The use of visual models such as pictures, diagrams and animations in science education is increasing. This is because of the complex nature associated with the concepts in the field. Students, especially entrant students, often report misconceptions and learning difficulties associated with various concepts especially those that exist at a microscopic level, such as DNA, the gene and meiosis as well as those that exist in relatively large time scales such as evolution. However the role of visual literacy in the construction of knowledge in science education has not been investigated much. This article explores the theoretical process of visualization answering the question “how can visual literacy be understood based on the theoretical cognitive process of visualization in order to inform the understanding, teaching and studying of visual literacy in science education?” Based on various theories on cognitive processes during learning for science and general education the author argues that the theoretical process of visualization consists of three stages, namely, Internalization of Visual Models, Conceptualization of Visual Models and Externalization of Visual Models. The application of this theoretical cognitive process of visualization and the stages of visualization in science education are discussed.

This paper explores pre-service life sciences teachers’ behavioral intentions toward integrating artificial intelligence into life sciences teaching. Despite the growing influence of AI in education, there is limited understanding of the factors affecting teachers’ willingness to integrate AI into life sciences teaching. These factors could inform policy-making and educational practice for AI adoption in the life sciences teaching. The current research aimed to identify key determinants influencing teachers’ behavioral intentions to adopt AI in life sciences teaching. The research followed a qualitative approach involving semi-structured interviews with 10 purposively selected pre-service life sciences teachers in South Africa. Thematic analysis was employed to analyze the data. The findings reveal that behavioral intentions are shaped by multiple factors within the framework of the Theory of Planned Behavior. Attitudes toward AI integration in life sciences education included themes such as pedagogical benefits, practical limitations, and philosophical concerns. Behavioral beliefs encompassed the advantages and disadvantages of AI adoption. Subjective norms highlighted inter-generational differences, administrative issues, stakeholder roles, and resource constraints. Normative beliefs included organizational authority, peer influence, parental concerns, and policy funding. The findings have important implications for policy and practice, highlighting the need for targeted training and resource allocation for effective AI integration in life sciences education.

In the field of biochemistry, the use of external representations such as static diagrams and animations has increased rapidly in recent years. However, their effectiveness as instructional tools can be hindered if students lack the visual literacy and cognitive skills necessary for processing and interpreting such representations. We aimed to identify and assess visualisation skills necessary for effective processing of external representations in biochemistry. We used a modified Bloom’s taxonomy to identify the cognitive skills essential for optimal visual literacy, and designed probes based on those skills to develop a test instrument. Student responses to the probes were scored and processed with the Rasch model. This approach enabled us to rate the degree of difficulty of each visualisation skill on a linear logit scale, and to generate a person–item map to measure biochemistry students’ level of visual literacy. The results showed that the identified visualisation skills could be measured reliably, and the Rasch model was effective both for ranking the skills according to level of difficulty and for estimating a student’s relative level of visual literacy. Significance: Addresses a recurring problem in biochemistry and similar fields. Identifies relevant skills to inform teaching and learning in biochemistry.

The integration of Artificial Intelligence (AI) in biology education offers transformative potential, yet teacher preparedness for AI remains under-researched. This study explores the perceived benefits of AI integration and self-reported Technological Pedagogical Content Knowledge (TPACK) among pre-service biology teachers from a South African university (n = 62) and an Indonesian university (n = 51). Using a comparative survey design, data were collected via an online questionnaire. Indonesian participants reported higher Technological Knowledge and Technological Pedagogical Knowledge levels, though both groups viewed AI integration positively, with no significant differences in perceived benefits. Among Indonesian participants, Technological Knowledge strongly correlated with perceived benefits, especially for peer collaboration. These findings highlight the importance of tailored teacher training and equitable technological resource allocation to enhance AI integration in biology education. The study underscores the critical role of technological and pedagogical knowledge in fostering positive attitudes toward AI adoption in diverse educational settings.