There has been widespread media commentary about the potential impact of generative Artificial Intelligence (AI) such as ChatGPT on the Education field, but little examination at scale of how educators believe teaching and assessment should change as a result of generative AI. This mixed methods study examines the views of educators (n = 318) from a diverse range of teaching levels, experience levels, discipline areas, and regions about the impact of AI on teaching and assessment, the ways that they believe teaching and assessment should change, and the key motivations for changing their practices. The majority of teachers felt that generative AI would have a major or profound impact on teaching and assessment, though a sizeable minority felt it would have a little or no impact. Teaching level, experience, discipline area, region, and gender all significantly influenced perceived impact of generative AI on teaching and assessment. Higher levels of awareness of generative AI predicted higher perceived impact, pointing to the possibility of an ‘ignorance effect’. Thematic analysis revealed the specific curriculum, pedagogy, and assessment changes that teachers feel are needed as a result of generative AI, which centre around learning with AI, higher-order thinking, ethical values, a focus on learning processes and face-to-face relational learning. Teachers were most motivated to change their teaching and assessment practices to increase the performance expectancy of their students and themselves. We conclude by discussing the implications of these findings in a world with increasingly prevalent AI.

The idea of computational thinking as skills and universal competence which every child should possess emerged last decade and has been gaining traction ever since. This raises a number of questions, including how to integrate computational thinking into the curriculum, whether teachers have computational thinking pedagogical capabilities to teach children, and the important professional development and training areas for teachers. The aim of this paper is to address the strategic issues by illustrating a series of computational thinking workshops for Foundation to Year 8 teachers held at an Australian university. Data indicated that teachers' computational thinking understanding, pedagogical capabilities, technological know-how and confidence can be improved in a relatively short period of time through targeted professional learning.

This study proposes and empirically tests a model delineating how different environmental motives would affect the practice of proactive and reactive environmental strategies, and ultimately corporate market performance. It also explores the moderating role of competitive intensity for the motive−strategy and strategy−performance relationships. By surveying manufacturing firms operating in the Pearl River Delta region of China, the study has derived several important findings. To summarize, the findings reveal that while performance‐based motivation drives the practice of proactive environmental strategy, it curtails the practice of reactive environmental strategy. Conversely, regulation‐based motive exerts exactly opposite influences on these two strategies. The findings further highlight that proactive environmental strategy directly enhances corporate market performance, and this enhancing effect will be strengthened as competitive intensity increases. However, reactive environmental strategy is found not to exert any significant direct influence on such performance, and competitive intensity does not moderate this influence too. Last, while competitive intensity does not moderate the positive influence of promotion‐based motive on proactive environmental strategy, it strengthens the negative influence of this motive on reactive environmental strategy. These findings furnish several significant academic and practical implications for pursuing corporate sustainable development.