Stepping into the shoes of a policy maker: Students investigate the social and environmental impacts of GenAI and rethink guidelines.
- Group activity
- Analysis of AI impact reports
- In class or online
- GenAI guideline review based on argument matrix
- All disciplines (best suited for first-year students)
- Basic
- 90-120 min (without individual preparation)
- up to 35 students / small groups of 3-4
- Flexible classroom setting
- AI-related reports
- Argument matrix template
Short description
In groups of 3-4, students investigate up-to-date reports on the social and environmental impacts of GenAI. Using an argument matrix approach, they identify key benefits, risks and trade-offs, and draw evidence-based conclusions. Based on these conclusions, students review their university’s (or national) guidelines on GenAI usage and propose critically important points that could strengthen or inform future guidance for GenAI use in academic contexts generally or in this course specifically. The activity concludes with individual reflections on how students’ assumptions about GenAI evolved while constructing the evidence-based matrix and learning from other groups.
Competence domain of the Didactic Framework: Ethical Responsibility
By the end of this activity, students can…
- identify and critically evaluate social and environmental impacts of GenAI using evidence from up-to-date reports. (FLAIR Didactic Framework: LO15)
- design and propose informed contributions to GenAI usage guidelines based on a thorough understanding of the existing policies and relating them to empirical findings.
- critically reflect on how their own assumptions and reliance on GenAI evolved, using explicit criteria to justify their judgments.
Instructions
Build small groups of 3-4 students and assign each group a chapter from a report or a full report focusing on social and environmental impacts of AI (see References section for example reports). Ensure that chapters or documents are of roughly comparable length to support balanced workload across groups. Students prepare by reading their assigned report or report chapter, taking notes and extracting key arguments and evidence. It is recommended to allow the use of GenAI tools to support sense‑making of the reports (e.g. summarising, identifying key themes).
At the beginning of the seminar, ask groups to briefly share insights from their assigned readings as a warm‑up. Groups then collaboratively complete an argument matrix (see template in Resources section), identifying pros/benefits and cons/risks related to social or environmental impacts of GenAI as well as supporting evidence from sources and reports. Based on this analysis, groups formulate preliminary conclusions.
In the next phase, groups review their university’s (or national) AI guidelines and identify four to five report‑inspired points that could strengthen or extend these guidelines or inform course‑specific guidance. Groups present their proposed points in a plenary discussion, comparing similarities and differences across reports and receiving brief peer feedback. To support comparison, make the existing guidelines visible and use a shared collaborative document structured along the guideline headings to record and present proposed additions. Conclude this phase by summarising key patterns and reflecting on the diversity of social and environmental impacts of GenAI.
At the end of the seminar or as a homework assignment, ask students to complete a short individual reflection. Students reflect on how their assumptions about GenAI changed through constructing the argument matrix and engaging with other groups’ perspectives. Guiding questions may include shifts in personal views on AI, areas of uncertainty in the evidence, and the responsibilities of universities and students when using AI in academic contexts. Reflections can be briefly shared in class or submitted via the learning platform.
Assessment
Assessment for this activity should focus on students’ ability to analyse AI impact reports, construct evidence-based arguments, and connect findings to existing AI policies.
The central group artefact is the argument matrix, which can be used to assess the quality of reasoning, use of evidence, and the articulation of benefits, risks, and trade‑offs related to AI impacts.
Peer feedback can be embedded during the plenary discussion, where groups compare their proposed guideline additions, respond to similarities or differences across reports and receive feedback on the relevance, clarity, and policy‑value of their points. This peer exchange functions as formative assessment and supports refinement of arguments.
An individual written reflection allows instructors to assess students’ metacognitive learning, particularly shifts in AI assumptions, awareness of uncertainty, and understanding of ethical responsibilities.
Possible challenges
- Groups may work with reports of different length, scope, or relevance, or the number of groups may not align evenly with the number of available reports.
- Students may focus mainly on summarising report content instead of engaging in critical evaluation and synthesis.
- Plenary discussions may become repetitive or disconnected from concrete policy implications, especially in larger classes.
- Time management can be challenging when many groups present similar points.
How to adress them
- Assign reports or chapters of comparable length in advance and clarify expectations if multiple groups work on the same source, emphasizing that different perspectives are valuable.
- Stress the purpose of the argument matrix as an analytical tool and prompt students to justify claims with evidence rather than description.
- Actively connect group contributions to existing institutional or national AI guidelines to maintain a policy‑focused discussion.
- Set clear time limits for presentations, cluster overlapping points, and synthesize contributions during the instructor’s wrap‑up.
The activity can be conducted with or without the use of GenAI tools during the preparation phase. If GenAI is used, expectations for appropriate use should be agreed upon in advance.
The activity is easily adaptable to online teaching using shared documents and breakout rooms.
tba matrix
Abendroth Dias, K., Arias Cabarcos, P., Bacco, F.M., Bassani, E., Bertoletti, A. et al. (2025). Generative AI Outlook Report – Exploring the Intersection of Technology, Society and Policy, Navajas Cawood, E., Vespe, M., Kotsev, A. and Van Bavel, R. (editors), Publications Office of the European Union, Luxembourg. https://data.europa.eu/doi/10.2760/1109679, JRC142598.
OECD (2022). Measuring the environmental impacts of artificial intelligence compute and applications: the AI footprint. OECD Digital Economy Papers, No. 341, 1-56. https://www.oecd.org/en/publications/measuring-the-environmental-impacts-of-artificial-intelligence-compute-and-applications_7babf571-en.html
OECD (2023). OECD Skills Outlook 2023: Skills for a Resilient Green and Digital Transition, OECD Publishing, Paris, https://doi.org/10.1787/27452f29-en
Smith, H. & Adams, C. (2024). Thinking about using AI? Here’s what you can and (probably) can’t change about its environmental impact. Green Web Foundation. https://www.thegreenwebfoundation.org/publications/report-ai-environmental-impact/
Using this resource
This resource is licensed under Creative Commons BY-SA 4.0 license. Suggested citation: Flair Collaboration. (2025). FLAIR Toolkit. Teaching GenAI Competencies.
