This study examined the effectiveness of the AI-powered plant classification tool, PlantNet, in enhancing Rwandan secondary school students’ conceptual understanding and engagement in plant classification. Anchored in constructivist learning theory, a sequential explanatory research design was employed involving 149 students (control = 74; experimental = 75) from both rural and urban schools, along with eight biology teachers. Data were collected through pre- and post-tests, a Likert-scale engagement questionnaire, and semi-structured teacher interviews. Quantitative data were analyzed using descriptive and inferential statistics in Statistical Package for the Social Sciences (SPSS), including t-test, while qualitative data were analyzed thematically. Results revealed a statistically significant improvement in students’ conceptual understanding and engagement in the experimental group compared to the control group (p = 0.006; p<0.01), with an effect size of Cohen’s d = 0.61, a practically meaningful (moderate to large) effect in educational research. Subgroup analysis also indicated consistent learning gains across gender and school location. Thematic analysis of teacher interviews supported the quantitative findings, highlighting enhanced student autonomy, curiosity, and confidence when using PlantNet. Although some technical challenges were reported, such as limited accuracy due to unclear images and poor internet access, teachers positively viewed the integration of AI tools into biology instruction. This study provides novel evidence on the pedagogical value of AI-powered plant identification in low-resource contexts, an area rarely explored in African secondary education. Future research should examine long-term learning retention, scalability, and integration of AI tools across other science disciplines to further advance technology-enhanced biology education.
Gatarira et al. (Mon,) studied this question.