10 Lessons on How to Drive Learning With AI

Over the last two decades, I have worked across enterprise and education building AI systems.

That experience has shaped many of the lessons I have learned about applying AI thoughtfully, particularly when the goal is to support learning.

At Kyron Learning, our mission is rooted in equitable access to high quality learning. AI gives us the opportunity to extend great teaching to more students, especially when they are struggling with concepts or when faculty cannot be physically present. Done well, AI can create a safe space for students to build conceptual understanding, practice what they’ve learned, and build confidence.

But we have also seen the downsides when AI is used for learning. Many tools institutions rely on today are adapted chat experiences not designed for instruction. While students increasingly turn to them, research shows that more than 80 % of usage is focused on getting answers or generating content, not building understanding. These responses often lack curricular alignment, may be incorrect, and do little to support metacognition or conceptual learning.

These lessons reflect what we have learned designing AI that is purpose built for learning, aligned with curriculum, and trusted by faculty and students.

1. Ask the Question, Do Not Wait for It

When a teacher sits down with a student, they do not wait silently for a question. They guide the conversation. They probe understanding. They ask questions that surface misconceptions.

More than 75% of students do not yet understand a concept well enough to ask the right question. If AI simply waits, it serves the few students who already understand and fails the rest. To effectively support learning, AI must be instructor-led, asking the right questions at the right time to stimulate understanding and support mastery.

2. Make Learning Visual

Learning is not just verbal. Students understand complex ideas more easily when explanations are supported by clear, relevant visual aids that help them process and organize information.

Effective AI for learning should pair guided dialogue with purposeful visuals that reinforce the concept being taught, such as diagrams, short animations, or visual cues. When visuals are aligned to the explanation, they reduce unnecessary mental effort and give learners multiple ways to make sense of the same idea.

3. Safety is Non Negotiable

Most general purpose AI tools are designed to keep conversations going, not to keep them on track.

In learning, focus matters. Tangents can confuse students or pull them away from the core concept. Worse, they can surface inaccurate or inappropriate content. AI must include guardrails that keep interactions safe, accurate, and aligned with instructional intent.

4. Build on Learning Science, Not Just New Technology

AI is new. Learning science is not. We already understand a great deal about how people learn, how misconceptions form, and how feedback and practice drive understanding. 

When integrating AI into the curriculum, insist that it's built on these principles. When technology is grounded in learning science, it reinforces instruction instead of distracting from it.

5. Align With the Curriculum

There are many ways to teach the same concept, but students should not be learning one version in class and a different version from AI.

AI driven learning experiences must align with curriculum, learning objectives, and faculty intent. This alignment builds trust with educators and ensures that AI reinforces what happens in the classroom.

6. Make It Assignable and Part of the Course Flow

When AI sits on the side as an optional resource, it is often the most motivated students who engage with it, not necessarily the students who need the support the most.

When AI is embedded into the course and made assignable, it becomes part of the learning experience for every student. This allows instruction to be scaffolded consistently across the class, ensuring all students receive guidance and support as they work through concepts. 

7. Use AI to Surface Deep Learning Insights

AI can do more than track completion or scores.

By analyzing conversations, AI can identify where students struggle, how their thinking evolves, and which misconceptions persist. These insights give educators a deeper understanding of student learning and instructional impact.

8. The AI Instructor Matters

Students respond to instruction that feels clear, human, and intentional.

We have seen that AI guided instruction is more effective when lessons are led by a visible instructor, whether a real person or an avatar, rather than voice over visuals alone. Seeing an instructor explain and model the concept helps maintain engagement and supports understanding. Even in an AI driven experience, instructor presence matters.

9. Faculty Champions Are Key

Faculty are understandably skeptical about AI in higher education, and they should be. Trust is earned through transparency, alignment with the curriculum, and proof that the tool improves learning.

The strongest deployments we have seen start with one or two faculty champions who are eager to find new ways to engage their students. When educators help shape the experience and see the impact firsthand, adoption grows more naturally, and the AI becomes a support for instruction rather than a distraction from it.

10. Focus on Impact, Not the AI

The goal is not to deploy AI. The goal is to improve learning outcomes.

Before starting any AI deployment, institutions should define what success looks like. Is it deeper understanding, improved confidence, higher pass rates, or stronger retention? Your AI tool selection and implementation should be evaluated against those outcomes. If it does not move learning forward, it is not the right solution.

AI will play a growing role across higher education, from operations to student services. But its most important role is in the classroom and across the learning experience itself.

The question is not whether institutions should use AI, but how it is applied to support teaching and learning. When AI is designed around learning science, aligned with curriculum, and guided by educators, it can deepen understanding, extend great teaching beyond the classroom, and improve student outcomes at scale. That is where AI delivers its most meaningful value in education.