ELF: Educational LLM Framework of Improving and Evaluating AI Generated Content for Classroom Teaching

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Recent studies [48, 7] have demonstrated that Large Language Models (LLMs), like ChatGPT [3, 46] and LLAMA [59], can assist with routine teaching tasks and have the potential to revolutionize traditional education. However, other studies [35] highlight that LLMs often contain inaccuracies and demonstrate limited effectiveness in educational contexts. To address this issue, we propose a unified Education LLM Framework ( ELF ) that integrates LLM into classroom teaching practice to enrich high-quality dialogical content and teacher-student interactions. Unlike complex data-driven models that require vast amounts of data, our framework can quickly enhance educational engagement and teaching strategies by utilizing a few carefully selected teaching examples from master teachers with our prompting techniques. We focus on two typical classroom teaching scenarios that require AI-generated content: Dialogue Completion and Expertise Transfer Learning. The former scenario requires generating contextually appropriate dialogues, while the latter scenario requires migrating the instructional styles and organization to new teaching topics. We demonstrate the effectiveness of our data quality-centered approach in generating semantically clear and factually accurate content as organized instructions for teaching materials. We comprehensively evaluate these materials by utilizing Perplexity-based Statistical Evaluation, Human Evaluation with Questionnaires (HEQ), BertScore, Rouge, and BLEU. Experiments on two self-collected datasets show that our method significantly improves various metrics in Dialogue Completion and Expertise Transfer Learning tasks, enhancing the overall utility of AI for educational purposes.