The Relationship Between Performance and Trust in AI in E-Finance.

Artificial intelligence (AI) is fundamentally changing how people work in nearly every field, including online finance. However, our ability to interact with AI is moderated by factors such as performance, complexity, and trust. The work presented in this study analyzes the effect of performance on trust in a robo-advisor (AI which assists in managing investments) through an empirical investment simulation. Results show that for applications where humans and AI have comparable capabilities, the difference in performance (between the human and AI) is a moderate indicator of change in trust; however, human or AI performance individually were weak indicators. Additionally, results indicate that biases typically seen in human-human interactions may also occur in human-AI interactions when AI transparency is low.

Deriving Effective Decision-Making Strategies of Prosthetists: Using Hidden Markov Modeling and Qualitative Analysis to Compare Experts and Novices.

OBJECTIVE: This research focuses on studying the clinical decision-making strategies of expert and novice prosthetists for different case complexities. BACKGROUND: With an increasing global amputee population, there is an urgent need for improved amputee care. However, current prosthetic prescription standards are based on subjective expertise, making the process challenging for novices, specifically during complex patient cases. Hence, there is a need for studying the decision-making strategies of prosthetists. METHOD: An interactive web-based survey was developed with two case studies of varying complexities. Navigation between survey pages and time spent were recorded for 28 participants including experts (n = 20) and novices (n = 8). Using these data, decision-making strategies, or patterns of decisions, during prosthetic prescription were derived using hidden Markov modeling. A qualitative analysis of participants' rationale regarding decisions was used to add a deep contextualized understanding of decision-making strategies derived from the quantitative analysis. RESULTS: Unique decision-making strategies were observed across expert and novice participants. Experts tended to focus on the personal details, activity level, and state of the residual limb prior to prescription, and this strategy was independent of case complexity. Novices tended to change strategies dependent upon case complexity, fixating on certain factors when case complexity was high. CONCLUSION: The decision-making strategies of experts stayed the same across the two cases, whereas the novices exhibited mixed strategies. APPLICATION: By modeling the decision-making strategies of experts and novices, this study builds a foundation for development of an automated decision-support tool for prosthetic prescription, advancing novice training, and amputee care.

Using workplace thriving theory to investigate first-year engineering students' abilities to thrive during the transition to online learning due to COVID-19.

Background: During the onset of the COVID-19 crisis, universities rapidly pivoted to online formats and were often unable to adhere to the best practices of online learning highlighted in prior literature. It is well documented that a variety of barriers impeded "normal" educational practices. Purpose/Hypothesis: The purpose of this paper is to investigate the perceptions of first-year engineering students enrolled in an introductory engineering design course during the rapid transition to online working environments. We view students' perceptions through the theoretical lens of workplace thriving theory, a framework that allowed us to capture aspects of education required for students to thrive in non-optimum learning settings. Design/Method: This research employed semi-structured interview methods with 13 students enrolled in an introductory engineering design course that relies on project-based team learning. We analyzed interview transcripts using thematic analysis through an abductive approach and made interpretations through workplace thriving theory. Results: Results indicated that students' abilities to thrive are related to four intersecting themes that demonstrate how workplace thriving theory manifests in this unanticipated online setting. These themes demonstrate elements that must be optimized for students to thrive in settings such as this: relationships with others, building and sharing knowledge through interactions, perceptions of experiential learning, and individual behaviors. Conclusion: Our research, viewed through workplace thriving theory, highlights the mechanisms by which students tried to succeed in suboptimal environments. While not all our participants showed evidence of thriving, the factors required for thriving point to opportunities to harness these same factors in in-person instruction environments.

Image collection of 3D-printed prototypes and non-3D-printed prototypes.

Image processing refers to the use of computer algorithms to manipulate and enhance digital images to improve their quality or to make them more suitable for tasks such as classification. Common benchmarking datasets in this field include the imagenet, CIFAR-100, and MNIST datasets. This dataset is a collection of images that are particularly relevant to engineering and design, consisting of two categories: 3D-printed prototypes, and non-3D-printed prototypes This data was collected through a hybrid approach that entailed both web scraping and direct collection from engineering labs and workspaces at Penn State University. The initial data was then augmented using several data augmentation techniques including rotation, noise, blur, and color shifting. This dataset is potentially useful to train image classification algorithms or attentional mapping approaches. This data can be used either by itself or used to bolster an existing image classification dataset.