Date
Publisher
arXiv
This study presents the design and development of the 21st Century Teacher Educator for Ghana GPT, a customized Generative AI (GenAI) tool created using OpenAI's Retrieval-Augmented Generation (RAG) and Interactive Semi-Automated Prompting Strategy (ISA). Anchored in a Glocalized design approach, this tool supports pre-service teachers (PSTs) in Ghana by embedding localized linguistic, cultural, and curricular content within globally aligned principles of ethical and responsible AI use. The model utilizes structured, preloaded datasets-including Ghana's National Teacher Education Curriculum Framework (NTECF), UNESCO's (2023) AI guidelines, and culturally responsive pedagogies-to offer curriculum-aligned, linguistically adaptive, and pedagogically grounded learning support. The ISA enables users to input their institution, year, and semester, generating tailored academic content such as lecture notes, assessment practice, practicum resources, and action research guidance. The design incorporates the Culture and Context-Aware Framework, GenAI-CRSciA, and frameworks addressing GenAI neocolonialism to ensure equity, curriculum fidelity, and local relevance. Pilot implementation revealed notable strengths in language adaptation and localization, delivering bilingual support in English and Ghanaian languages like Twi, Dagbani, Mampruli, and Dagaare, with contextualized examples for deeper understanding. The GPT also generated practice assessments aligned with course objectives, reinforcing learner engagement. Challenges included occasional hallucinations due to limited corpora in some indigenous languages and access barriers tied to premium subscriptions. This design case contributes to discourse on Glocalized GenAI and calls for collaboration with OpenAI NextGen to expand access and empirically assess usage across diverse African educational contexts.
Study at a glance
- Question: How can a customized generative AI tool be designed to support pre-service teachers within Ghana's national curriculum?
- Setting and data: Bagabaga College of Education in Ghana, March 2025, approximately 250 students and several faculty members.
- Method: Design case and pilot testing of a customized AI prototype with stakeholder feedback.
Why it matters
Mainstream generative AI systems often underrepresent Global South educational data, leading to concerns about cultural bias and contextual inaccuracy. Customized tools are needed to support pre-service teachers and educators in adhering to local curricula.
Key findings
- Pilot testing showed the tool enabled language adaptation and bilingual instruction in English and indigenous Ghanaian languages like Twi, Dagbani, Mampruli, and Dagaare.
- Teacher educators and pre-service teachers reported the tool addressed gaps in instructional support and effectively generated practice assessments aligned with course objectives.
- Occasional AI hallucinations occurred, particularly in underrepresented local languages lacking written transcription or sufficient digital corpora.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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