AI Qualitative Analysis for School Food Research

According to the PLOS ONE study published on November 3, 2026, secondary school students in Northern Benin perceived their food environments as unhealthy because nutritious foods were difficult to access, expensive, seasonal, or viewed as unhygienic.

AI qualitative analysis can help researchers organize focus group evidence across themes such as availability, affordability, food safety, culture, and urban-rural differences while keeping participant quotations and contextual nuance visible.

• The November 3, 2026, study analyzed 4 focus group discussions involving 40 students from 2 secondary schools in Natitingou and Boukombe, Northern Benin.
• The study collected discussions from students aged 11 to 15 years in lower secondary school and 14 to 19 years in upper secondary school during November and December 2022.
• Students identified 10 fruits and 18 vegetables as available in or around the schools, but the study reported that fruit consumption commonly occurred only 2 to 3 times per week.
• The November 3, 2026, findings showed that urban Natitingou offered greater food variety, while rural Boukombe had sharper seasonal and physical access barriers.
• The study reported that students proposed school gardens, orchards, vendor hygiene training, cooking demonstrations, nutrition education, and support for local food production.

The November 3, 2026, PLOS ONE study found that students perceived their school food environments as predominantly starch-based, with limited protein diversity and few vegetable-based dishes.

Houndolo and colleagues conducted the qualitative study in Natitingou, an urban municipality, and Boukombe, a rural municipality, to compare student perceptions of food available within schools and within a 1-kilometre area around them.

The researchers held 2 focus groups in each municipality, separating lower-secondary and upper-secondary students and maintaining equal gender representation. Each discussion lasted approximately 45 to 60 minutes, and the research team continued data collection until it determined that no substantively new themes were emerging.

The study used discussions in French, Ditammari, and Waama. According to Houndolo and colleagues, bilingual researchers transcribed and translated local-language material, used back-translation, and resolved discrepancies before coding.

The researchers used a hybrid deductive-inductive coding approach. Two researchers independently coded selected transcripts in MAXQDA version 24, discussed disagreements, refined the codebook, and completed a comparative analysis of urban and rural contexts.

Student quotations show that perceived food quality involved more than nutrition. One student said, "The canteen food sometimes has a bad smell, and the water tap is often closed, so we cannot wash our hands or drink." Another student summarized the rural setting by saying, "Our environment is very, very poor in terms of food in general."

The study authors reported that the data could not be made publicly available because the transcripts contained potentially identifiable and sensitive participant information. The authors stated that a minimal dataset could be requested by researchers under the study's ethical restrictions.

The November 3, 2026, findings indicate that school food interventions should address perceived access, not only the physical presence of food outlets.

The study distinguishes between an objective food environment, such as measurable availability and location, and a perceived food environment, such as whether students consider food affordable, desirable, safe, and culturally acceptable. AI qualitative analysis is especially useful for connecting these interpretive dimensions to specific excerpts.

• For nutrition researchers, code student explanations of why healthy foods are rejected, including high prices, poor hygiene, seasonal scarcity, and cultural or religious restrictions.
• For education policymakers, compare urban and rural themes before designing a single nationwide intervention.
• For food safety teams, isolate references to vendor practices, water access, handwashing, smell, taste, and illness concerns.
• For program evaluators, separate student recommendations from researcher interpretations so proposed interventions remain visibly grounded in participant evidence.
• For mixed-methods researchers, use qualitative themes to explain why objective audits and student perceptions may not produce the same picture of a school food environment.

The study authors cautioned that their findings came from 2 schools in one region and 40 participants, so the results should not be treated as representative of all students in Benin or West Africa. AI can accelerate analysis, but researchers still need to assess transferability, translation quality, group dynamics, and ethical safeguards.

The November 3, 2026, Northern Benin study demonstrates how student perspectives reveal barriers that a simple food outlet inventory could miss, including cost, hygiene, seasonality, trust, and cultural acceptability.

AI qualitative analysis gives nutrition and public health teams a practical way to examine these themes across multilingual focus groups while preserving links to participant evidence and differences between study settings.

Researchers can use Evidano to move from transcripts to thematic, frequency, and cross-segment insights while keeping human validation at the center of the workflow. Start your next qualitative project with Try Evidano for free.