In the past decade, more than half of Tanzanian pupils have failed mathematics in the national Primary School Leaving Examinations (PSLEs), a problem often linked to large class sizes, limited resources, and a shortage of qualified teachers. Social robots have shown promise in supporting learning, and their integration with large language models (LLMs) enables advanced conversational tutoring capabilities. This study investigates the use of two LLM-powered NAO robots, one acting as a tutor and the other as a peer, to assist pupils in solving complex mathematics problems from past PSLEs. Recognising that LLMs are prone to errors in mathematical reasoning, the robots were deliberately programmed to make noticeable mistakes, allowing us to examine whether pupils detect these errors and how their responses shape the learning process. Data collected from 54 pupils across two Tanzanian primary schools indicate that LLM-powered robots can significantly enhance mathematics performance, with the robot tutor slightly outperforming the robot peer. However, results also reveal that pupils often accept robot-provided answers, even when recognised as incorrect, if they perceive the robot as being smart. These findings underscore both the potential and the risks of deploying autonomous robots in education, with the authority attributed to the robot being a double-edged sword, highlighting the need for designs that encourage pupils to question robot-provided solutions.

The use of social robots in education is increasingly being explored as a way to enhance learner engagement and improve learning outcomes. However, most research to date has focused on one-to-one tutoring in high-resource settings, leaving open questions about how social robots perform in group learning contexts—especially in low-resource environments. This study is one of the first to investigate human-robot interaction (HRI) in a low-resource African context, specifically in Tanzanian primary schools. We examined how a social robot tutor can support group-based mathematics learning, comparing the effects of adaptive versus non-adaptive tutoring strategies. Through an experimental, mixed-methods research design, we evaluated pupils’ learning outcomes, engagement, and classroom interactions. Our findings show that social robot tutoring has a significant positive impact on learning outcomes, with adaptive tutoring leading to slightly higher knowledge gains than non-adaptive tutoring. Qualitative observations further reveal that the presence of the robot fostered motivation, engagement, and collaborative classroom dynamics. This work demonstrates the potential of social robots to support group learning in under-resourced educational settings and highlights the importance of extending HRI research beyond well-resourced contexts.