Large Language Models (LLMs) and generative AI hold significant promise in transforming education through personalized learning, instant feedback, and adaptive tutoring. However, deploying these models without robust conversational safety measures can amplify biases, produce harmful content, and overlook cultural nuances. This work emphasizes the need to embed conversational safety mechanisms directly into the design and development of LLMs for educational contexts. We examine the core challenges, including bias, emotional insensitivity, and cultural misalignment, and propose a framework to mitigate these risks. Although policies and regulations provide overarching guidelines, we argue that continuous, context-specific refinement of LLMs supported by quantitative metrics and human oversight offers a more proactive, learner-focused safeguard. By uniting these technical and procedural strategies, AI-driven educational tools can remain inclusive, trustworthy, and attuned to the evolving demands of modern classrooms.