Human Experts' Evaluation of Generative AI for Contextualizing STEAM Education in the Global South

TL;DR

This study evaluates how human experts perceive the ability of Generative AI to create culturally relevant STEAM lessons in the Global South, highlighting strengths and limitations in representing local diversity.

Contribution

It introduces a culturally responsive lesson planning framework and assesses GenAI's effectiveness in contextualizing STEAM education for Ghanaian learners.

Findings

GenAI improved connection to learners' experiences

Teacher Agency was rated highest among evaluated domains

GenAI struggled with representing Ghana's cultural diversity

Abstract

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.