AI Doesn’t Understand Learners — Unless You Design for Sensemaking
Artificial intelligence can generate impressive responses, summarize complex ideas, and personalize content on an unprecedented scale. Yet despite these capabilities, large language models (LLMs) do not truly understand learners. They recognize patterns in data and predict likely sequences of words, but they do not possess lived experience, personal identity, motivation, or meaning-making capacity.
This distinction matters profoundly in K–16 education.
Learning is not simply the accumulation of information. Research from Harvard Graduate School of Education's Project Zero emphasizes that deep understanding develops when learners actively make meaning, reflect on their thinking, and connect new ideas to their experiences, identities, and goals. Students continuously ask questions that extend beyond factual accuracy:
Who am I becoming?
Why does this matter to me?
How does this connect to my future?
What meaning should I draw from this experience?
These are sensemaking questions, and they sit at the heart of human learning.
When educational systems treat learning as a text prediction problem, they risk confusing information delivery with understanding. An AI system may provide a correct explanation of a scientific concept, but only the learner can determine how that knowledge fits into their identity, interests, and worldview.
The opportunity for educators is not to replace sensemaking with AI, but to design learning experiences where AI supports sensemaking.
The K–16 Lens: Identity Development, Motivation, and Meaning
From elementary school through higher education, learners are actively constructing identities.
Research on student engagement and motivation consistently demonstrates that students learn more deeply when they perceive relevance and personal connection. Resources from Edutopia's Student Engagement Center highlight how belonging, relevance, and learner agency strengthen educational outcomes.
A student who sees themselves as a future scientist engages differently with biology than a student who sees science as disconnected from their life. A college student exploring entrepreneurship interprets feedback differently than one focused solely on earning a grade.
Similarly, the work of the Collaborative for Academic, Social, and Emotional Learning (CASEL) emphasizes that self-awareness and identity development are foundational components of long-term success.
AI can assist with reflection prompts, personalized feedback, and narrative exploration. However, these benefits emerge only when educational experiences are intentionally designed around learner identity rather than content delivery alone. The challenge is not making AI smarter. The challenge is making learning more human.
The ESTE Lens: Science + Technology
Within the ESTE Framework, this challenge sits at the intersection of Science and Technology.
Science: Exploring the Unknown
Science thrives on questions. Researchers in the learning sciences continue exploring how learners develop identity, motivation, curiosity, and meaning. Organizations such as the Learning Sciences Research Institute (LSRI) investigate how people construct understanding and transfer knowledge across contexts.
Science asks:
How do learners construct understanding?
What drives engagement and persistence?
How does identify influence learning outcomes?
Technology: Applying What We Know
Technology translates knowledge into practical tools. AI systems can support reflection, personalize pathways, and provide adaptive learning experiences.
Recent guidance from UNESCO's Generative AI in Education and Research Initiative encourages educational leaders to focus on human-centered AI implementation that enhances learning rather than simply automating tasks.
Similarly, the Organisation for Economic Co-operation and Development (OECD) continues examining how AI can support future-ready learning ecosystems while preserving critical human capacities such as judgment, creativity, and meaning-making.
Technology asks:
How can we operationalize insights about learning?
How can tools support individuals growth?
How can systems become more responsive to leaner needs?
When Science informs Technology, AI becomes more than a content generator. It becomes a support structure for reflection, exploration, and growth.
Grounding AI in learner identity enables greater relevance, deeper engagement, and more meaningful educational experiences.
Bright Spot: AI Supporting Personal Narratives
Across education, promising initiatives are emerging that use AI to help learners articulate their personal stories rather than simply consume information.
Examples include:
Reflective journaling platforms that help students identify patterns in their learning journeys.
Career exploration tools that connect academic experiences to future aspirations.
Narrative-based advising systems that encourage students to reflect on goals, strengths, and growth.
These approaches align with emerging research in the learning sciences showing that learners retain knowledge more effectively when they integrate it into personal narratives and identity development.
Learning becomes more durable when learners see themselves within the story of what they are learning. Rather than replacing human reflection, AI can create opportunities for deeper self-discovery.
Call to Practice
Reflect on Meaning, Not Just Accuracy
This month, use a journal to explore your interactions with AI. After receiving a response from an AI system, ask:
Does this response feel meaningful or merely informative?
What aspects of my experience, identity, or goals are missing?
How would I modify the prompt to better reflect who I am and what I value?
What insights emerged through my own reflection rather than from the AI itself?
Notice when AI helps you make sense of an idea and when it simply predicts text. The difference reveals an important truth: Understanding is ultimately created by learners, not generated by algorithms.
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