评估大型语言模型作为AI辅导教师在低资源环境中的教学准备性：尼泊尔K-10课程的案例研究
Assessing the Pedagogical Readiness of Large Language Models as AI Tutors in Low-Resource Contexts: A Case Study of Nepal's K-10 Curriculum
AI总结：
本文评估了四个先进LLM在尼泊尔K-10课程中的教学有效性，发现存在显著的课程对齐差距，提出人机协同部署策略和课程特定微调方法。
摘要：
将大型语言模型（LLMs）整合到教育生态系统中有望普及个性化辅导，但这些系统在非西方低资源环境中的部署准备性仍严重不足。本研究系统评估了四个前沿LLM——GPT-4o、Claude Sonnet 4、Qwen3-235B和Kimi K2——在尼泊尔五年级至十年级科学和数学教育特定课程和文化框架中的功能。我们引入了一个新的课程对齐基准和受
英文摘要：
The integration of Large Language Models (LLMs) into educational ecosystems promises to democratize access to personalized tutoring, yet the readiness of these systems for deployment in non-Western, low-resource contexts remains critically under-examined. This study presents a systematic evaluation of four state-of-the-art LLMs--GPT-4o, Claude Sonnet 4, Qwen3-235B, and Kimi K2--assessing their capacity to function as AI tutors within the specific curricular and cultural framework of Nepal's Grade 5-10 Science and Mathematics education. We introduce a novel, curriculum-aligned benchmark and a fine-grained evaluation framework inspired by the "natural language unit tests" paradigm, decomposing pedagogical efficacy into seven binary metrics: Prompt Alignment, Factual Correctness, Clarity, Contextual Relevance, Engagement, Harmful Content Avoidance, and Solution Accuracy. Our results reveal a stark "curriculum-alignment gap." While frontier models (GPT-4o, Claude Sonnet 4) achieve high aggregate reliability (approximately 97%), significant deficiencies persist in pedagogical clarity and cultural contextualization. We identify two pervasive failure modes: the "Expert's Curse," where models solve complex problems but fail to explain them clearly to novices, and the "Foundational Fallacy," where performance paradoxically degrades on simpler, lower-grade material due to an inability to adapt to younger learners' cognitive constraints. Furthermore, regional models like Kimi K2 exhibit a "Contextual Blindspot," failing to provide culturally relevant examples in over 20% of interactions. These findings suggest that off-the-shelf LLMs are not yet ready for autonomous deployment in Nepalese classrooms. We propose a "human-in-the-loop" deployment strategy and offer a methodological blueprint for curriculum-specific fine-tuning to align global AI capabilities with local educational needs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
