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Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

Please note that all times are shown in the time zone of the conference. The current conference time is: 18th Apr 2026, 04:03:27pm CEST

Agenda Overview

Session

D312: AI INTEGRATION AND PRACTICE IN DESIGN

Time:

Wednesday, 20/May/2026:

8:45am - 9:45am

Session Chair: Peter R. N. Childs, Imperial College London, United Kingdom

Location: Conference Hall Bobara

Presentations

A structural framework for generative engineering and design assistance systems development

Timo Köring, Detlef Gerhard

Ruhr-Universität Bochum, Germany

Engineering software is evolving through the integration of artificial intelligence, creating new opportunities for enhanced assistance within product development. This paper proposes a use context model to systematically align and classify the functionalities of Generative Engineering and Design software with respect to the combination of product development phase, the nature of the task, and the level of support provided. Based on this model, a methodological guideline is proposed, offering a structured framework for the development and application of these tools in product development.

Human-AI co-creation: why, what, and how?

Runtao Ren¹, Jian Ma¹, Jianxi Luo²

¹Department of Information Systems, City University of Hong Kong, Hong Kong S.A.R. (China); ²Department of Systems Engineering, City University of Hong Kong, Hong Kong S.A.R. (China)

The rapid diffusion of generative AI is pushing creative work toward human–AI co-creation (HAIC). This paper designs a conceptual HAIC model that specifies several indispensable elements of effective co-creation: Human, AI, Artifact, Instruction, and Interaction. We demonstrated through a case study of a large-scale management information system development project how the HAIC model helps organizations implement HAIC. The proposed framework offers both an analytical lens for researchers and prescriptive guidance for practitioners seeking to engineer reliable human–AI collaboration.

Handling AI-generated knowledge artifacts in generative product engineering

Martin Becker¹, Damun Mollahassani¹, Simon Schleifer², Stefan Goetz², Sandro J. Wartzack², Jens C. Göbel¹

¹RPTU University Kaiserslautern-Landau, Germany; ²Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany

Development of complex interdisciplinary products increases engineering challenges, that AI supported engineering approaches attempt to reduce by increasing automation. The resulting AI generated engineering artifacts, however, need to be classified, verified and managed to enable traceability and auditability of engineering decisions. This paper presents a classification and management approach for these artifacts, allowing verification of AI generated engineering artifacts. A use case on the iterative development of an e-bike demonstrates the approach.

Reframing AI readiness: a multi-dimensional use case-centered AI readiness framework

Benedikt Müller, Daniel Roth, Matthias Kreimeyer

University of Stuttgart, Germany

A technology-oriented approach to AI predominates in research and practice, yet despite a high level of technological readiness, projects often fail due to poor domain-specific problem framing and data quality in early-stage AI system development. This contribution conducts an analysis of existing AI-related readiness models, to identify gaps in addressing these factors. The use case-centered AI readiness level framework is proposed on the basis of these findings – a unified, evidence-based model that links problem, data, and technology readiness across planning and implementation stages.