Artificial intelligence (AI) has made remarkable strides in recent years, finding applications in various fields, including chemistry research and industry. Its integration into chemistry education has gained attention more recently, particularly with the advent of generative AI (GAI) tools. However, there is a need to understand how teachers’ knowledge can impact their ability to integrate these tools into their practice. This position paper emphasizes two central points. First, teachers technological pedagogical content knowledge (TPACK) is essential for more accurate and responsible use of GAI. Second, prompt engineering—the practice of delivering instructions to GAI tools—requires knowledge that falls partially under the technological dimension of TPACK but also includes AI-related competencies that do not fit into any aspect of the framework, for example, the awareness of GAI-related issues such as bias, discrimination, and hallucinations. These points are demonstrated using ChatGPT on three examples drawn from chemistry education. This position paper extends the discussion about the types of knowledge teachers need to apply GAI effectively, highlights the need to further develop theoretical frameworks for teachers’ knowledge in the age of GAI, and, to address that, suggests ways to extend existing frameworks such as TPACK with AI-related dimensions.

The goal of this research was to examine the change in the skills, Technological Pedagogical Content Knowledge (TPACK) and self-efficacy beliefs of chemistry teachers regarding video editing and using YouTube videos in high-school chemistry lessons, as a result of a professional development program that focused on editing YouTube videos and the accompanying teaching pedagogy. Sixteen experienced chemistry teachers participated in a professional development course regarding video editing skills and the use of videos in chemistry teaching in Israel. Research tools consisted of (1) a pre-post questionnaire, (2) interviews with teachers, (3) an analysis of the videos they edited (which were part of the course assignment), and (4) follow-up interviews conducted ten months after the end of the course. It was found that teachers improved their skills and developed a unique TPACK that combines videos with chemistry teaching needs. Self-efficacy beliefs were found to be high for most of the teachers: they all trusted in their ability to integrate videos in their chemistry teaching but not all of them were confident in their video editing skills.

A nanotechnology module was developed for ninth grade students in the context of teaching chemistry. Two basic concepts in nanotechnology were chosen: (1) size and scale and (2) surface-area-to-volume ratio (SA/V). A wide spectrum of instructional methods (e.g., game-based learning, learning with multimedia, learning with models, project based learning, and storytelling and narratives) was implemented to support students' understanding. Students' interviews and the content of students' final projects were used and analyzed to learn how using a variety of teaching methods influenced students' understanding of basic concepts in nanotechnology. In addition, the study examined which methods enhance students' understanding and which do not, according to students' perceptions. Students felt that most of the teaching methods facilitated their learning, with the exception of two activities: the Hungarian cube, and nano effect simulation; these two activities were very abstract and confused the students. Finally, we formulated recommendations regarding methods for teaching nanotechnology in the future.

Understandings of the nature of scientific inquiry (NOSI), as opposed to engaging students in inquiry learning experiences, are included in science education reform documents around the world. However, little is known about what students have learned about NOSI during their pre-college school years. The purpose of this large-scale follow-up international project (i.e. 32 countries and regions, spanning six continents and including 3917 students for the high school sample) was to collect data on what exiting high school students have learned about NOSI. Additionally, the study investigated changes in 12th grade students’ NOSI understandings compared to seventh grade (i.e. 20 countries and regions) students’ understandings from a prior investigation [Lederman et al. (2019). An international collaborative investigation of beginning seventh grade students’ understandings of scientific inquiry: Establishing a baseline. Journal of Research in Science Teaching, 56(4), 486–515. https://doi.org/10.1002/tea.21512]. This study documents and discusses graduating high school students’ understandings and compares their understandings to seventh grade students’ understandings of the same aspects of scientific inquiry for each country. It is important to note that collecting data from each of the 130+ countries globally was not feasible. Similarly, it was not possible to collect data from every region of each country. A concerted effort was made, however, to provide a relatively representative picture of each country and the world.

This paper discusses the ethical considerations surrounding generative artificial intelligence (GenAI) in chemistry education, aiming to guide teachers toward responsible AI integration. GenAI, driven by advanced AI models like Large Language Models, has shown substantial potential in generating educational content. However, this technology’s rapid rise has brought forth ethical concerns regarding general and educational use that require careful attention from educators. The UNESCO framework on GenAI in education provides a comprehensive guide to controversies around generative AI and ethical educational considerations, emphasizing human agency, inclusion, equity, and cultural diversity. Ethical issues include digital poverty, lack of national regulatory adaptation, use of content without consent, unexplainable models used to generate outputs, AI-generated content polluting the internet, lack of understanding of the real world, reducing diversity of opinions, and further marginalizing already marginalized voices and generating deep fakes. The paper delves into these eight controversies, presenting relevant examples from chemistry education to stress the need to evaluate AI-generated content critically. The paper emphasizes the importance of relating these considerations to chemistry teachers’ content and pedagogical knowledge and argues that responsible AI usage in education must integrate these insights to prevent the propagation of biases and inaccuracies. The conclusion stresses the necessity for comprehensive teacher training to effectively and ethically employ GenAI in educational practices.