Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Education and Culture Executive Agency (EACEA). Neither the European Union nor EACEA can be held responsible for them. Project number: 2023-1-NL01-KA220-HED-000155675. Benefits for Teachers: - The LIS augments teacher support: The system's data-driven insights enhance teachers' ability to identify struggling students early on. This allows for more proactive and focused interventions to keep students on track. Implications for practice N/A Case Study 35: Attention to diversity from artificial intelligence. General information Reference/Source: Domínguez-González, M. de los Á., Hervás-Gómez, C., DíazNoguera, M. D., & Reina-Parrado, M. (2023). Attention to diversity from artificial intelligence. The European Educational Researcher, 6(3), 101–115. https://doi.org/10.31757/euer.633 Institution: University of Seville, Spain (public institution) Course/subject: Information and Communication Technology applied to Education, within the Degree in Primary Education Aim: - To explore how teachers in training design prompts (i.e., questions or instructions used with AI) that align with Bloom's Taxonomy and address the needs of students with Special Educational Needs (SEN). - Analyse teachers-in-training's understanding and application of Bloom's Taxonomy and their ability to create effective AI prompts for inclusive learning. Target group: Pre-service teachers enrolled in an undergraduate education course. Description of case Overview: - Students were asked to write an AI prompt focusing on students with SEN before and after receiving AI training sessions. - Prompts were classified according to Bloom's Taxonomy levels (remember, understand, apply, analyse, evaluate, create). - Researchers analysed the distribution of prompts across different taxonomy levels, as well as patterns and trends.
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