Fine-tuning large language models (LLMs) like GPT or LLaMA for subject-specific tutoring raises significant challenges related to factual precision, alignment with curriculum standards, and mitigation of embedded biases. Education systems vary widely across regions, and ensuring that AI tutors reflect localized pedagogical goals while maintaining factual integrity is complex. Additionally, subject-specific fine-tuning often risks overfitting or amplifying domain-specific stereotypes. This question seeks to explore whether current methods in transfer learning and fine-tuning are robust enough for educational deployment across multiple disciplines and contexts, and how we can empirically evaluate their instructional accuracy and ethical soundness.