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.

翻译：全球南方许多地区的STEAM教育仍较为抽象，与学习者的社会文化现实联系薄弱。本研究探讨了人类专家如何评估生成式人工智能（GenAI）在这些情境中实现STEAM教学情境化的能力。基于以人为中心和文化响应教学法的融合混合方法设计，四位STEAM教育专家审阅了标准化的加纳国家课程与评估委员会（NaCCA）教案以及通过定制的文化响应型课程规划器（CRLP）生成的GenAI教案。定量数据通过包含25个项目的已验证文化响应教学法评估量表收集，涵盖偏见意识、文化表征、情境相关性、语言响应性和教师能动性五个维度。定性反思则进一步揭示了每节课的教学与文化动态。研究结果表明，GenAI（尤其是通过CRLP工具）增强了抽象课程标准与学习者生活经验之间的联系。教师能动性维度表现最强，而文化表征维度最为薄弱。CRLP生成的教案在文化根基性和教学吸引力方面评分更高。然而，GenAI在呈现加纳文化多样性方面存在困难，常产生表面化的文化指涉，尤其在数学与计算学科中更为明显。专家强调需要教师中介、社区参与以及对AI输出进行文化适应性优化。未来工作应包含课堂试验、扩大专家参与范围，并利用本土语料库进行模型微调。