Teaching with AI: Ethics, Integrity and Critical Thinking

AI can increase confidence without increasing competence.
Students may feel more certain because the answer looks polished, even when it is wrong. This is a problem for learning, because confidence normally improves through practice, feedback, and correction. If AI removes that loop, students can stop checking and stop improving.

So the goal is not “ban AI” or “let AI do everything”. The goal is to teach students how to use AI in a way that still protects learning.

AI is already part of everyday school life. Students use it to write, summarise, translate, and solve problems, often faster than teachers can react. The real challenge is not only whether students use AI, but how they use it, and what this does to learning.

This course helps teachers understand what generative AI is (and what it is not), how it produces answers, and why it can sound convincing even when it is wrong. We focus on ethical and responsible use, data privacy, and academic integrity: what counts as legitimate support, what becomes plagiarism, and how to communicate clear expectations to students and parents.

A key course theme is the growing risk that AI separates confidence from real competence. Research discussed in the course describes a pattern where students encounter a task, ask AI, accept the output, and move on — with very little thinking in between. We explore practical classroom “interruption points” that slow this process down and bring back reasoning, checking, and accountability.

Participants leave with a complete classroom-ready toolkit: AI-use levels, assignment templates, student disclosure forms, rubrics, and case studies that can be used in staff training.

Check out this course on European School Education Platform a meeting point for the school education community:
https://school-education.ec.europa.eu/en/learn/courses/teaching-ai-ethics-integrity-and-critical-thinking

Learning outcomes

By the end of this course, participants will be able to:

• Explain how generative AI works in clear, teacher-friendly terms
• Identify common learning risks: over-trust, reduced effort, shallow reasoning, and “copy–paste thinking”
• Set clear boundaries between acceptable AI support and academic misconduct, with examples students understand
• Implement a practical AI Use Levels framework (from “no AI” to “AI allowed with disclosure and verification”)
• Design tasks that require thinking traces (evidence of reasoning), not only final answers
• Apply five classroom strategies to interrupt “AI auto-acceptance” and rebuild critical thinking
• Produce a classroom or school AI policy pack aligned with ethical principles (privacy, fairness, inclusion, transparency)

Tentative schedule

Day 1 – Introduction to the course and the importance of teaching ethics, intergity and critical thinking in the AI era

• Welcome event; presenting participants, their schools, cities, countries
• Presenting host organization and the city
• Getting to know each other: Icebreakers
• Introduction to the course and the schedule

Day 2 – AI basics for educators: what it can and cannot do

• How generative AI produces answers
• Why “sounds good” does not mean “is correct”
• Classroom reality: where students already use AI
• Workshop: map risks/opportunities by subject

Day 3 – Ethics, privacy, fairness and responsible use

• What should never be shared with AI tools (student data, sensitive info)
• Bias and fairness: who benefits, who is disadvantaged
• What “responsible use” looks like in everyday classroom routines

Day 4 – Academic integrity: plagiarism, authorship and transparency

• Clear definitions with examples: support vs. misconduct
• Why AI detectors are not a real solution (and can harm trust)
• How to teach disclosure and honesty without creating fear

Day 5 – Teaching for critical thinking: interrupt auto-acceptance

• The confidence/competence gap: why students may stop checking
• The 5 interruption points: pre-commitment → accountability

Day 6 – Better assessment and a full classroom policy pack

• Designing tasks that require thinking traces and verification
• Fast oral accountability methods (quick checks that scale)
• Parent communication and school alignment

Day 7 – Course closure and evaluation

• Building a Classroom AI Policy Pack
• Reflecting on the course and what we have learned from the past week
• Course evaluation
• Erasmus+ Certificate award ceremony