Teaching is cognitive work, not just task execution 

Teaching has always been cognitively demanding. Long before AI entered classrooms, teachers were already engaged in continuous diagnostic reasoning: deciding what to teach next, interpreting student confusion, sequencing instruction, and adjusting explanations in real time. These are not administrative tasks; they are the cognitive heart of professional expertise. 

Cognitive Load Theory (CLT), a framework typically used to study how students learn, offers a useful lens here. CLT distinguishes between: 

• Intrinsic load (task complexity), 

• Extraneous load (avoidable effort caused by poor design), and 

• Germane load (the productive effort that builds understanding and expertise). 

Efficiency gains, cognitive losses 

Across platforms and contexts, teachers we interviewed described a consistent pattern: AI tools were undeniably efficient. Lesson plans appeared in seconds. Dashboards identified learning gaps instantly. Feedback was generated automatically. Many teachers spoke of relief, even gratitude. 

As AI systems increasingly handled planning, sequencing, diagnosis, and feedback, teachers found themselves thinking less deeply about those processes. Diagnostic reasoning became monitoring. Lesson design became template acceptance. Assessment became confirmation rather than interpretation. 

From a CLT perspective, this is not just reduced workload. It is a shift of germane cognitive load away from teachers and toward algorithms. The very mental effort through which teachers refine judgment and build expertise is gradually displaced.