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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. A text-guided protein design framework

   https://doi.org/10.1038/s42256-025-01011-z  

   Liu, Shengchao; Li, Yanjing; Li, Zhuoxinran; Gitter, Anthony; Zhu, Yutao; Lu, Jiarui; Xu, Zhao; Nie, Weili; Ramanathan, Arvind; Xiao, Chaowei; et al (March 2025, Nature Machine Intelligence)

   Current AI-assisted protein design utilizes mainly protein sequential and structural information. Meanwhile, there exists tremendous knowledge curated by humans in text format describing proteins’ high-level functionalities, yet whether the incorporation of such text data can help in protein design tasks has not been explored. To bridge this gap, we propose ProteinDT, a multimodal framework that leverages textual descriptions for protein design. ProteinDT consists of three consecutive steps: ProteinCLAP, which aligns the representation of two modalities, a facilitator that generates the protein representation from the text modality and a decoder that creates the protein sequences from the representation. To train ProteinDT, we construct a large dataset, SwissProtCLAP, with 441,000 text and protein pairs. We quantitatively verify the effectiveness of ProteinDT on three challenging tasks: (1) over 90% accuracy for text-guided protein generation; (2) best hit ratio on 12 zero-shot text-guided protein editing tasks; (3) superior performance on four out of six protein property prediction benchmarks. 

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5. GOLDBAR: A Framework for Combinatorial Biological Design

   https://doi.org/10.1021/acssynbio.4c00296  

   Roehner, Nicholas; Roberts, James; Lapets, Andrei; Gould, Dany; Akavoor, Vidya; Qin, Lucy; Gordon, D Benjamin; Voigt, Christopher; Densmore, Douglas (September 2024, ACS Synthetic Biology)