Grading Invasive Ductal Carcinoma (IDC) from histopathological images poses significant challenges due to the dependence on input images at various magnification levels. This study presents a novel methodology based on knowledge distillation (KD) designed to bridge the gap between magnification-independent and magnification-dependent classifications of IDC grading. Unlike traditional distillation approaches, we propose an adaptive KD framework that dynamically adjusts the weight between the student loss and the distillation loss, enabling the student model to effectively balance learning from the teacher model while optimizing for specific magnifications. Our methodology employs a teacher–student architecture in which the EfficientNetV2 B3 model functions as the teacher, trained on multiple magnification levels. In contrast, the EfficientNetV2 B0 model serves as the student, trained on 40 \(\times \) or 20 \(\times \) magnifications. Results indicate that the student model exhibits exceptional performance at its trained magnification, achieving 98.41% accuracy when trained and evaluated at 20 \(\times \) . However, the performance decreases when tested across different magnifications, highlighting challenges in generalization.