Small qualitative study finds South African trainee life sciences teachers’ AI readiness varies with school resources and equity concerns

A qualitative study of 10 final-year South African pre-service Life Sciences teachers found that their perceived readiness to use artificial intelligence in teaching differed notably according to the resources available at their practicum schools. Teachers placed in higher-quintile schools generally reported greater technological exposure and confidence, while those in lower-quintile schools described infrastructure constraints, uncertainty about feasibility and concerns that AI could widen educational inequalities.

Researchers at a public university in South Africa interviewed 10 Bachelor of Education students who had recently completed Life Sciences teaching practicums in public secondary schools. Six had taught in low-quintile schools, classified as Quintiles 1–3, and four in high-quintile schools, classified as Quintiles 4–5. The study was interpretive and qualitative, so it was intended to provide contextual insight rather than statistically generalizable estimates about teachers nationally.

South Africa’s school-quintile system classifies public schools according to the socioeconomic circumstances of the communities they serve and is used in resource allocation. Quintile 1–3 schools generally serve poorer communities and may have limited infrastructure, learning materials and access to electricity and digital technologies. Quintile 4–5 schools are generally located in more affluent communities and tend to have better access to laboratories, digital resources and teaching materials, according to the study’s description of the system.

Each participant took part in an individual online interview lasting approximately 45 to 60 minutes. The researchers used an open-ended interview schedule, piloted it with three pre-service teachers and reviewed it with a three-person panel of experts in science education and educational technology. Interviews were recorded with consent, transcribed verbatim and analysed with Braun and Clarke’s reflexive thematic approach. The researchers coded the data manually, reviewed the developing themes collaboratively and attended to cases that differed from the dominant patterns, but they did not conduct member checking.

The analysis identified three connected themes concerning technology and infrastructure, perceived usefulness of AI, and pedagogical value. Participants in higher-quintile schools described environments with robotics, smart classrooms and supportive leadership, and generally saw AI as useful for engagement, interactivity and visualizing complex scientific content. Participants in lower-quintile schools emphasized problems including load-shedding, limited internet access and shortages of basic equipment such as printers and projectors. Their reservations were often described as concerns about whether AI could be used at all, rather than as opposition to its potential in principle.

School resources did not fully determine participants’ views. Neereshnie, who had practised in a Quintile 4 school, said, “I’m not particularly enthusiastic about AI. It sounds good in theory, but I’m not sure it suits our learners’ needs.” Nomathemba also described herself as more traditional and said AI did not feel like a tool she would be comfortable using. Conversely, Luxolo, whose practicum was in a Quintile 1 school, recognized that automation might save time on marking despite expressing low confidence. These accounts indicated that personal teaching preferences, digital literacy and prior exposure also shaped perceptions.

Many participants linked their confidence directly to what they had encountered during practicum. Those with little or no exposure to AI reported uncertainty, while those who had used even basic digital tools were more oriented toward its possible classroom applications. The researchers also identified a pattern they termed conditional epistemic self-efficacy: participants did not describe themselves simply as ready or unready, but said adoption depended on conditions such as demonstrable learner benefit, reliable infrastructure, appropriate training and assurance that AI would not widen existing learning gaps.

Several participants described ways they had built competence without formal AI instruction. Karabo, placed in a Quintile 2 school, said she had learned through “bits and pieces I’ve picked up from webinars and online courses,” while Ololade, in a Quintile 3 placement, described learning through informal networks of technology-proficient peers. Participants also proposed sustained training, mentorship, pilot phases, communities of practice, reliable internet and dependable electricity, supported by school governing bodies and education departments. These were participants’ recommendations, not tested interventions or measured outcomes.

The authors interpret the findings as evidence that a uniform rollout could distribute benefits unevenly because resource access, practicum exposure and institutional support differed across school settings. They also argue that these differences can compound: limited infrastructure may be accompanied by fewer opportunities to encounter AI during training, which may in turn reduce teachers’ confidence. Based on these qualitative accounts, the study calls for justice-oriented teacher preparation that combines context-responsive AI training with attention to infrastructure and learner access. The authors connect this approach to the United Nations Sustainable Development Goals on inclusive and equitable education and on innovation and resilient infrastructure.

The study has important limitations. Its sample was small and limited to pre-service Life Sciences teachers at one South African public university, and the researchers described differences between quintile groups as illustrative rather than definitive. The data were self-reported, interview questions sometimes directly referenced resource inequalities, and some wording may have implied that AI adoption was desirable. Race, home language, ability, prior AI experience and other individual background characteristics were not systematically collected. The authors therefore caution that the findings offer contextual insight rather than population-level proof, and they recommend mixed-method and longitudinal research to examine how AI-related confidence changes once teachers enter service.

Subject of Research: How school socioeconomic context and practicum experiences shape South African pre-service Life Sciences teachers’ perceived readiness to integrate artificial intelligence into teaching.

Article Title: Preservice life sciences teachers’ readiness for AI integration in teaching across varying socioeconomic contexts

Article References: Mnguni, L., Nuangchalerm, P., Sari, I. J., Mokhele, M., Camara, J. S., Van Bien, N., & Islami, R. A. Z. E. (2026). Preservice life sciences teachers’ readiness for AI integration in teaching across varying socioeconomic contexts. Discover Education, 5(1), Article 889. https://doi.org/10.1007/s44217-026-02140-2

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02140-2

Keywords: artificial intelligence in education, pre-service teachers, life sciences education, South Africa, school quintiles, digital inequality, teacher readiness, epistemic self-efficacy, teacher education, practicum experience, Preservice, life