From prompt to prototype: Students tackle an innovation challenge through AI-supported ideation process, problem analysis and the communication of solutions.
- Group activity
- Innovation challenge
- In class
- AI-assisted problem exploration, idea generation and prototyping
- All disciplines
- Advanced
- 120-155 min / 2-3 sessions
- 15-30 students
- GenAI tools
- Flexible classroom setting
Short description
Students engage in an innovation challenge (e.g., “Improve campus sustainability,” “Reimagine student wellbeing services,” “Innovate a learning support system”) in which they strategically use AI tools to support ideation, problem analysis, prototyping, and solution communication. They must deliberately plan, track, and evaluate how they use different AI tools – and adjust their approaches based on reflections and instructor guidance.
Competence Domain of the Didactic Framework: Creative and Practical Application
At the end of this activity, students can…
- select and justify the use of appropriate AI tools for different stages of an innovation process, considering workflows, prompts, and decision criteria. (FLAIR Didactic Framework: LO17)
- use GenAI to generate, expand and refine ideas. (FLAIR Didactic Framework: LO19)
- monitor and reflect on AI interactions, including limitations and unexpected outcomes.
- evaluate the impact of AI on their creative and problem-solving workflow.
- adjust AI-use strategies by refining prompts, tools, or workflows based on reflection.
Instructions
Briefly introduce the innovation challenge. Students begin by individually developing an AI Use Plan that makes their intended AI use explicit. They decide which AI tools they intend to use (e.g., ChatGPT, image generators, coding assistants, data analysis tools) for which tasks in the design process (e.g., ideation, background research, prototyping, drafting) based on which criteria. In addition, students anticipate potential risks or limitations (e.g. bias, hallucinations, over‑reliance) and define how they will verify AI outputs. The plan also includes how they will monitor whether AI actually supports their innovation work.
Students build teams of 3-4 and start working on the innovation challenge provided by the teacher. AI tools are strategically integrated at 2-3 stages of the innovation process:
- Problem exploration: Students use an LLM to clarify and refine the core problem
- Idea generation: Students use generative AI to brainstorm options, generate sketches, or create conceptual diagrams
- Prototype creation: Students use coding assistants, mockup tools, image generators, or AI-based data analysis as appropriate.
Throughout the challenge, teams document their AI use in an AI Activity Log, recording which tools and prompts they used, why they chose them, how AI outputs influenced their work, and which limitations they encountered.
Teams pause to review their AI Activity Logs and critically reflect on their current approach. Based on this review, they revise key aspects of their AI strategy, including which AI tools they rely on, how they formulate prompts, which methods they use to verify AI outputs and how tasks are distributed between human and AI contributions. This checkpoint helps teams make informed adjustments before continuing with the design challenge.
To conclude the activity, students complete an individual reflective task. They evaluate the effectiveness of their initial AI plan, identify productive, ineffective, or risky uses of AI, and describe how and why they adjusted their strategy during the challenge. The reflection also invites students to articulate personal insights and emerging best practices related to AI‑supported innovation.
Assessment
Team‑based assessment may draw on the documented challenge solution, the AI interaction log, and a short team presentation, which together make the innovation process and the role of AI visible.
Recommended individual deliverables are the personal AI use plan and, in particular, the individual reflection on AI use providing insight into how students evaluate the effectiveness of AI, monitor their own practices, and adjust strategies over time.
It is recommended to focus the assessment on the reflection assignment, as it supports the development of responsible and effective AI use beyond this activity. Possible evaluation criteria include depth of self-evaluation, monitoring AI use, ability to adjust learning strategies, understanding of AI tool capabilities and limitations, connection to the initial AI use plan as well as clarity, structure, and professional communication.
Possible challenges
- The time needed for the design challenge may vary depending on its complexity.
- Students may have very different levels of prior experience with AI.
- Some students may have ethical concerns about using AI tools.
How to address them
- If needed, spread the activity across multiple sessions or shift parts to preparation or follow‑up work.
- Check students’ prior AI experience in advance and include short introductions or form mixed‑experience groups based on self‑assessment.
- Take ethical concerns seriously and, where possible, offer alternative tools or approaches that address data protection or other relevant issues.
The activity can be flexibly adapted in its organisation and timing. The AI Strategy Planning phase may be assigned as pre-class preparatory work and the AI Use Evaluation and Reflection as post-class follow‑up work. Instead of providing a predefined challenge, student teams can also be asked to develop their own mini design challenge in advance. If peer review elements shall be incorporated, AI Activity Logs can be shared between teams during the process. In this case, providing a simple Log template is recommended to ensure clarity, comparability, and readability across teams.
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.
