From Self-Check to Reflection: Tools for Individual and Institutional Self-Awareness [Technical Document]

This record presents the technical document for the ETH-TECH Self-Reflection Tool, developed as part of Work Package 2 (WP2) of the ETH-TECH project. Originally conceived as self-check tools, the project output evolved into self-reflection tools designed to provide an interoperable structure to self-assess one's position relative to European ethical guidelines for Artificial Intelligence (AI). The tool aims to foster awareness, critical thinking, and practical application of these ethical principles, especially in areas that are less familiar or more challenging to implement. Beyond encouraging personal and institutional reflection on AI ethics, it seeks to transform abstract guidelines into actionable insights, supporting an ethical and responsible approach to AI adoption and development in education. Hosted on the ETH-TECH project website (ETH-TECH Self-Reflection Tool), this interactive resource consists of five main components: The European Commission’s Ethical Guidelines for Trustworthy AI – provides key concepts, keywords, and linked explanations to enhance understanding. How to Use the Self-Reflection Tools—introduces the tool and guides users step-by-step through the reflection process. How to Read Reports and Graphs—offers instructions for interpreting visualizations to deepen understanding and strengthen ethical awareness. Individual Self-Reflection—focuses on personal beliefs, practices, and ethical attitudes. Institutional Self-Reflection—focuses on organizational policies, strategies, and responsibilities. Each section is structured around the dimensions of the EU ethical guidelines and is followed by comparative reports and visual outputs that aggregate data from other participants. These features enable users to benchmark their responses, support reflection, and identify areas for potential improvement. Developed on the Qualtrics platform, the tool is available in six partner languages: Italian, English, Romanian, German, Spanish, and Catalan. The Zenodo record includes a technical document that provides: An introduction to the Self-Reflection Tool; Detailed descriptions of the Individual and Institutional tools, including screenshots and explanations of each section; Presentation of the generated reports; Preliminary results from testing sessions using the Individual tool, with initial analysis and reflections. This project deliverable is the result of joint collaboration between all project partners, who contributed through iterative feedback, user testing, and linguistic review. Final document authored by the UNIPD team (Work Package Leader). 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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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-11-28
DOI
https://doi.org/10.5281/zenodo.17638642
Primary Topic
E-Learning and Knowledge Management
Type
article
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article

From Self-Check to Reflection: Tools for Individual and Institutional Self-Awareness [Technical Document]

Zenodo (CERN European Organization for Nuclear Research)
E-Learning and Knowledge Management
article

From Self-Check to Reflection: Tools for Individual and Institutional Self-Awareness [Technical Document]

article en

Abstract

This record presents the technical document for the ETH-TECH Self-Reflection Tool, developed as part of Work Package 2 (WP2) of the ETH-TECH project. Originally conceived as self-check tools, the project output evolved into self-reflection tools designed to provide an interoperable structure to self-assess one's position relative to European ethical guidelines for Artificial Intelligence (AI). The tool aims to foster awareness, critical thinking, and practical application of these ethical principles, especially in areas that are less familiar or more challenging to implement. Beyond encouraging personal and institutional reflection on AI ethics, it seeks to transform abstract guidelines into actionable insights, supporting an ethical and responsible approach to AI adoption and development in education. Hosted on the ETH-TECH project website (ETH-TECH Self-Reflection Tool), this interactive resource consists of five main components: The European Commission’s Ethical Guidelines for Trustworthy AI – provides key concepts, keywords, and linked explanations to enhance understanding. How to Use the Self-Reflection Tools—introduces the tool and guides users step-by-step through the reflection process. How to Read Reports and Graphs—offers instructions for interpreting visualizations to deepen understanding and strengthen ethical awareness. Individual Self-Reflection—focuses on personal beliefs, practices, and ethical attitudes. Institutional Self-Reflection—focuses on organizational policies, strategies, and responsibilities. Each section is structured around the dimensions of the EU ethical guidelines and is followed by comparative reports and visual outputs that aggregate data from other participants. These features enable users to benchmark their responses, support reflection, and identify areas for potential improvement. Developed on the Qualtrics platform, the tool is available in six partner languages: Italian, English, Romanian, German, Spanish, and Catalan. The Zenodo record includes a technical document that provides: An introduction to the Self-Reflection Tool; Detailed descriptions of the Individual and Institutional tools, including screenshots and explanations of each section; Presentation of the generated reports; Preliminary results from testing sessions using the Individual tool, with initial analysis and reflections. This project deliverable is the result of joint collaboration between all project partners, who contributed through iterative feedback, user testing, and linguistic review. Final document authored by the UNIPD team (Work Package Leader). 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.

Zenodo (CERN European Organization for Nuclear Research)
University of Padua (IT), Universitat Oberta de Catalunya (ES), University of Florence (IT)
Openalex Percentile: Top 34%
E-Learning and Knowledge Management
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