This study explores the transformative potential of Quantum Neural Networks (QNNs) in revolutionizing teaching and learning through the lens of non-anthropomorphic intelligence. Unlike traditional AI systems that mimic human cognition, QNNs leverage quantum mechanical principles such as superposition and entanglement to create novel computational paradigms. These architectures offer exponential improvements in processing efficiency, scalability, and learning adaptability compared to classical neural networks. The paper examines key QNN architectures and their application in personalized education, adaptive learning systems, and complex problem-solving environments, highlighting their ability to support emergent learning behaviors that are not constrained by human cognitive limitations. Empirical findings suggest that QNNs exhibit superior training efficiency, generalization performance, and the capacity to dynamically adapt to complex educational data patterns. These qualities position QNNs as powerful tools for designing highly responsive and intelligent educational environments. However, challenges remain, including hardware limitations, algorithmic interpretability, and the lack of scalable quantum infrastructure. The paper addresses these issues while proposing a roadmap for integrating QNNs into future educational paradigms. Ultimately, this research underscores the potential of quantum AI to move beyond human-centric learning models and to establish intelligent, scalable, and innovative educational ecosystems that redefine the boundaries of teaching and learning in the post-digital era