Each year, the consortium will conduct a joint new product development (NPD) course hosted by a participating HEI, collaborating with an industrial partner to provide real-world assignments for mixed student teams. These courses will serve as a foundation for testing and integrating GenAI tools into the NPD process. The project will pursue three main objectives, each yielding two concrete project results.
Year 1: Identify Tools or „What to Use“?
The first objective is to identify and test available GenAI tools suitable for tasks in the product development process, such as problem analysis, idea generation, development, and validation.
Project Result 1: A comprehensive review and hands-on evaluation of GenAI tools, involving a desk study and practical testing.
This will result in a structured knowledge base, including a curated list of tools, instructions, advantages, limitations, risks, and relevant sources. Output coming soon!
Project Result 2: Mapping GenAI tools to key NPD processes.
This will demonstrate best practices for applying GenAI in product development, resulting in a visual mapping of tools to NPD phases. The second objective is to establish a methodology for effectively integrating GenAI tools into NPD. This will include raising awareness of AI’s limitations and formulating best practices. Educators and students will receive guidance on the purpose and scope of GenAI in educational settings and how to refine AI inputs for high-quality outputs. Output coming soon!
Year 2: Process or „How to Use“?
The second objective is to establish a methodology for effectively integrating GenAI tools into NPD. This will include raising awareness of AI’s limitations and formulating best practices. Educators and students will receive guidance on the purpose and scope of GenAI in educational settings and how to refine AI inputs for high-quality outputs.
Project Result 3: A methodology for integrating GenAI into NPD processes, which will redefine tasks traditionally performed by humans by having GenAI generate solutions.
This methodology will be documented with examples for course integration. Output expected 2027
Project Result 4: Input refinement strategies, which are essential for generating satisfactory results using GenAI, especially in creative product development environments.
This will result in a set of guidelines for refining inputs across tools used in the process. Output expected 2027
Year 3: Responsibility or „How to Use Responsibly“?
The third objective is to derive guidelines for the ethical use of GenAI tools, ensuring human judgment remains central in decision-making. Engineers must critically evaluate AI outcomes against industry standards to avoid errors or biases.
Project Result 5: A responsibility framework for the ethical use of GenAI in NPD, based on desk research into existing AI
ethics guidelines, tailored for NPD.
This will ensure GenAI use aligns with transparency, accountability, and human
oversight.
Project Result 6: Generalized educational materials to be applicable across various engineering disciplines.
By generalizing content to focus on core tasks like problem analysis, idea generation, and development, the materials will support a wider academic audience, allowing for broader adaptation of GenAI methodologies across different fields.
