Practitioner Research Insights: Applications of Science and Technology to Real-World Innovations
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Item Leveraging Artificial Intelligence and Collaborative Strategies for Innovative Product Development(2025-01-07) Ozkan-Seely, Gulru; Bezmez, DemetThe integration of artificial intelligence (AI) in new product development (NPD) is transforming technologically advanced industries. This paper examines the impact of AI in design and development, highlighting its advantages and potential drawbacks. We identify key challenges and propose strategic solutions to overcome them, aiming to develop a vision for effective AI incorporation in NPD. Our findings emphasize the need for a balanced approach to harness AI's capabilities while addressing its challenges to foster innovation and efficiency in NPD processes.Item Enhancing Teamwork in News Media: A Design Science Approach to Human-AI Collaboration for Story Planning(2025-01-07) Bonazzi, Riccardo; Manafy, Michelle; Gautschi, Heidi; Viscusi, GianluigiThis ongoing study addresses the increasing need for human-AI collaboration in newsrooms by conversational system to enhance teamwork. Our prototype leverages retrieval-augmented generation to provide features for content analysis and social networking. Expert evaluation validated the value of the content analysis feature, while highlighting areas for improvement in expert identification. The preliminary results offer practical insights and design guidelines for news media organizations.Item Utilizing Fair and Explainable Machine Learning to Analyze High School Graduation Likelihood(2025-01-07) Kelberlau, Darin; Agarwal, Sonia; Grimaldo, Jorge; Hall, Margeret; Haas, ChristianThe traditional focus of data-driven decision-making has been business applications. Other domains, like education, also have significant potential. For instance, various factors impact the likelihood of successfully graduating; advance identification of students "at risk" of not graduating allows administrators to intervene, increasing graduation likelihood. This Research-Practice Partnership applies Machine Learning (ML), including aspects of Fairness and Explainability, to identify High School students at risk of not graduating. We show that ML approaches can successfully predict such students, while Explainable ML techniques can shed light on the factors that contribute most to a reduced likelihood of graduation. With this information, school counselors can efficiently identify roadblocks and also follow up with "grey zone" students, i.e., students at risk of not graduating but who do not follow typical non-graduation patterns (and might not be on the radar of counselors).Item Introduction to the Minitrack on Practitioner Research Insights: Applications of Science and Technology to Real-World Innovations(2025-01-07) Reineke, Nicole; Griffith, Terri; Qian, Maggie; Keskin, Tayfun