Anat Yarden Group

In the realm of science education, particularly biology education, keeping abreast of new discoveries and insights is of paramount importance. During the 20222023 school year, we sent 21 scientific newsletters (SNs) via WhatsApp dealing with contemporary biological topics to 901 biology teachers in junior high schools and high schools. The aim of this survey study was to investigate how teachers perceive the possible impact of these SNs on both their own professional development and their students. We sent a questionnaire to the teachers, 110 of which were filled out. The key finding from the teachers responses was that the primary and most crucial contribution of the SNs lies in enhancing teachers biological disciplinary knowledge, contributing to their own professional development, and enhancing their students biological disciplinary knowledge. The SNs were notably less significant for teachers in terms of elevating their own or their students scientific literacy. Future examination of teachers experiences and insights may lead to a deeper understanding of how SNs influence teaching methodologies and content knowledge, and impact instructional effectiveness.

Recognizing the ecological roles of microorganisms as drivers of marine biogeochemical cycles and how they are influenced by seawater pollution, warming, and acidification is essential to understanding climate change mechanisms and their global impacts. We call for updating current science teaching standards by linking microscale processes and macroscale dynamics in the context of marine ecosystems to promote ocean and climate literacy among the next generation.

The educational landscape is rapidly evolving, driven by Generative Artificial Intelligence (GenAI) advancements. As GenAI transforms educational practices, a key area of impact is developing automatic scoring systems to efficiently and accurately assess students' problem-solving and explanation skills. This study introduces a novel prompt engineering strategy, Reflective Prompt Engineering (RPE), which applies iterative human-AI collaboration through discussion and reflection with a powerful LLM to enhance scoring performance. In RPE, human experts guide the AI by integrating its inferred criteria and language into subsequent prompts, enabling reflective alignment and improvement. We applied RPE to score open-ended biology items using analytic grading rubrics, evaluating its performance against two benchmarks: a BERT-based scoring model and multiple examples prompts with no discussion. Performance (accuracy and Cohen's Kappa) was assessed in two scenarios: within-item and cross-items. RPE achieved excellent agreement with human experts (Cohen's Kappa > 0.8) using only 40-60 examples and consistently outperformed the multiple examples strategy, achieving significantly higher Kappa values. Our findings highlight the potential of RPE to optimise AI alignment with human-designed tasks and open pathways for broader applications across diverse domains, advancing automated evaluation practices with enhanced precision and adaptability.