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1. Transparency Enhances Positive Perceptions of Social Artificial Intelligence

   Xu, Y.; Bradford, N.; Radhika, G. (January 2023, Human behavior and emerging technologies)

   Social chatbots are designed to build emotional bonds with users, and thus it is particularly important to design these technologies so as to elicit positive perceptions from users. In the current study, we investigate the impacts transparent explanations of chatbots’ mechanisms have on users’ perceptions of the chatbots. A total of 914 participants were recruited from Amazon Mechanical Turk. They were randomly assigned to observe conversation between a hypothetical chatbot and user in one of the two-by-two experimental conditions: whether the participants received an explanation about how the chatbot was trained and whether the chatbot was framed as an intelligent entity or a machine. A fifth group, who believed they were observing interactions between two humans, served as a control. Analyses of participants’ responses to post-observation survey indicated that transparency positively affected perceptions of social chatbots by leading users to (1) find the chatbot less creepy, (2) feel greater affinity to the chatbot, and (3) perceive the chatbot as more socially intelligent, thought these effects were small. Importantly, transparency appeared to have a larger effect in increasing the perceived social intelligence among participants with lower prior AI knowledge. These findings have implications for the design of future social chatbots and support the addition of transparency and explanation for chatbot users. 

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2. Transparency Enhances Positive Perceptions of Social Artificial Intelligence

   https://doi.org/10.1155/2023/5550418  

   Xu, Ying; Bradford, Nora; Garg, Radhika (September 2023, Human Behavior and Emerging Technologies)

   Chakraborty, Pinaki (Ed.)

   Social chatbots are aimed at building emotional bonds with users, and thus it is particularly important to design these technologies so as to elicit positive perceptions from users. In the current study, we investigate the impacts that transparent explanations of chatbots’ mechanisms have on users’ perceptions of the chatbots. A total of 914 participants were recruited from Amazon Mechanical Turk. They were randomly assigned to observe conversations between a hypothetical chatbot and a user in one of the two-by-two experimental conditions: whether the participants received an explanation about how the chatbot was trained and whether the chatbot was framed as an intelligent entity or a machine. A fifth group, who believed they were observing interactions between two humans, served as a control. Analyses of participants’ responses to the postobservation survey indicated that transparency positively affected perceptions of social chatbots by leading users to (1) find the chatbot less creepy, (2) feel greater affinity to the chatbot, and (3) perceive the chatbot as more socially intelligent, though these effects were small. Moreover, transparency appeared to have a larger effect on increasing the perceived social intelligence among participants with lower prior AI knowledge. These findings have implications for the design of future social chatbots and support the addition of transparency and explanation for chatbot users. 

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3. Chatbots: A Tool to Supplement the Future Faculty Mentoring of Doctoral Engineering Students

   https://doi.org/10.28945/4579  

   Mendez, Sylvia; Johanson, Katie; Martin Conley, Valerie; Gosha, Kinnis; A Mack, Naja; Haynes, Comas; A Gerhardt, Rosario (January 2020, International Journal of Doctoral Studies)

   Aim/Purpose: The purpose of this paper is to explore the efficacy of simulated interactive virtual conversations (chatbots) for mentoring underrepresented minority doctoral engineering students who are considering pursuing a career in the professoriate or in industry. Background: Chatbots were developed under the National Science Foundation INCLUDES Design and Developments Launch Pilot award (17-4458) and provide career advice with responses from a pre-programmed database populated by renowned emeriti engineering faculty. Chatbots have been engineered to fulfill a myriad of roles, such as undergraduate student advisement, but no research has been found that addresses their use with supplemental future faculty mentoring for doctoral students.Methodology: Chatbot efficacy is examined through a phenomenological design with focus groups with underrepresented minority doctoral engineering students. No theoretical or conceptual frameworks exist relative to chatbots designed for future faculty mentoring; therefore, an adaptation and implementation of the conceptual model posited on movie recommendations was utilized to ground this study. The four-stage process of phenomenological data analysis was followed: epoché, horizontalization, imaginative variation, and synthesis.Contribution: No studies have investigated the utility of chatbots in providing supplemental mentoring to future faculty. This phenomenological study contributes to this area of investigation and provides greater consideration into the unmet mentoring needs of these students, as well as the potential of utilizing chatbots for supplementary mentoring, particularly for those who lack access to high quality mentoring.Findings: Following the data analysis process, the essence of the findings was, while underrepresented minority doctoral engineering students have ample unmet mentoring needs and overall are satisfied with the user interface and trustworthiness of chatbots, their intent to use them is mixed due to a lack of personalization in this type of supplemental mentoring relationship.Recommendations for Practitioners: One of the major challenges faced by underrepresented doctoral engineering students is securing quality mentoring relationships that socialize them into the engineering culture and community of practice. While creating opportunities for students and incentivizing faculty to engage in the work of mentoring is needed, we must also consider the ways in which to leverage technology to offer supplemental future faculty mentoring virtually. Recommendation for Researchers: Additional research on the efficacy of chatbots in providing career-focused mentoring to future faculty is needed, as well as how to enhance the functionality of chatbots to create personal connections and networking opportunities, which are hallmarks of traditional mentoring relationships.Impact on Society: An understanding of the conceptual pathway that can lead to greater satisfaction with chatbots may serve to expand their use in the realm of mentoring. Scaling virtual faculty mentoring opportunities may be an important breakthrough in meeting mentoring needs across higher education.Future Research: Future chatbot research must focus on connecting chatbot users with human mentors; standardizing the process for response creation through additional data collection with a cadre of diverse, renowned faculty; engaging subject matter experts to conduct quality verification checks on responses; testing new responses with potential users; and launching the chatbots for a broad array of users. 

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4. SciTokens: Capability-Based Secure Access to Remote Scientific Data

   https://doi.org/10.1145/3219104. 3219135  

   Withers, Alex; Bockelman, Brian; Weitzel, Derek; Brown, Duncan; Gaynor, Jeff; Basney, Jim; Tannenbaum, Todd; Miller, Zach (July 2018, PEARC ’18: Practice and Experience in Advanced Research Computing)

   The management of security credentials (e.g., passwords, secret keys) for computational science workflows is a burden for scientists and information security officers. Problems with credentials (e.g., expiration, privilege mismatch) cause workflows to fail to fetch needed input data or store valuable scientific results, distracting scientists from their research by requiring them to diagnose the problems, re-run their computations, and wait longer for their results. In this paper, we introduce SciTokens, open source software to help scientists manage their security credentials more reliably and securely. We describe the SciTokens system architecture, design, and implementation addressing use cases from the Laser Interferometer Gravitational-Wave Observatory (LIGO) Scientific Collaboration and the Large Synoptic Survey Telescope (LSST) projects. We also present our integration with widely-used software that supports distributed scientific computing, including HTCondor, CVMFS, and XrootD. SciTokens uses IETF-standard OAuth tokens for capability-based secure access to remote scientific data. The access tokens convey the specific authorizations needed by the workflows, rather than general-purpose authentication impersonation credentials, to address the risks of scientific workflows running on distributed infrastructure including NSF resources (e.g., LIGO Data Grid, Open Science Grid, XSEDE) and public clouds (e.g., Amazon Web Services, Google Cloud, Microsoft Azure). By improving the interoperability and security of scientific workflows, SciTokens 1) enables use of distributed computing for scientific domains that require greater data protection and 2) enables use of more widely distributed computing resources by reducing the risk of credential abuse on remote systems. 

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5. Developing a Curated Chatbot as an Exploratory Communication Tool for Chemistry Learning

   https://doi.org/10.1021/acs.jchemed.3c00520  

   Lolinco, Annabelle T.; Holme, Thomas A. (October 2023, Journal of Chemical Education)

   In a technology-centric world, leveraging digital tools such as chatbots allows educators to engage students in ways that may be more accessible for both parties, particularly in large lecture classrooms. This report details the development of an interactive web-based chatbot to curate content for writing about chemistry in context. Students were assigned a 500-word paper where they discuss general chemistry concepts through the lens of a timely, sustainability-related topic, i.e., water footprint, carbon footprint, or embodied carbon. Discussed herein are the development of the decision tree, the chatbot’s components, and results from the initial implementation in a large lecture general chemistry classroom. Over 78% of the 347 enrolled students (271) used the chatbot over 350 times in the 3 weeks leading up to the assigned due date of the paper. Eighty-three percent of the interactions were captured for further analysis, which showed that 22% of students used the chatbot more than once. Forty-six percent of recorded interactions were used to aid students in developing or refining their idea for the assignment. The curated chatbot technology reported here for writing assignments in chemistry can be readily adapted to other aspects of coursework in chemistry. 

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