Ron Blonder

Many educational programs seek to promote students' pro-environmental attitudes and behaviors, yet few are explicitly designed or examined in light of evidence-based design features known to support such change. This study presents and examines the validity of an evaluation model, the Environmental Attitudes and Behavior Model, through its application to an independently developed intervention program targeting ninth-grade students' attitudes and behaviors related to SDG 13 (Climate Action). The model is grounded in eight design features identified in the literature as effective in fostering pro-environmental attitudes and behaviors. Using a mixed-methods approach, we examined pre-post changes in the attitudes and behaviors of 23 teachers and 127 students through questionnaires, alongside interviews with six teachers and eight student groups. The findings indicate improvements in students' environmental attitudes and behaviors, as well as in their perceived importance of learning about these issues. Analysis of interview data further examined the presence of the model's design features in the intervention, leading to the suggestion of an additional design feature and the refinement of the model into a nine-feature framework. We recommend that evaluations of environmental education programs adopt a dual approach that combines assessment of learning outcomes with analysis of the presence of evidence-based design features that promote pro-environmental attitudes and behavior.

As artificial intelligence (AI), and particularly Generative AI (GenAI), is increasingly being integrated into education, preparing teachers for the AI era is essential. This study presents the design, implementation, and evaluation of a graduate-level course for in-service STEM teachers; it was designed according to six established principles of effective teacher professional development (PD) and structured around the United Nations Educational, Scientific, and Cultural Organization (UNESCO)'s AI competency framework for teachers (AI-CFT). The course lasted 14 weeks and covered all AI-CFT aspects: human-centered mindset, ethics of AI, AI foundations, AI pedagogy, and AI for PD. A mixed-methods study was used to examine the reflection of AI-CFT aspects in teachers' learning, and their trust and self-efficacy regarding AI use. The findings from two consecutive course offerings (N = 25 and N = 19) revealed significant gains in teachers' self-efficacy and their understanding of the key AI-CFT aspects. Notably, teachers' trust in AI-based educational technology did not increase, while their growing attention to the ethical challenges and limitations of GenAI was in line with the course's emphasis on critical engagement and pedagogical alignment. The research contributes to the emerging area of AI competence development by providing a PD framework that is based on UNESCO's AI-CFT.

Integrating generative artificial intelligence (GenAI) in pre-service teachers education programs offers a transformative opportunity to enhance the pedagogical development of future science educators. This conceptual paper suggests applying the GenAI tool to evaluate pedagogical content knowledge (PCK) among pre-service science teachers. By holding interactive dialogues with GenAI, pre-service teachers engage in lesson planning in a way that reveals their understanding of content, pedagogy, and PCK while facilitating the practical application of theoretical knowledge. Interpretation of these interactions provides insights into teachers-to-be knowledge and skills, enabling personalized learning experiences and targeted program adjustments. The paper underscores the need to equip pre-service teachers with the necessary competencies to utilize GenAI effectively in their future teaching practices. It contributes to the ongoing discourse on technologys role in teacher preparation programs, highlighting the potential of addressing existing challenges in evaluating and developing teacher knowledge via GenAI. The suggested future research directions aim to further investigate the GenAI usage implications in educational contexts.

The rise of digital technologies since the second half of the 20th century has transformed every aspect of our lives and has had an ongoing effect even on one of the most conservative fields, education, including chemistry education. During the Covid-19 pandemic, chemistry teachers around the world were forced to teach remotely. This situation provided the authors with an opportunity to investigate how chemistry teachers integrate technology into their teaching, compared with how the research literature suggests that it is done. The theoretical framework used in this explorative qualitative study involves chemistry teachers' technological, pedagogical, and content knowledge (TPACK). In particular, the study focused on different modes of technology integration (MOTIs) in chemistry teaching, which is a part of the teachers TPACK. In the first stage, five expert chemistry teachers were interviewed so that they could share their extensive experience with technology during online chemistry teaching. Analysis of their interviews revealed that the teachers applied 7 MOTIs in their chemistry teaching. Of these MOTIs, 4 were reported in the chemistry teaching literature: (1) using digital tools for visualization, (2) using open digital databases, (3) using computational methods, and (4) using virtual laboratories and videos of chemical experiments. In addition, the interviews revealed three new MOTIs in chemistry teaching not previously reported: (5) supporting multi-level representations, (6) enabling outreach of chemistry research, and (7) presenting chemistry in everyday life phenomena. In the second research stage, we collected the perspectives of other chemistry teachers (N = 22) regarding the 7 MOTIs. This stage enabled us to validate the findings of the first stage on a wider population and provided data to rate the importance of the seven different MOTIs according to the teachers. We wish to stress that understanding the MOTIs will not only enrich teachers theoretical knowledge base regarding integrating technology into chemistry teachingit will also contribute to chemistry teachers' preparation and professional development programs.