Static: They cannot adapt to a student's individual pace or reading level.
Hard to Scale: Creating alternative representations for every student requires arduous human effort.
Generic: They often fail to connect with a student's personal interests or motivations.
Static: They cannot adapt to a student's individual pace or reading level.
Hard to Scale: Creating alternative representations for every student requires arduous human effort.
Generic: They often fail to connect with a student's personal interests or motivations.
Transforming static text into multiple representations—simplifying language or summarising key points without losing the core facts.
Adjusting content based on age group, language proficiency, focus levels, and personal interests.
Pedagogical Safety
Ensuring that while the delivery changes, the educational learning objectives and success criteria remain accurate and rigorous.
How It Adapts to the Learner
Relatability & Interest:
Description: The AI rewrites examples to match the user's hobbies (e.g., explaining physics concepts using soccer or music references).
Simplicity & Proficiency:
Description: Adjusting the reading level (Lexile score) to match the student's grade level and language proficiency.
Conversational Interaction:
Description: Turning passive reading into an active dialogue, allowing students to "chat" with their textbook to ask clarifying questions.
Brevity & Focus:
Description: offering concise summaries and "snackable" content for learners who need focused, shorter bursts of information.
LearnLM team compared "Learn Your Way" against standard textbook usage. The results highlighted significant advantages in learning outcomes when using the AI-augmented approach. By weaving together learning science and cutting-edge technology, we can move beyond the static page.
Based on the paper "Towards an AI-Augmented Textbook" by the LearnLM Team, Google (2025).