Background: The current rapid development demands educators to possess innovative behaviour. Presently, in the environment of rapid technological growth and borderless global communication, teachers with innovative behaviour are capable of facing each painful challenge that confronts the education system; therefore, it is essential to understand the factors influencing the innovative behaviour of teachers. Purpose: To identify the influence of teachers’ self-efficacy and school administrators’ transformational leadership practices on teachers’ innovative behaviour. Method: A quantitative approach using a cross-sectional survey design with a sample of 1415 teachers from four states in Malaysia, and the data were statistically analysed using SPSS^® version 26.0 for Windows™ (IBM Corporation, New York, NY, USA). Result: Multiple Regression Analysis found that teachers’ self-efficacy and school administrators’ transformational leadership practices both had a significant influence on teachers’ innovative behaviour by contributing 47.0% of the variance in teachers’ innovative behaviour. Conclusion: The findings suggested that teachers’ self-efficacy and school administrators’ transformational leadership practices both play a role in influencing teachers’ innovative behaviour. Therefore, the stakeholders need to consider the aspects of self-efficacy and transformational leadership practices of school administrators in drafting policies and related programmes to improve teachers’ innovative behaviour.

Youths are a dominant cluster in Malaysia’s population. They need to be resilient as they are the anchor of our country’s development. Resilience is often associated with Adversity Quotient (AQ). There are many instruments developed across contexts and countries; however, good validity and reliability in measuring the AQ of youths are still scarce. Therefore, this study seeks to examine whether the items developed for the Malaysian Youth Adversity Quotient Instrument (MY-AQi) in measuring AQ within the context of Malaysian youths managed to manifest good psychometric properties using Rasch analysis. Apart from that, this study aims to measure the construct validity of the MY-AQi items using confirmatory factor analysis (CFA). Four constructs of AQ were established, namely control, ownership, reach, and endurance. A total of 1000 youths were assigned using stratified random sampling from five zones in Malaysia: northern, western, southern, Borneo, and eastern. Analysis from WINSTEPS 3.71 has revealed that 27 items were able to meet Rasch statistical modeling, while CFA has shown that 12 AQ measuring items could function well. The findings of this study demonstrated the strengths of the psychometric aspects by combining Rasch and CFA analysis to prove item quality and construct stability. Future studies should endeavor to develop more items to establish better youth profiles and combine AQ with other appropriate variables, such as achievement and demography. Nevertheless, some limitations can be scrutinized including a report on the more in-depth errors in the analysis conducted.

Generative artificial intelligence (AI) has rapidly evolved, demanding new forms of literacy that go beyond traditional AI concepts. However, current definitions of generative AI literacy often overlook its unique challenges, including prompt engineering, critical evaluation of AI-generated outputs, and complex ethical considerations. This study addresses these gaps through an exploratory review of 20 peer-reviewed articles. These articles were identified using systematic searches across major academic databases and selected based on predefined inclusion criteria. The analysis reveals conceptual limitations in existing frameworks, particularly their lack of structure and their generalization of AI literacy. To overcome these issues, we propose a new competency framework adapted from Bloom’s taxonomy. The framework integrates three essential dimensions: technical proficiency, ethical responsibility, and societal awareness. It is organized into five progressive cognitive stages: understand, apply, analyze, evaluate, and create. This framework clarifies the distinct demands of generative AI literacy and can be implemented to guide curriculum design, professional training, and the development of generative AI literacy across sectors.