Students engage in structured debates on ethical risks of AI decision-making. Based on the results, they develop evidence-based ethical guidelines.
- Group activity
- Structured debate
- In class
- Examining ethical implications of AI-based decision-making
- All disciplines
- Intermediate
- 90-100 min / 2 lessons
- approx. 25 students (groups of 3-5)
- GenAI tool
- Case scenario
- Flexible classroom setting
Short description
In this group-based debate activity, students critically examine the ethical risks of using AI systems for decision-making processes. Working in teams as Supporters, Opponents and Moderators, students analyze issues of fairness, bias, privacy, transparency and accountability through a structured debate. The session concludes with a collaborative drafting of concise ethical guidelines, each linked to a specific risk, evidence from the debate and an operational criterion.
Competence domain of the Didactic Framework: Ethical Responsibility
By the end of this activity, students can…
- identify key ethical risks in AI-supported-decision-making (bias, privacy, fairness, opacity, accountability) and explain how data practices and algorithmic design may contribute to them.
- analyze and debate opposing perspectives on AI-supported decision-making in societal or sensitive contexts using evidence-based arguments and counterarguments.
- identify ways to mitigate ethical risks of GenAI use by developing ethical guidelines linked to specific risks, supporting evidence and measurable criteria. (FLAIR Didactic Framework: LO11)
- reflect on the impact of AI-assisted decision-making on public trust, stakeholder rights, and communication, and articulate the role of transparency and ethical judgment.
Instructions
Present a case in which an AI tool analyzes public data (e.g., social media activity to evaluate digital professionalism) for decision-making processes. For Sample Scenarios see Further Resources section.
Students are organized into role-based preparation groups of 3–5 students, with each group assigned one role only: Supporters (argue in favor of AI use), Opponents (highlight risks and concerns) or Moderators (structure the debate). All students assigned to the same role work together during the preparation phase to develop arguments (Supporters/Opponents) or guiding questions and synthesis criteria (Moderators).
Each team prepares arguments using examples, evidence and anticipated counterarguments. Moderators prepare a brief introduction to frame the debate, outline communication rules (e.g., turn-taking, time limits, respectful discourse) and plan a short concluding synthesis to structure the debate effectively. To support preparation, students may choose to use GenAI tools for generating ideas, exploring perspectives or checking information accuracy.
Supporters and Opponents engage in timed rounds of claims, rebuttals and closing statements, while Moderators facilitate and take notes. The structured debate takes place in a whole-class setting. From each role-based preparation group, only a small number of representatives (3–4 Supporters, 3–4 Opponents, and 1–2 Moderators) participate in the live debate to ensure balanced participation and time efficiency. Other group members may rotate into the debate or contribute additional arguments or questions.
Moderators summarize key points, highlighting risks, safeguards, and unresolved issues. The synthesis is facilitated by the Moderators in a whole-class setting, with active input from all students. Moderators use structured guidelines provided during the preparation phase to summarize key risks, safeguards and open questions. These points are documented in a shared visual space for use in the next activity.
As a whole class, students co-create a concise set of ethical guidelines, linking each to a risk, evidence from the debate and an operational criterion. This step emphasizes translating ethical principles into actionable and assessable practices. Depending on class size and available time, instructors may choose to adapt this step using alternative facilitation formats (e.g. small-group drafting followed by plenary discussion).
Briefly discuss what the debate revealed about responsible AI use and decision-making. Reflect on how issues such as transparency, fairness, and accountability may influence public trust, stakeholder rights, and communication in AI-supported contexts.
Assessment
This assessment evaluates students’ ethical reasoning through participation in a structured debate and the co-creation of ethical guidelines linked to specific risks, supporting evidence and an operational criterion. Assessment may also include peer feedback, observation and group reflective writing.
Evaluation focuses on the accurate identification of ethical risks, the use of relevant examples or sources to support arguments, the quality of ethical reasoning and proposed mitigation strategies and the clarity with which debate insights are synthesized into well-justified guidelines. Moderator performance is assessed based on facilitation, synthesis quality, and ability to link arguments to ethical risks.
A detailed example weighting can be found under the Further Resources section.
Possible challenges
- Discussions may become overly opinion-based without sufficient evidence or ethical reasoning
- Students may focus on “winning” the debate rather than critically engaging with different perspectives
- Uneven participation may occur within debate roles or group preparation
How to adress them
- Encourage students to support arguments with examples, evidence or external sources
- Emphasize respectful discussion, critical reflection and openness to multiple perspectives
- Assign clear responsibilities within groups and actively moderate participation during the debate
Recommended weighting example (Total 100%)
- Debate preparation (group notes) — 25% (Lecturer)
- Debate performance (role-specific, rubric-based) — 20% (Lecturer)
- Co-drafted ethical guidelines (group product) — 25% (Lecturer)
- Individual reflection (short essay, 300–500 words) — 20% (Lecturer)
- Peer evaluation (individual) — 10% (Lecturer)
Sample Scenarios
- Communication / Public Relations
A university’s Communication Faculty plans to use an AI tool that analyses applicants’ public social media activity to evaluate “digital professionalism” and “communication competence.” The AI system scores candidates based on factors such as tone, consistency, writing clarity, and online behavior. Admissions staff want to use these scores as an additional evaluation criterion.
However, concerns arise regarding algorithmic bias (e.g., penalising informal language), privacy violations, contextual misinterpretation of posts, and fairness for students with limited online presence.
- Sociology
A municipality adopts an AI system that analyses public Twitter and Instagram data to identify “at-risk neighbourhoods” for targeted social programmes. The algorithm clusters communities based on keywords, sentiment, and frequency of posts about unemployment, discrimination, or insecurity.
Sociologists warn that the model might reinforce stereotypes, misinterpret sarcasm or activism as “risk,” ignore offline realities, or disproportionately label marginalised communities based on digital visibility rather than structural factors.
- Psychology
An organisation promoting mental health pilots an AI tool that screens potential candidates for a counselling training programme. The AI analyses applicants’ past public social media posts to detect indicators of empathy, emotional regulation, and communication style.
While administrators believe this will improve candidate selection, psychologists raise concerns that the AI may misjudge emotional expressions, oversimplify complex behaviours, misinterpret humor or cultural communication patterns, and inadvertently discriminate against neurodivergent applicants whose online expression differs from typical patterns.
Yousufi, M. (n.d.). Debating the ethics of generative AI. AI Pedagogy Project. metaLAB (at) Harvard. https://aipedagogy.org/assignment/debating-the-ethics-of-generative-ai/ Licensed under Creative Commons BY-NC-SA 4.0. Accessed 28.11.2025.
Day of AI. (2025). AI Ethics Debate. Day of AI. https://dayofai.org/units/ai-ethics-debate
Debate Project. (2024). Chapter 7 — Artificial intelligence on classroom debates. DEBATE Project. https://debateproject.eu/chapter-7-artificial-intelligence-on-classroom-debates/
Using this resource
This resource is licensed under Creative Commons BY-NC-SA 4.0 license. Suggested citation: Flair Collaboration. (2025). FLAIR Toolkit. Teaching GenAI Competencies.
