Enhancing Collaborative Design Through Process Feedback with Motivational Interviewing: Can AI Play a Role?

Abstract

Collaborative design is a key element in Product/System development. However, delivering true collaboration in multidisciplinary teams is challenging. Feedback systems are one of the solutions to improve collaboration; although teams normally receive feedback on outcomes, the collaboration process itself is neglected. During a PBL course, 40 engineers from 22 disciplines and 12 countries were distributed in six teams. In addition to receiving outcome feedback, we used Motivational Interviewing (MI) techniques to provide process feedback for half of the design teams whereas the other half only received outcome feedback. At the same time, we employed a pre-trained Machine Learning (ML) technique to compare the teams’ progress through teams’ communication and sentiment analysis. Our results show that; (i) adding process feedback in the early stages of the design process enhances the collaborative design. (ii) ML algorithms can predict the progress. We suggest further research using Natural Language Processing (NLP) and supervised ML techniques for designing a new AI team-mate and mentoring assistant, as well as fostering Human-AI interaction styles via MI methods. © IFIP International Federation for Information Processing 2024.

Keywords

Author Keywords

Machine Learning PBL Feedback Motivational interviewing Collaborative Design

Index Keywords

Product design Machine learning Machine-learning Sentiment analysis Key elements Machine learning techniques System development Collaborative design Feedback systems Motivational interviewing Multidisciplinary teams PBL Product systems

Author Affiliations

Skolkovo Institute of Science and Technology, Moscow, Russian Federation

Funding & Acknowledgements

No funding information

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Enhancing Collaborative Design Through Process Feedback with Motivational Interviewing: Can AI Play a Role?

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