Title: Does using artificial intelligence assistance accelerate skill decay and hinder skill development without performers’ awareness?
Abstract Artificial intelligence in the workplace is becoming increasingly common. These tools are sometimes used to aid users in performing their task, for example, when an artificial intelligence tool assists a radiologist in their search for abnormalities in radiographic images. The use of artificial intelligence brings a wealth of benefits, such as increasing the efficiency and efficacy of performance. However, little research has been conducted to determine how the use of artificial intelligence assistants might affect the user’s cognitive skills. In this theoretical perspective, we discuss how artificial intelligence assistants might accelerate skill decay among experts and hinder skill acquisition among learners. Further, we discuss how AI assistants might also prevent experts and learners from recognizing these deleterious effects. We then discuss the types of questions: use-inspired basic cognitive researchers, applied researchers, and computer science researchers should seek to answer. We conclude that multidisciplinary research from use-inspired basic cognitive research, domain-specific applied research, and technical research (e.g., human factors research, computer science research) is needed to (a) understand these potential consequences, (b) design artificial intelligence systems to mitigate these impacts, and (c) develop training and use protocols to prevent negative impacts on users’ cognitive skills. Only by answering these questions from multidisciplinary perspectives can we harness the benefits of artificial intelligence in the workplace while preventing negative impacts on users’ cognitive skills. more »« less
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.
Cambre, Julia; Reig, Samantha; Kravitz, Queenie; Kulkarni, Chinmay
(, 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.
Sahoo, Avimanyu; Park, Haejun
(, 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.
Bernacki, Matthew L.; Cogliano, Megan Claire; Kuhlmann, Shelbi L.; Utz, Jenifer; Strong, Christy; Hilpert, Jonathan C.; Greene, Jeffrey A.
(, Metacognition and Learning)
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.
Virtual reality (VR) and interactive 3D visualization systems have enhanced educational experiences and environments, particularly in complicated subjects such as anatomy education. VR-based systems surpass the potential limitations of traditional training approaches in facilitating interactive engagement among students. However, research on embodied virtual assistants that leverage generative artificial intelligence (AI) and verbal communication in the anatomy education context is underrepresented. In this work, we introduce a VR environment with a generative AI-embodied virtual assistant to support participants in responding to varying cognitive complexity anatomy questions and enable verbal communication. We assessed the technical efficacy and usability of the proposed environment in a pilot user study with 16 participants. We conducted a within-subject design for virtual assistant configuration (avatar- and screen-based), with two levels of cognitive complexity (knowledge- and analysis-based). The results reveal a significant difference in the scores obtained from knowledge- and analysis-based questions in relation to avatar configuration. Moreover, results provide insights into usability, cognitive task load, and the sense of presence in the proposed virtual assistant configurations. Our environment and results of the pilot study offer potential benefits and future research directions beyond medical education, using generative AI and embodied virtual agents as customized virtual conversational assistants.
@article{osti_10522794,
place = {Country unknown/Code not available},
title = {Does using artificial intelligence assistance accelerate skill decay and hinder skill development without performers’ awareness?},
url = {https://par.nsf.gov/biblio/10522794},
DOI = {10.1186/s41235-024-00572-8},
abstractNote = {Abstract Artificial intelligence in the workplace is becoming increasingly common. These tools are sometimes used to aid users in performing their task, for example, when an artificial intelligence tool assists a radiologist in their search for abnormalities in radiographic images. The use of artificial intelligence brings a wealth of benefits, such as increasing the efficiency and efficacy of performance. However, little research has been conducted to determine how the use of artificial intelligence assistants might affect the user’s cognitive skills. In this theoretical perspective, we discuss how artificial intelligence assistants might accelerate skill decay among experts and hinder skill acquisition among learners. Further, we discuss how AI assistants might also prevent experts and learners from recognizing these deleterious effects. We then discuss the types of questions: use-inspired basic cognitive researchers, applied researchers, and computer science researchers should seek to answer. We conclude that multidisciplinary research from use-inspired basic cognitive research, domain-specific applied research, and technical research (e.g., human factors research, computer science research) is needed to (a) understand these potential consequences, (b) design artificial intelligence systems to mitigate these impacts, and (c) develop training and use protocols to prevent negative impacts on users’ cognitive skills. Only by answering these questions from multidisciplinary perspectives can we harness the benefits of artificial intelligence in the workplace while preventing negative impacts on users’ cognitive skills.},
journal = {Cognitive Research: Principles and Implications},
volume = {9},
number = {1},
publisher = {Springer Science + Business Media},
author = {Macnamara, Brooke_N and Berber, Ibrahim and Çavuşoğlu, M_Cenk and Krupinski, Elizabeth_A and Nallapareddy, Naren and Nelson, Noelle_E and Smith, Philip_J and Wilson-Delfosse, Amy_L and Ray, Soumya},
}
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