Cerebrocentric bias in stroke terminology and the hemovascular paradigm: a conceptual reframing of cerebrovascular disease classification.

AI technologies have moved from a peripheral topic in education to a practical element of instructional design, assessment, and professional learning. This shift changes the meaning of teacher competence: teachers are increasingly expected to interpret AI-generated outputs, design learning activities around AI-supported tools, and make informed ethical decisions about their use (; ). Two broad research directions frame this review. The first uses artificial intelligence to assess or model teachers, drawing on ML, LA, and related computational techniques to analyse teacher portfolios, classroom observations, accreditation records, or survey data (; ). The second studies AI as part of teachers' own professional competence, especially as generative AI requires new forms of pedagogical, technical, and ethical judgement (; ). Three contextual studies outside the scoping review's core corpus help position the present review. proposed a deep-learning framework for assessing teacher performance from classroom interaction data and reported that automated modeling can capture instructional interaction patterns relevant to teacher evaluation. examined AI-related technostress among middle school teachers and showed that AI adoption also creates psychological and organizational pressures that must be considered when discussing teacher competence. developed a conceptual model and illustrative case for integrating AI tools into teacher professional learning, emphasizing that AI-supported professional development requires pedagogical design rather than tool adoption alone.