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.

In this article, we focus on performing few-shot learning (FSL) under multi-agent scenarios in which participating agents only have scarce labeled data and need to collaborate to predict labels of query observations. We aim at designing a coordination and learning framework in which multiple agents, such as drones and robots, can collectively perceive the environment accurately and efficiently under limited communication and computation conditions. We propose a metric-based multi-agent FSL framework which has three main components: an efficient communication mechanism that propagates compact and fine-grained query feature maps from query agents to support agents; an asymmetric attention mechanism that computes region-level attention weights between query and support feature maps; and a metric-learning module which calculates the image-level relevance between query and support data fast and accurately. Furthermore, we propose a specially designed ranking-based feature learning module, which can fully utilize the order information of training data by maximizing the inter-class distance, while minimizing the intra-class distance explicitly. We perform extensive numerical studies and demonstrate that our approach can achieve significantly improved accuracy in visual and acoustic perception tasks such as face identification, semantic segmentation, and sound genre recognition, consistently outperforming the state-of-the-art baselines by 5%-20%.