AI FOR TEACHERS: LEARNING ANALYSIS AND TEACHING DECISIONS

Abstract

The study addressed artificial intelligence as support for data-informed teaching practice, focusing on learning analytics and instructional decision-making. It investigated how artificial intelligence could support teachers in analyzing learning and making data-based instructional decisions without compromising ethics, teacher autonomy, and the quality of academic assessment. The general objective was to analyze the use of artificial intelligence as support for data-informed teaching practice, emphasizing learning analysis and instructional decisions. A bibliographic research method was adopted through the collection and critical analysis of selected publications on teacher education, academic assessment, and dynamic learning assessment. In the development, possibilities and limitations of AI-mediated educational data use were discussed, highlighting the need for contextual interpretation by teachers, caution regarding automated inferences, and the relationship between technological support and formative assessment practices. In the final considerations, it was concluded that artificial intelligence had the potential to improve diagnosis and ongoing monitoring of learning when integrated with pedagogical and ethical criteria, preserving teacher mediation and avoiding high-stakes automated decisions, while also indicating the need for empirical investigations to complement the findings in real school contexts.