.
A coherent framework relies on these forces intersecting in a purposeful way. Artificial intelligence brings adaptive capabilities. Educational technology provides the tools to do just that. Neuroscience is based on understanding how the brain learns, remembers, and adapts to change. Together, these form a triangle that can guide the next stage of instructional design, one in which progress is measured through the long-term outcomes of each leg of the stool.
AI: Adaptive but directionless
Artificial intelligence is perhaps the most adaptable tool in the education ecosystem, but its adaptability and usefulness are only as strong as the purpose that guides it. Tools like ChatGPT’s Study Mode can interpret patterns, adjust instructions, and generate feedback faster than human systems can manage on their own. These features make it a powerful ally for personalization and responsiveness. But efficiency alone cannot justify the enormous strain it places on the surrounding financial and human systems. Without clear alignment with long-term goals, learning science, and the human context, AI can easily reinforce surface-level reporting goals instead of fostering deeper understanding and potential.
When used judiciously, AI can interpret student behavior and turn data into insights. This helps identify lapses in concentration, highlights cognitive overload, predicts gaps and redundancies in lessons, and lets you know when you need to adjust your pace. Its value is revealed by patterns that inform rather than circumvent human judgment. In a consistent framework, AI becomes an extension of educators’ ability to predict, respond, adapt, and personalize learning.
The next challenge is to ensure that what the AI observes matches what teachers and students experience. That collaboration begins with tools built around education technology to deliver, build, and measure that impact.
EdTech: Tools without pedagogy
Educational technology will determine how AI reaches the classroom. Incorporate innovation into your teaching, grading, and communication routines. However, the proliferation of new platforms often results in consistency overload. Teachers manage multiple dashboards, data streams, and logins, each of which is greedily added to the algorithm, creating new layers of fragmentation while solving small pieces of the puzzle. What is designed to streamline learning becomes another system for learning and retaining.
The problem is rarely the technology itself, but the lack of pedagogical design that connects tools to learning outcomes. If you develop EdTech without understanding how the brain processes, stores, and retrieves information, the result will be just activation rather than learning to maintain memory. Although the interface may track participation, participation alone does not indicate engagement or mastery. A purposeful EdTech ecosystem must be built around cognitive and guiding principles that guide both pacing and feedback.
For technology to be effective in education, it must function as a means of education, rather than a substitute for it. Each tool should enhance the conditions that make learning possible, such as attention, curiosity, emotion, memory, and reflection. That adjustment depends on another factor: neuroscience, which bases its design and implementation on the reality of how individuals think and learn.
Neuroscience: An anchor for how learning actually happens
Neuroscience provides the basis for understanding what makes learning possible. Learn how attention is sustained, how information is transferred from working memory to long-term memory, how emotions influence recall and motivation, and what barriers to learning arise. These are practical guideposts for designing instruction and building digital environments that support true understanding.
When learning designs match cognitive functioning, students are able to process information more effectively, retain it longer, and apply it more flexibly. Teachers recognize differences not only in test scores but, more importantly, in engagement and persistence. Neuroscience also reveals the limits of ability. No matter how advanced technology may seem, cognitive overload, stress response, and fatigue all impede learning.
AI, EdTech, and Neuroscience: A Consistent Education Ecosystem
Integrating neuroscience with AI and EdTech creates a system that adapts not only to performance data, but also to the mental and emotional states that shape performance itself. This coordination forms the basis of consistency. This is an educational ecosystem where every part of integration follows the rhythms of the human brain and ROI is measured through lasting and transferable understanding. If an LMS powered by AI and neuroscience can do all of this, there’s no need to sell anyone on a dashboard.
