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1. The educative design problem framework

   Svihla, V.; Wilson-Fetrow, M.; Chi, E.; Chen, Y.; Datye, A.K.; Gomez, J.R.; Olewnik, A. (January 2021, Proceedings of the ASEE 128th Annual Conference and Exhibition)

   Research suggests expert designers frame problems more broadly than novices, but authentic context may make a design problem too difficult. Yet decontextualized problems provide little opportunity for students to learn how to direct their framing and solving of problems. This paper considers characteristics of design problems that support students to develop design skills as they learn and apply concepts to the framing and solving of design problems. We selected and analyzed (un)successful design problems used over four years of iterations in an undergraduate chemical engineering program. We analyze salient features that made the design problems particularly educative and generalize an Educative Design Problem Framework, finding that such problems are relevant to students, have sociotechnical complexity, and are accessible yet require accurate application of technical content to solutions that are not deterministic—in other words, they are low-bar entry and high ceiling. Faculty can use this framework to evaluate and improve design problems in their teaching. 

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2. An additive framework for kirigami design

   https://doi.org/10.1038/s43588-023-00448-9  

   Dudte, Levi H.; Choi, Gary P.; Becker, Kaitlyn P.; Mahadevan, L. (May 2023, Nature Computational Science)
3. Design Decision Framework for AI Explanations

   Oghenemaro Anuyah and Ronald Metoyer (October 2021, Mensh and Computer Workshop on Human-Centered Artificial Intelligence, 2021)

   Explanations can help users of Artificial Intelligent (AI) systems gain a better understanding of the reasoning behind the model’s decision, facilitate their trust in AI, and assist them in making informed decisions. Due to its numerous benefits in improving how users interact and collaborate with AI, this has stirred the AI/ML community towards developing understandable or interpretable models to a larger degree, while design researchers continue to study and research ways to present explanations of these models’ decisions in a coherent form. However, there is still the lack of intentional design effort from the HCI community around these explanation system designs. In this paper, we contribute a framework to support the design and validation of explainable AI systems; one that requires carefully thinking through design decisions at several important decision points. This framework captures key aspects of explanations ranging from target users, to the data, to the AI models in use. We also discuss how we applied our framework to design an explanation interface for trace link prediction of software artifacts. 

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4. Reflectarray Design Using a Discrete Dipole Framework

   https://doi.org/10.1109/OJAP.2023.3245688  

   Diebold, Aaron V.; Pande, Divya; Gregg, Christine; Smith, David R. (January 2023, IEEE Open Journal of Antennas and Propagation)
5. Design-based Research in GIS-infused Disciplinary Courses: Toward a Design Framework

   https://doi.org/10.22318/icls2020.1111  

   James, K.; McGee, S.; Uttal, D.; Kolvoord, B. (January 2020, The Interdisciplinarity of the Learning Sciences, 14th International Conference of the Learning Sciences (ICLS) 2020)

   Gresalfi, M.; Horn, I. S. (Ed.)

   Geographic information systems (GIS) is valuable as a teaching and learning tool and will play a key role in the careers of current K-12 students (NRC, 2006). However, little work has been done to understand effective approaches to integrating GIS into content instruction. In this paper, we discuss the adaptation of the Learning for Use model, a framework for the design of technology-supported, content-driven inquiry tasks (Edelson, 2001), for the context of GIS-infused content courses. Using a design-based research approach, we developed a set of design principles that reflect key elements of effective GIS-driven content instruction, which guided the adaptation of the design framework. The goal of this work is to develop a set of supports to scaffold the co-design and implementation of GIS-infused content courses that will inform a general design model of infusing GIS into content courses. 

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