Differentiating Instruction with AI

Differentiated instruction, as described by Carol Ann Tomlinson, adjusts four elements: content (what students learn or how they access it), process (how they make sense of it), product (how they show learning) and learning environment. Teachers differentiate based on readiness, interest and learning profile. AI fits most naturally with content and process: leveling texts, generating scaffolds and producing tiered practice. Tools such as Diffit are built for this, taking a text or topic and producing a version at a chosen reading level with vocabulary lists and questions. General chatbots can do similar work with a clear prompt. The time saved is real, since writing three versions of a reading by hand can take an evening. The main risk is watering down. A simplified text that drops the key concepts, or replaces precise vocabulary with vague words, gives some students less to learn rather than a different route to the same learning. Good leveled versions keep core ideas and important terms and add support, such as definitions, shorter sentences and visuals. A second risk is fixed tracking: if the same students always get the easiest version, expectations drop. Flexible grouping, reassigned based on recent evidence, avoids this. Scaffolding, a term introduced by Wood, Bruner and Ross in 1976 and often linked to Vygotsky's zone of proximal development, means temporary support that is removed as competence grows. AI makes it easy to add supports, but teachers must also plan to fade them. A common misconception is that differentiation means matching instruction to 'learning styles' such as visual or auditory learners. A 2008 review by Pashler and colleagues found little evidence that matching teaching to learning styles improves outcomes. Differentiation is better grounded in readiness and evidence of what students currently understand.