Editorial
The landscape of education and…
1. Hoo, H. T., & Carless, D. (2026). From feedback literacy to feedback fluency: building habits and automaticity in generating and using feedback. Higher Education Research & Development, 1–14.
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https://doi.org/10.1080/07294360.2026.2649006
ABSTRACT
This conceptual paper introduces feedback fluency as a novel way of thinking about the long-term development of student feedback literacy. Feedback fluency is defined as habits and automaticity in generating and using feedback productively in varying sociocultural contexts. The theoretical foundations of feedback fluency emanate from academic literacies and sociocultural approaches to feedback research. Key feedback fluency features involve achieving the natural flow that comes with extensive repetition of three purposeful feedback practices: generating feedback information through feedback-seeking from different sources; attaining automaticity in processing information and making evaluative judgments across contexts; and deliberative use of feedback information for enhancement purposes. The progression from feedback literacy to feedback fluency represents a critical developmental pathway underpinned by socially and culturally appropriate study habits in working with feedback information. Student, teacher and resourcing challenges are surfaced, and ways of reducing them proposed. Implications for practice are exemplified through a practical example of a feedback-rich environment with sustained opportunities for self-assessment, peer feedback and feedback uptake. Future research possibilities include how students develop and evidence feedback fluency in diverse educational settings.
2. Wang, Z., Chai, CS., Li, J. et al. Assessment of AI ethical reflection: the development and validation of the AI ethical reflection scale (AIERS) for university students. Int J Educ Technol High Educ 22, 19 (2025).
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https://doi.org/10.1186/s41239-025-00519-z
ABSTRACT
This study developed and validated the AI Ethical Reflection Scale (AIERS), a tool for measuring university students’ ethical reflection on AI in three dimensions: AI ethical awareness, AI critical evaluation, and AI for social good. This study used a sample of 730 university students, and confirmatory factor analysis supported the threedimensional structure of the AIERS, which demonstrated good internal consistency and construct validity. Additionally, we found evidence of convergent and discriminant validity, as AI ethical reflection was positively yet modestly correlated with AI literacy, indicating that AI ethical reflection is a related yet distinct construct from AI literacy. The study also explored the differences in AI ethical reflection based on gender, academic discipline, and prior AI experience. The results revealed that female students exhibited higher AI ethical awareness than male students. Furthermore, frequent AI users reported significantly greater AI ethical reflection across all dimensions than those who used AI less frequently or had never used it. However, no significant differences were found between Science, Technology, Engineering, Mathematics (STEM) and non-STEM students. Collectively, these findings emphasize the importance of integrating ethical considerations and hands-on AI experiences from diverse perspectives of gender in AI education across disciplines to promote the ethical and responsible use of AI among university students.
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