This study investigates how human experts evaluate the capacity of Generative AI (GenAI) to contextualize STEAM education in the Global South, with a focus on Ghana. Using a convergent mixed-methods design, four STEAM specialists assessed GenAI-generated lesson plans created with a customized Culturally Responsive Lesson Planner (CRLP) and compared them to standardized lesson plans from the Ghana National Council for Curriculum and Assessment (NaCCA). Quantitative ratings were based on a validated 25-item Culturally Responsive Pedagogy Rubric measuring bias awareness, cultural representation, contextual relevance, linguistic responsiveness, and teacher agency. Qualitative reflections provided additional insight into how GenAI handles cultural and pedagogical appropriateness. Findings show that GenAI, when paired with the CRLP tool, can support contextualized STEAM instruction by linking abstract curriculum standards to learners' cultural knowledge, community practices, and everyday experiences. Experts rated GenAI-assisted lessons as more culturally grounded and pedagogically responsive than NaCCA plans, integrating Indigenous knowledge, bilingual elements, and locally relevant examples. However, GenAI struggled to represent Ghana's cultural pluralism, often offering surface-level references to language, history, and identity. These weaknesses were most evident in Mathematics and Computing, where cultural nuance was limited. The results highlight the need for continued teacher mediation, community involvement, and culturally attuned refinement of AI outputs. Future work should include classroom trials, expanded expert participation, and model fine-tuning using Indigenous language corpora to strengthen cultural fidelity in Global South contexts.


翻译:本研究探讨了人类专家如何评估生成式人工智能(GenAI)在全球南方(以加纳为重点)STEAM教育情境化方面的能力。采用收敛性混合方法设计,四位STEAM专家评估了通过定制的文化响应式课程规划器(CRLP)生成的GenAI课程计划,并将其与加纳国家课程与评估委员会(NaCCA)的标准课程计划进行比较。定量评分基于包含25个项目的验证性文化响应式教学法量表,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等维度。定性反思进一步揭示了GenAI如何处理文化与教学适宜性问题。研究结果表明,当GenAI与CRLP工具结合时,能够通过将抽象课程标准与学习者的文化知识、社区实践及日常经验相连接,支持情境化的STEAM教学。专家认为GenAI辅助的课程比NaCCA计划更具文化根基和教学响应性,整合了本土知识、双语元素及本地相关案例。然而,GenAI在呈现加纳文化多元性方面存在局限,常对语言、历史和身份认同提供表层化引用。这些不足在数学与计算领域尤为明显,其文化细微表达较为有限。结果强调了持续教师介入、社区参与以及针对文化特性优化AI输出的必要性。未来工作应包含课堂试验、扩大专家参与范围,并利用本土语言语料库进行模型微调,以增强全球南方语境下的文化保真度。

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