{"ID":2893575,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.13205","arxiv_id":"2507.13205","title":"Automatically assessing oral narratives of Afrikaans and isiXhosa children","abstract":"Developing narrative and comprehension skills in early childhood is critical for later literacy. However, teachers in large preschool classrooms struggle to accurately identify students who require intervention. We present a system for automatically assessing oral narratives of preschool children in Afrikaans and isiXhosa. The system uses automatic speech recognition followed by a machine learning scoring model to predict narrative and comprehension scores. For scoring predicted transcripts, we compare a linear model to a large language model (LLM). The LLM-based system outperforms the linear model in most cases, but the linear system is competitive despite its simplicity. The LLM-based system is comparable to a human expert in flagging children who require intervention. We lay the foundation for automatic oral assessments in classrooms, giving teachers extra capacity to focus on personalised support for children's learning.","short_abstract":"Developing narrative and comprehension skills in early childhood is critical for later literacy. However, teachers in large preschool classrooms struggle to accurately identify students who require intervention. We present a system for automatically assessing oral narratives of preschool children in Afrikaans and isiXh...","url_abs":"https://arxiv.org/abs/2507.13205","url_pdf":"https://arxiv.org/pdf/2507.13205v2","authors":"[\"Retief Louw\",\"Emma Sharratt\",\"Febe de Wet\",\"Christiaan Jacobs\",\"Annelien Smith\",\"Herman Kamper\"]","published":"2025-07-17T15:15:43Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"eess.AS\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}