The current study presents a systematic review of teachers’ data literacy, arising from a synthesis of 83 empirical studies published between 1990 to 2021. Our review identified 95 distinct indicators across five dimensions: (a) knowledge about data, (b) skills in using data, (c) dispositions towards data use, (d) data application for various purposes, and (e) data-related behaviors. Our findings indicate that teachers' data literacy goes beyond addressing the needs of supporting student learning and includes elements such as teacher reflection, collaboration, communication, and participation in professional development. Considering these findings, future policies should acknowledge the significance of teacher dispositions and behaviors in relation to data, recognizing that they are as important as knowledge and skills acquisition. Additionally, prioritizing the provision of system-level support to foster teacher collaboration within in-school professional development programs may prove useful in enhancing teachers’ data literacy.

Artificial intelligence (AI) is increasingly recognised as a useful tool for assessment, but its specific role in English language assessment remains unexplored. We conducted a scoping review of peer-reviewed studies published between 2011 and 2025, following the framework by Arksey and O’Malley (2005). Results show seven main ways AI is used in English assessment: generating test items, using chatbots for conversation practice, marking and scoring, supporting self-assessment, enabling adaptive testing, giving instant feedback, and recognising speech. AI helps make assessments more efficient, keeps students engaged, and supports more personalised learning.

While student competitiveness may be helpful in some contexts as it is associated with increased motivation, it may also be detrimental in other contexts related to increased pressure and anxiety. But what contexts actually matter? In this research, we investigate how high-performing and low-performing contexts variably impact how different forms of competitiveness are associated with student performance. Using hierarchical linear models of Singaporean and Filipino high school students, we show how different types of competitiveness—i.e., trait, school, and perceived environmental competitiveness—have varying influence on academic achievement in reading and mathematics. In particular, we find that competitiveness is predictive of achievement for students from a low performing context like the Philippines, but only marginally for those from a high-performing context like Singapore. Our findings suggest that ‘friendly’ competition may be leveraged to help students from lower-performing contexts to perform better.