Human Experts' Evaluation Of Generative Ai For Contextualizing Steam Education In The Global South

Matthew Nyaaba, Macharious Nabang, Patrick Kyeremeh, Ibrahim Nantomah, Collins Owusu-Fordjour, Martin Ako, Bismark Nyaaba Akanzire, Kassim Korah Nantomah, Cecilia Issaka, Xiaoming Zhai

Date

11/2025

AI Overview

Why it matters

STEAM curricula in the Global South often rely on Western-centric, abstract content that disconnects classroom learning from students' sociocultural realities. While teachers increasingly turn to generative AI for lesson planning, AI-generated lessons frequently omit essential cultural and linguistic contexts.

Study at a glance

Question: How do experts evaluate the cultural responsiveness and pedagogical quality of generative AI-generated STEAM lesson plans compared to national standards?
Setting and data: Ghana; 4 STEAM education experts evaluating 4 national curriculum lesson plans and 4 AI-generated lesson plans across primary and junior high school subjects.
Method: Convergent mixed methods combining quantitative rubric evaluations (4-point scale) with qualitative thematic reflections.
AI tools: Culturally Responsive Lesson Planner (CRLP), ChatGPT

Key findings

Experts rated AI-generated lessons highest for teacher agency (mean 3.6 of 4) and contextual relevance (3.4), outperforming standard national plans in localizing examples, activities, and resources.
Cultural representation scored lowest across all domains (mean 2.25 of 4), with particular weaknesses in mathematics (1.2) and computing (2.4) in affirming diverse identities.
Although AI tools integrated bilingual support and local terms, reviewers found they frequently produced inaccurate translations, superficial historical references, and risked subtly reproducing stereotypes.

Summary drafted with AI from the full paper and may contain errors. See the full paper for details.

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.