用AI生成更贴近非洲学生生活的科学教育课，专家称效果不错但需人工润色。
Human Experts' Evaluation of Generative AI for Contextualizing STEAM Education in the Global South
- 用定制AI工具生成结合当地文化的课程，提升教学关联性。
- 教师自主权评分最高，文化代表性最弱，数学与计算机课表现差。
- 适合教育AI开发者、跨文化教学研究者参考，需人工介入优化。
全球南方许多地区的STEAM教育仍抽象且脱离学习者的生活现实。本研究通过融合式混合方法，基于以人为本和文化响应型教学理念，四位STEAM教育专家评估了生成式AI（GenAI）在该地区情境化教学中的能力。专家审阅了加纳NaCCA标准教案及由定制化文化响应型课程规划器（CRLP）生成的课程。采用25项经验证的文化响应型教学量表，从偏见意识、文化表征、情境相关性、语言响应性和教师自主性五个维度量化评估。定性反思补充了教学与文化动态分析。结果表明，借助CRLP，GenAI显著增强了抽象标准与学习者生活经验的联系；教师自主性得分最高，文化表征最低；CRLP生成课程被认为更具文化根基和教学吸引力。然而，GenAI在呈现加纳文化多样性方面表现不足，尤其在数学与计算领域多为表面化引用。专家强调需教师干预、社区参与及文化引导的输出优化。未来工作应开展课堂试验、扩大专家参与，并使用土著语料库进行微调。
原文摘要 · Abstract (English)
STEAM education in many parts of the Global South remains abstract and weakly connected to learners sociocultural realities. This study examines how human experts evaluate the capacity of Generative AI (GenAI) to contextualize STEAM instruction in these settings. Using a convergent mixed-methods design grounded in human-centered and culturally responsive pedagogy, four STEAM education experts reviewed standardized Ghana NaCCA lesson plans and GenAI-generated lessons created with a customized Culturally Responsive Lesson Planner (CRLP). Quantitative data were collected with a validated 25-item Culturally Responsive Pedagogy Rubric assessing bias awareness, cultural representation, contextual relevance, linguistic responsiveness, and teacher agency. Qualitative reflections provided additional insight into the pedagogical and cultural dynamics of each lesson. Findings show that GenAI, especially through the CRLP, improved connections between abstract standards and learners lived experiences. Teacher Agency was the strongest domain, while Cultural Representation was the weakest. CRLP-generated lessons were rated as more culturally grounded and pedagogically engaging. However, GenAI struggled to represent Ghana's cultural diversity, often producing surface-level references, especially in Mathematics and Computing. Experts stressed the need for teacher mediation, community input, and culturally informed refinement of AI outputs. Future work should involve classroom trials, broader expert participation, and fine-tuning with Indigenous corpora.
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