Date
Publisher
arXiv
Global frameworks increasingly advocate for Responsible Artificial Intelligence (AI) in education, yet they provide limited guidance on how ethical, culturally responsive, and curriculum-aligned AI can be operationalized within functioning teacher education systems, particularly in the Global South. This study addresses this gap through the design and evaluation of GenAITEd Ghana, a context-aware, region-specific conversational AI prototype developed to support teacher education in Ghana. Guided by a Design Science Research approach, the system was developed as a school-mimetic digital infrastructure aligned with the organizational logic of Ghanaian Colleges of Education and the National Council for Curriculum and Assessment (NaCCA) framework. GenAITEd Ghana operates as a multi-agent, retrieval-augmented conversational AI that coordinates multiple models for curriculum-grounded dialogue, automatic speech recognition, voice synthesis, and multimedia interaction. Two complementary prompt pathways were embedded: system-level prompts that enforce curriculum boundaries, ethical constraints, and teacher-in-the-loop oversight, and interaction-level semi-automated prompts that structure live pedagogical dialogue through clarification, confirmation, and guided response generation. Evaluation findings show that the system effectively enacted key Responsible AI principles, including transparency, accountability, cultural responsiveness, privacy, and human oversight. Human expert evaluations further indicated that GenAITEd Ghana is pedagogically appropriate for Ghanaian teacher education, promoting student engagement while preserving educators' professional authority. Identified challenges highlight the need for continued model integration, professional development, and critical AI literacy to mitigate risks of over-reliance.
Study at a glance
- Question: Can a context-aware conversational AI prototype aligned with Ghanaian curriculum standards support teacher education effectively and responsibly?
- Setting and data: Ghana, teacher educators from Colleges of Education and a public university, five expert reviewers, system-generated logs, and policy frameworks.
- Method: Design science research with system testing, comparative policy analysis, and reflexive thematic analysis of expert feedback.
Why it matters
Global frameworks promote responsible educational AI, but few offer guidance on operationalizing ethical, culturally responsive, and curriculum-aligned systems within teacher education in the Global South.
Key findings
- The prototype demonstrated operational feasibility, maintaining text and voice conversational flow with an average end-to-end response latency of approximately 1.8 seconds while mapping queries to curriculum standards.
- Teacher education experts viewed the system as pedagogically aligned and capable of supporting pre-service training in lesson planning, microteaching, and reflective practice.
- Full teacher voice cloning and long-term conversation history persistence were not fully functional, while experts raised concerns about teacher over-reliance and uneven agent quality.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
What is the application?
Who age?
Study design
