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1. Sketching AI Concepts with Capabilities and Examples: AI Innovation in the Intensive Care Unit

   https://doi.org/10.1145/3613904.3641896  

   Yildirim, Nur; Zlotnikov, Susanna; Sayar, Deniz; Kahn, Jeremy M; Bukowski, Leigh A; Amin, Sher Shah; Riman, Kathryn A; Davis, Billie S; Minturn, John S; King, Andrew J; et al (May 2024, ACM)

   Mueller, Florian Floyd; Kyburz, Penny; Williamson, Julie R; Sas, Corina; Wilson, Max L; Dugas, Phoebe Toups; Shklovski, Irina (Ed.)

   Advances in artificial intelligence (AI) have enabled unprecedented capabilities, yet innovation teams struggle when envisioning AI concepts. Data science teams think of innovations users do not want, while domain experts think of innovations that cannot be built. A lack of effective ideation seems to be a breakdown point. How might multidisciplinary teams identify buildable and desirable use cases? This paper presents a first hand account of ideating AI concepts to improve critical care medicine. As a team of data scientists, clinicians, and HCI researchers, we conducted a series of design workshops to explore more effective approaches to AI concept ideation and problem formulation. We detail our process, the challenges we encountered, and practices and artifacts that proved effective. We discuss the research implications for improved collaboration and stakeholder engagement, and discuss the role HCI might play in reducing the high failure rate experienced in AI innovation. 

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2. "All Rise for the AI Director": Eliciting Possible Futures of Voice Technology through Story Completion

   https://doi.org/10.1145/3357236.3395479  

   Cambre, Julia; Reig, Samantha; Kravitz, Queenie; Kulkarni, Chinmay (July 2020, DIS '20: Proceedings of the 2020 ACM Designing Interactive Systems Conference)

   null (Ed.)

   How might the capabilities of voice assistants several decades in the future shape human society? To anticipate the space of possible futures for voice assistants, we asked 149 participants to each complete a story based on a brief story stem set in the year 2050 in one of five different contexts: the home, doctor's office, school, workplace, and public transit. Story completion as a method elicits participants' visions of possible futures, unconstrained by their understanding of current technological capabilities, but still reflective of current sociocultural values. Through a thematic analysis, we find these stories reveal the extremes of the capabilities and concerns of today's voice assistants---and artificial intelligence---such as improving efficiency and offering instantaneous support, but also replacing human jobs, eroding human agency, and causing harm through malfunction. We conclude by discussing how these speculative visions might inform and inspire the design of voice assistants and other artificial intelligence. 

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3. BOARD # 286: NSF REU: Multidisciplinary Collaborative Undergraduate Research Experience: Impacts on Engineering and Technology

   https://doi.org/10.18260/1-2--55650  

   Sahoo, Avimanyu; Park, Haejun (June 2025, ASEE Conferences)

   Early involvement in engineering research has proven to be a highly effective way to inspire undergraduate students to pursue advanced studies or research-intensive careers. By engaging students in real-world, hands-on research projects, they not only sharpen their problem-solving skills but also develop the intellectual independence needed to tackle complex engineering challenges. These benefits are amplified when the research experience is multidisciplinary, allowing students to engage with topics beyond the confines of their chosen major. Moreover, participation in a collaborative cohort—where continual interactions and shared learning experiences occur—helps foster a sense of community and shared purpose, further enhancing the learning process. This paper presents the outcomes and impacts of a unique undergraduate research program conducted collaboratively between Oklahoma State University, Stillwater, and the University of Alabama in Huntsville. What sets this program apart is its fusion of engineering and engineering technology disciplines, its blend of applied and fundamental research, and its focus on multidisciplinary topics such as human safety, fire protection technology, mechanical engineering technology, electrical engineering, and artificial intelligence. The program engages students from sophomore to senior levels, offering them a chance to explore various research methodologies and work on projects that span multiple fields of engineering. This exposure helps them cultivate a comprehensive understanding of engineering systems and their real-world applications. In this paper, we will delve into the structure and activities of the Research Experiences for Undergraduates (REU) program, discussing its various components as well as the educational and research outcomes it has produced. A central theme of the program is its focus on multidisciplinary research, which ranges from technical fields such as fire protection and mechanical engineering technology to more advanced areas like electrical engineering and artificial intelligence. This breadth of topics ensures that students are equipped with a wide range of skills, from analytical problem-solving to creative thinking, as they learn to approach engineering challenges from multiple perspectives. Additionally, the program’s emphasis on cohort-building activities plays a crucial role in shaping the students’ experiences. By promoting collaboration among students from different disciplines, the program encourages the cross-pollination of ideas, mutual learning, and the development of soft skills such as communication, teamwork, and leadership. The interactions fostered within the cohort help students build a network of peers who share similar academic and career aspirations, strengthening their commitment to research and professional development. The paper will also present the results of both formative and summative assessments of the program, highlighting its impacts on student learning, skill development, and long-term career trajectories. By examining these outcomes, we demonstrate how this collaborative and multidisciplinary research program has successfully nurtured the next generation of independent researchers and engineering leaders, equipping them to meet the challenges of an increasingly complex and interconnected world. 

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4. Relations between undergraduates’ self-regulated learning skill mastery during digital training and biology performance

   https://doi.org/10.1007/s11409-023-09356-9  

   Bernacki, Matthew L.; Cogliano, Megan Claire; Kuhlmann, Shelbi L.; Utz, Jenifer; Strong, Christy; Hilpert, Jonathan C.; Greene, Jeffrey A. (December 2023, Metacognition and Learning)

   Anastasia Efklides, Ph.D. (Ed.)

   Abstract Undergraduate STEM lecture courses enroll hundreds who must master declarative, conceptual, and applied learning objectives. To support them, instructors have turned to active learning designs that require students to engage inself-regulated learning(SRL). Undergraduates struggle with SRL, and universities provide courses, workshops, and digital training to scaffold SRL skill development and enactment. We examined two theory-aligned designs of digital skill trainings that scaffold SRL and how students’ demonstration of metacognitive knowledge of learning skills predicted exam performance in biology courses where training took place. In Study 1, students’ (n = 49) responses to training activities were scored for quality and summed by training topic and level of understanding. Behavioral and environmental regulation knowledge predicted midterm and final exam grades; knowledge of SRL processes did not. Declarative and conceptual levels of skill-mastery predicted exam performance; application-level knowledge did not. When modeled by topic at each level of understanding, declarative knowledge of behavioral and environmental regulation and conceptual knowledge of cognitive strategies predicted final exam performance. In Study 2 (n = 62), knowledge demonstrated during a redesigned video-based multimedia version of behavioral and environmental regulation again predicted biology exam performance. Across studies, performance on training activities designed in alignment with skill-training models predicted course performances and predictions were sustained in a redesign prioritizing learning efficiency. Training learners’ SRL skills –and specifically cognitive strategies and environmental regulation– benefited their later biology course performances across studies, which demonstrate the value of providing brief, digital activities to develop learning skills. Ongoing refinement to materials designed to develop metacognitive processing and learners’ ability to apply skills in new contexts can increase benefits. 

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5. AI, visual imagery, and a case study on the challenges posed by human intelligence tests

   https://doi.org/10.1073/pnas.1912335117  

   Kunda, Maithilee (November 2020, Proceedings of the National Academy of Sciences)

   null (Ed.)

   Observations abound about the power of visual imagery in human intelligence, from how Nobel prize-winning physicists make their discoveries to how children understand bedtime stories. These observations raise an important question for cognitive science, which is, what are the computations taking place in someone’s mind when they use visual imagery? Answering this question is not easy and will require much continued research across the multiple disciplines of cognitive science. Here, we focus on a related and more circumscribed question from the perspective of artificial intelligence (AI): If you have an intelligent agent that uses visual imagery-based knowledge representations and reasoning operations, then what kinds of problem solving might be possible, and how would such problem solving work? We highlight recent progress in AI toward answering these questions in the domain of visuospatial reasoning, looking at a case study of how imagery-based artificial agents can solve visuospatial intelligence tests. In particular, we first examine several variations of imagery-based knowledge representations and problem-solving strategies that are sufficient for solving problems from the Raven’s Progressive Matrices intelligence test. We then look at how artificial agents, instead of being designed manually by AI researchers, might learn portions of their own knowledge and reasoning procedures from experience, including learning visuospatial domain knowledge, learning and generalizing problem-solving strategies, and learning the actual definition of the task in the first place. 

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