The ETH-TECH Narrative Report analyzes Awareness Raising Sessions (ARS) conducted in Germany, Romania, Spain, and Italy to explore how ethics of AI and data can be anchored in educational practices. Using activity theory, the sessions aimed to trigger transformative reflection among educators and students through case studies and emotional mapping. Findings reveal shared concerns across countries: lack of transparency in AI systems, insufficient institutional support, misalignment between syllabi and actual practices, and overreliance on opaque technologies. Participants emphasized the importance of human agency, accountability, and critical pedagogy, while criticizing EU ethical guidelines for being abstract and disconnected from local contexts. Country analyses showed that Germany highlighted discrepancies between curricula and classroom practices; Romania stressed students’ blind trust in AI and the need for inclusive digital environments; Spain focused on institutional opacity and the politics of proprietary technologies; and Italy underlined simulated learning, teacher disengagement, and the need for relational ethics. Emotional reactions ranged from hope to anxiety, underscoring AI’s complex impact on learning. The report proposes a preliminary framework distinguishing technological, institutional, and personal levels of ethical practice. It calls for participatory governance, teacher training, curriculum redesign, and process-oriented assessment to foster ethical AI integration. Ethics, the study concludes, must be contextualized, relational, and embedded into pedagogy rather than treated as compliance. All materials included are licensed under a Creative Commons Attribution-NonCommercial (CC BY-NC) license. This applies to all files contained within the record, unless otherwise specified. Users may share and adapt the materials for non-commercial purposes only, giving appropriate credit to the original authors.
Analysis of Awareness Raising Session Country-Level Reports and Preliminary Proposal of a Framework about Ethics of AI and Data in Education
Raffaghelli, Juliana Elisa
Writing – Original Draft Preparation
;
2025
Abstract
The ETH-TECH Narrative Report analyzes Awareness Raising Sessions (ARS) conducted in Germany, Romania, Spain, and Italy to explore how ethics of AI and data can be anchored in educational practices. Using activity theory, the sessions aimed to trigger transformative reflection among educators and students through case studies and emotional mapping. Findings reveal shared concerns across countries: lack of transparency in AI systems, insufficient institutional support, misalignment between syllabi and actual practices, and overreliance on opaque technologies. Participants emphasized the importance of human agency, accountability, and critical pedagogy, while criticizing EU ethical guidelines for being abstract and disconnected from local contexts. Country analyses showed that Germany highlighted discrepancies between curricula and classroom practices; Romania stressed students’ blind trust in AI and the need for inclusive digital environments; Spain focused on institutional opacity and the politics of proprietary technologies; and Italy underlined simulated learning, teacher disengagement, and the need for relational ethics. Emotional reactions ranged from hope to anxiety, underscoring AI’s complex impact on learning. The report proposes a preliminary framework distinguishing technological, institutional, and personal levels of ethical practice. It calls for participatory governance, teacher training, curriculum redesign, and process-oriented assessment to foster ethical AI integration. Ethics, the study concludes, must be contextualized, relational, and embedded into pedagogy rather than treated as compliance. All materials included are licensed under a Creative Commons Attribution-NonCommercial (CC BY-NC) license. This applies to all files contained within the record, unless otherwise specified. Users may share and adapt the materials for non-commercial purposes only, giving appropriate credit to the original authors.
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