Artificial intelligence (AI) chatbots are next-word predictors built on large language models (LLMs). There is great interest within the educational field for this new technology because AI chatbots can be used to generate information. In this theoretical article, we provide educational insights into the possibilities and challenges of using AI chatbots. These insights were produced by designing chemical information-seeking activities for chemistry teacher education which were analyzed via the SWOT approach. The analysis revealed several internal and external possibilities and challenges. The key insight is that AI chatbots will change the way learners interact with information. For example, they enable the building of personal learning environments with ubiquitous access to information and AI tutors. Their ability to support chemistry learning is impressive. However, the processing of chemical information reveals the limitations of current AI chatbots not being able to process multimodal chemical information. There are also ethical issues to address. Despite the benefits, wider educational adoption will take time. The diffusion can be supported by integrating LLMs into curricula, relying on open-source solutions, and training teachers with modern information literacy skills. This research presents theory-grounded examples of how to support the development of modern information literacy skills in the context of chemistry teacher education.

This paper explores how concept maps can be structured based on researcher narration as a systems thinking (ST) approach in science education to portray the systemic nature of developmental work by chemists on solutions related to sustainability. Sustainability cannot be achieved without a systemic approach that considers all the domains of prosperity and well-being—ecological, social, and economic. Science education must respond to this challenge accordingly and find effective ways to include the ST approach. Data were collected from three semi-structured, in-depth interviews with chemists. The analysis was carried out using qualitative content analysis and modelling the systemic structures in concept maps as articulated by the chemists. The results show that authentic narratives of chemists’ developmental work can be used as material in a concept mapping exercise to reveal several ST elements and learning objectives, including leverage points and delays, that have not been presented in previous exercises. The chemists’ descriptions were also found to address the challenge of sustainability education by depicting a holistic and multidimensional picture of the reality where the developmental work is conducted. Furthermore, all three domains of sustainability were identified. The economic and industrial perspectives were especially valuable from the science education viewpoint.

Systems thinking (ST) is increasingly emphasized in sustainable chemistry education, yet teacher preparation for ST is still underexplored. This design-based research (DBR) case study reports the first implementation of a pedagogical model that integrates expert-informed learning with an ST task in a mandatory sustainable chemistry course for preservice teachers. The model operationalizes eight ST elements and was enacted around a real-world case (lignin in a circular-economy context). The aim of this study is to examine how students applied the system elements, what connections to sustainability they identified, and what difficulties they encountered in the task. Students (n = 8) worked in pairs to construct a systems model concept map (SMCM) and compose a written explanation; additional data included an individual preorganizing concept map, learning diaries, and a focus-group interview. Findings indicate that students identified relevant sustainability perspectives and represented aspects of systemic structure and behavior to varying degrees. Elements such as circular connections, delays, leverage points, and temporal perspectives were articulated more consistently in writing than in the maps. Recurrent challenges included difficulties in naming and directing links, visually representing circular connections, and distinguishing delays from broader temporal change. We suggest that SMCM can serve as an exploratory tool for surfacing a system’s structure, with the accompanying written explanation used to elaborate on students’ reasoning; clearer instructions on proposition-level linking, targeted scaffolds for cyclic and temporal reasoning, and structured debriefing (peer/self-assessment) are recommended. The model’s socio-technical emphasis, which foregrounds how the world shapes chemistry, offers a complementary pathway for preparing future chemistry teachers to integrate ST into sustainability contexts.

Artificial intelligence (AI) chatbots are the latest advance in information technology. They are next-word predictors built on large language models (LLM) that offer the possibility to process and generate information. In this theoretical article, we provide educational insights of the possibilities and challenges of educational usage of AI chatbots. The insights were produced in the context of chemical information-seeking activities designed for chemistry teacher education. The analysis was conducted via a SWOT approach using technological pedagogical content knowledge framework (TPACK) to improve the accuracy. The analysis revealed several internal and external possibilities and challenges. The key insight is that AI chatbots will change the way people interact with information. For example, they enable the building of personal learning environments with ubiquitous access to information and AI tutoring. Their ability to support chemistry learning is impressive. However, processing of chemical information reveals the limitations of current AI chatbots not being able to process multimodal chemical information. There are also ethical issues to address. Despite the benefits, wider educational adoption of AI chatbots will take time. The obstacles hindering the adoption of AI chatbots can be removed, for example, through integrating LLMs to curricula, focusing on open-source solutions, and training teachers with modern information literacy skills. This research presents theory-grounded examples of how to support the development of modern information literacy skills in chemistry teacher education. Based on the conducted analysis, we predict that AI chatbots will be a major technological change agent towards inclusive and equitable quality lifelong learning for all.

This paper presents a pedagogical model for integrating systems thinking (ST) into pre-service chemistry teacher education in the context of sustainable chemistry. The model was developed through a design-based research (DBR) project and combines conceptual preparation, expert-informed learning, and collaborative modelling. The central learning task is the System Model Assignment (SMA), in which students construct a System Model Concept Map of a contemporary, sustainability-related chemistry research topic, representing eight ST elements defined in the DBR project. The model is structured into three phases: a pre-assignment phase building the conceptual foundation, an assignment phase centred on the SMA, and a post-assignment phase consolidating learning through presentations and peer assessment. This article presents the revised version of the model in a form in which it can be adopted, with practical guidance for teachers; its revised elements have not yet been tested in practice. Supporting materials include detailed session guidance and assignment instructions. The article also presents a preliminary assessment rubric for the SMA and discusses how the model can be adapted for upper and lower secondary education.