Self-reflection tool

The Self-Reflection Tool is designed to guide you through a simple and meaningful process of personal and institutional reflection on the ethical use of AI in education.
Here’s how to make the most of it — step by step:

• Take the Survey: Start by completing the questionnaire. Reflect on your own beliefs, practices, and attitudes (or, for the institutional version, on your organization’s policies and strategies). The questions are based on the key ethical dimensions identified in the European guidelines.
• Explore Your Results: Once you’ve submitted your responses, you’ll receive a summary showing your profile across the main ethical areas. This helps you understand your current position and identify areas for reflection or improvement.
• Compare with Peers: View the aggregated data from other users or institutions to see broader trends and levels of engagement.

➡️ From Using to Understanding

After you’ve completed the survey and explored your results, the next step is learning how to interpret them effectively.
In the following section, “How to Read Reports and Graphs,” you’ll find guidance on understanding the visuals, values and indicators used in your report — so you can move beyond the numbers to meaningful insight.

The bar chart summarizes the average self-assessed levels of awareness and competence reported by respondents for each dimension of the EU Guidelines on the Ethical Use of AI in Education.

• Horizontal axis (X): represents the different guideline dimensions.
• Vertical axis (Y): shows the mean perceived levels. Higher values indicate greater perceived understanding, control, or competence, while lower values suggest areas where respondents feel less confident.

Overall, the chart provides a clear, at-a-glance overview of how respondents evaluate their performance across dimensions, highlighting both strengths and areas for improvement. 

☁️ Word Clouds (Profiles and Perceptions)

Word clouds display the distribution of responses across different categories. The larger the word, the higher the frequency of that response.

Examples included in the report:

• Countries of participants → show geographical diversity.
• Personal reflections on AI ethics → shows key concepts where respondents perceive a greater need for support or intervention.

👉 Word clouds are not about precision but about emphasis. They provide a quick visual impression of what matters most across the group.

🍩 Donut Charts (Profiles of Respondents)

Donut charts summarize participants’ background characteristics. Each slice represents the percentage and number of respondents in a given category.

• Research field → disciplinary distribution of participants.
• Educational level → teaching levels represented (e.g., primary, secondary, higher education).
• Professional experience in teaching → years or levels of teaching experience.

👉 These charts are useful to understand who the respondents are and to contextualize the other results.

• Compare your answers with the averages:  See whether your perception is aligned with, above, or below the group’s.
• Look beyond the averages: Pay attention to the distribution of scores. If responses cluster at higher values (4–5), it signals strong confidence or awareness; if they cluster at lower values (0–1), it points to areas needing improvement.
• Identify strengths and growth areas: Lower-than-average ratings may signal opportunities for reflection and development.
• Appreciate diversity: Differences in responses are not “wrong”; they offer valuable perspectives that enrich the overall understanding.

Use the results as a mirror: NOT to judge your absolute position, but to recognize where you are already strong and where you could grow in relation to the ethical use of AI. Comparing your results with the averages helps you see whether your perceptions align with or differ from those of your colleagues.