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Despite the potential of generative AI (GenAI) design tools to enhance design processes, professionals often struggle to integrate AI into their workflows. Fundamental cognitive challenges include the need to specify all design criteria as distinct parameters upfront (intent formulation) and designers' reduced cognitive involvement in the design process due to cognitive offloading, which can lead to insufficient problem exploration, underspecification, and limited ability to evaluate outcomes. Motivated by these challenges, we envision novel metacognitive support agents that assist designers in working more reflectively with GenAI. To explore this vision, we conducted exploratory prototyping through a Wizard of Oz elicitation study with 20 mechanical designers probing multiple metacognitive support strategies. We found that agent-supported users created more feasible designs than non-supported users, with differing impacts between support strategies. Based on these findings, we discuss opportunities and tradeoffs of metacognitive support agents and considerations for future AI-based design tools. 

Abstract How can the NGO–NGO interactions of Global South organizations be better understood and improved? Global South nongovernmental organizations (NGOs) have been frequently missing from the overall advocacy network. When they are included, global South organizations are less likely to take on brokerage roles that may help them build connections and resources across communities. Instead, if they are included, Global South NGOs often are relegated to less powerful, intra-community brokerage roles. Drawing on Deloffre and Quack’s framework (Chapter 1, this volume), Chapter 6 examines the top Global South NGOs that have been able to overcome exclusionary structures and forge inter-community brokerage connections to other NGOs. A deeper look at these organizations and the structures where they are embedded can help to gain insights into the transformative nature of NGO–NGO interactions. The chapter finds that certain country, community, and organizational factors help some Global South NGOs develop connections outside of their immediate community. A focus on these factors may help innovation and protect against a civil society backlash. 

The way media portray public health problems influences the public’s perception of problems and related solutions. Social media allows users to engage with news and to collectively construct meaning. This paper examined news in comparison to user-generated content related to opioids to understand the role of second-level agenda-setting in public health. We analyzed 162,760 tweets about the opioid crisis, and compared the main topics and their sentiments with 2998 opioid stories from The New York Times online. Evidence from this study suggests that second-level agenda setting on social media is different from the news; public communication about opioids on X/Twitter highlights attributes that are different from those highlighted in the news. The findings suggest that public health communication should strategically utilize social media data, including obtaining consumer insight from personal tweets, listening to diverse views and warning signs from issue tweets, and tuning in to the media for policy trends. 

Loan behavior modeling is crucial in financial engineering. In particular, predicting loan prepayment based on large-scale historical time series data of massive customers is challenging. Existing approaches, such as logistic regression or nonparametric regression, could only model the direct relationship between the features and the prepayments. Motivated by extracting the hidden states of loan behavior, we propose the smoothing spline state space (QuadS) model based on a hidden Markov model with varying transition and emission matrices modeled by smoothing splines. In contrast to existing methods, our method benefits from capturing the loans’ unobserved state transitions, which not only increases prediction performances but also provides more interpretability. The overall model is learned by EM algorithm iterations, and within each iteration, smoothing splines are fitted with penalized least squares. Simulation studies demonstrate the effectiveness of the proposed method. Furthermore, a real-world case study using loan data from the Federal National Mortgage Association illustrates the practical applicability of our model. The QuadS model not only provides reliable predictions but also uncovers meaningful, hidden behavior patterns that can offer valuable insights for the financial industry. 

Conceptual design is the foundational stage of a design process that translates ill-defined design problems into low-fidelity design concepts and prototypes through design search, creation, and integration. In this stage, product shape design is one of the most paramount aspects. When applying deep learning-based methods to product shape design, two major challenges exist: (1) design data exhibit in multiple modalities and (2) an increasing demand for creativity. With recent advances in deep learning of cross-modal tasks (DLCMTs), which can transfer one design modality to another, we see opportunities to develop artificial intelligence (AI) to assist the design of product shapes in a new paradigm. In this paper, we conduct a systematic review of the retrieval, generation, and manipulation methods for DLCMT that involve three cross-modal types: text-to-3D shape, text-to-sketch, and sketch-to-3D shape. The review identifies 50 articles from a pool of 1341 papers in the fields of computer graphics, computer vision, and engineering design. We review (1) state-of-the-art DLCMT methods that can be applied to product shape design and (2) identify the key challenges, such as lack of consideration of engineering performance in the early design phase that need to be addressed when applying DLCMT methods. In the end, we discuss the potential solutions to these challenges and propose a list of research questions that point to future directions of data-driven conceptual design.