Academics’ generative artificial intelligence (GenAI) competency is crucial for addressing the transformations it brings to higher education. Existing digital/AI competency frameworks may indirectly cope with AI competency issues needed but often overlook the impact of GenAI technology on research and assessment. Most existing AI competency frameworks for teachers focus on K–12 education, which is not appropriate for higher education. Therefore, this study aims to develop and validate a scale designed to measure GenAI Competency Self-Efficacy for Teachers and Researchers (GAICS-TR) in higher education. The scale is based on our proposed framework driven by literature and includes four domains and nine dimensions. The domains are basic GenAI understanding, teaching with GenAI, research with GenAI, and professional engagement and GenAI. The participants were 787 academics from nine universities. We used confirmatory factor analysis and group comparisons to validate the scale. Personal factors such as gender and age showed no significant differences across the scale components. The findings contribute to policies and research targeting the integration of GenAI into educational practices, as well as teacher professional development in higher education.

The advancement of artificial intelligence (AI) has attracted attention across disciplines. Different research has revealed the role of AI and the outcomes of AI in education (AIEd). However, teachers need to use AI to cater to learner diversities in mathematics education, which needs exploration. Therefore, how different AI tools assist mathematics teachers in developing teaching materials for students was investigated in this study. Teachers were invited to utilize AI techniques to develop their teaching and learning materials. The findings can be used to enhance the remedial and enrichment measures in teaching secondary mathematics and construct a framework to help teachers with learner diversities with AI tools.

Of the manuscript: is generative artificial intelligence (GenAI) a game changer for interdisciplinary collaborative learning in higher education? Correct: Is generative artificial intelligence (GenAI) a game changer for interdisciplinary collaborative learning in higher education?

Generative artificial intelligence (GenAI) and large language models (LLMs) offer new opportunities for teachers to create digital learning resources. However, limited research has examined how educators use prompting strategies to develop instructional tools in classroom contexts. This study employed an embedded single-case study design to investigate how fifteen in-service secondary mathematics teachers in Hong Kong developed Artificial Intelligence-Technological Pedagogical Content Knowledge (AI-TPACK) through participation in a professional learning network. The professional learning circle served as the primary case, with three embedded units of analysis examining the development of a Socratic tutor applet, a three-dimensional disc-method visualization tool, and an automated calculus worksheet generator. Findings indicate that teachers engaged in an iterative design process in which curriculum goals were translated into prompts and subsequently refined to produce digital learning resources. Collaborative problem-solving within the learning community helped teachers address practical challenges, including maintaining output quality across multiple interactions and ensuring the accurate presentation of mathematical notation and representations. The findings highlight the importance of AI-technological knowledge (AI-TK) in supporting teachers' effective use of AI tools and provide insights into how AI can be integrated into the design of teaching and learning resources in secondary mathematics and STEM education.