PERFORMANCE EVALUATION OF LARGE LANGUAGE MODELS IN MACHINE TRANSLATION AND TEXT CLASSIFICATION TASKS ON TWO GHANAIAN LANGUAGE DATASETS, TWI AND DAGBANI, AND THE ACADEMIC (MIS)USE CASES OF GENERATIVE AI IN GHANAIAN TERTIARY EDUCATION

The potential transformative capabilities of large language models in education, especially for developing countries could be enormous. Machine translation and classification for Ghanaian languages is a persisting challenge due to the scarcity of requisite datasets. However, the dearth of datasets for low-resourced languages poses a significant challenge to achieving the perceived benefits. We present ongoing research on assessing the adaptability of large language models to Ghanaian languages and the extent to which GenAI influences tertiary education in Ghana. We are curating datasets in Twi and Dagbani to support large language models and assessing the current state of generative AI in Ghanaian tertiary education. The proposed datasets could be utilized in downstream tasks such as named entity recognition, part-of-speech tagging, question answering and text classification.