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1. Artificial Intelligence-Empowered Chatbot for Effective COVID-19 Information Delivery to Older Adults

   https://doi.org/10.4018/IJEHMC.293285  

   Wang, Xin ; Liang, Tianyi ; Li, Juan ; Roy, Souradip ; Pandey, Vikram ; Du, Yang ; Kong, Jun ( November 2021 , International Journal of E-Health and Medical Communications)

   The coronavirus disease 2019 (COVID-19) epidemic poses a threat to the everyday life of people worldwide and brings challenges to the global health system. During this outbreak, it is critical to find creative ways to extend the reach of informatics into every person in society. Although there are many websites and mobile applications for this purpose, they are insufficient in reaching vulnerable populations like older adults who are not familiar with using new technologies to access information. In this paper, we propose an AI-enabled chatbot assistant that delivers real-time, useful, context-aware, and personalized information about COVID-19 to users, especially older adults. To use the assistant, a user simply speaks to it through a mobile phone or a smart speaker. This natural and interactive interface does not require the user to have any technical background. The virtual assistant was evaluated in the lab environment through various types of use cases. Preliminary qualitative test results demonstrate a reasonable precision and recall rate. 

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2. Identifying Ridesharing Risk, Response, and Challenges in the Emergence of Novel Coronavirus Using Interactions in Uber Drivers Forum

   https://doi.org/10.3389/fbuil.2021.619283  

   Mojumder, Md Nizamul ; Ahmed, Md Ashraf ; Sadri, Arif Mohaimin ( February 2021 , Frontiers in Built Environment)

   null (Ed.)

   The outbreak and emergence of the novel coronavirus (COVID-19) pandemic affected every aspect of human activity, especially the transportation sector. Many cities adopted unprecedented lockdown strategies that resulted in significant nonessential mobility restrictions; hence, transportation network companies (TNCs) have experienced major shifts in their operation. Millions of people alone in the USA have filed for unemployment in the early stage of the COVID-19 outbreak, many belonging to self-employed groups such as Uber/Lyft drivers. Due to unprecedented scenarios, both drivers and passengers experienced overwhelming challenges that might elongate the recovery process. The goal of this study is to understand the risk, response, and challenges associated with ridesharing (TNCs, drivers, and passengers) during the COVID-19 pandemic situation. As such, large-scale crowdsourced data were collected from online ridesharing forums (i.e., Uber Drivers) since the emergence of COVID-19 (January 25–May 10, 2020). Word bigrams, word frequency heatmaps, and topic models are among the different natural language processing and text-mining techniques used to preprocess the data and classify risk perception, risk-taking, or risk-averting behaviors associated with ridesharing during a major disease outbreak. Results indicate higher levels of concern about economic disruption, availability of stimulus checks, new employment opportunities, hospitalization, pandemic, personal hygiene, and staying at home. In addition, unprecedented challenges due to unemployment and the risk and uncertainties in the required personal protective actions against spreading the disease due to sharing are among the major interactions. The proposed text-based data analytics of the ridesharing risk communication dynamics during this pandemic will help to identify unobserved factors inadvertently affecting the TNCs as well as the users (drivers and passengers) and identify more efficient strategies and alternatives for the forthcoming “new normal” of the current pandemic and the ones in the future. The study will also guide us toward understanding how efficiently online social interaction outlets can be designed and implemented more effectively during a major crisis and how to leverage such platforms for providing guidelines during emergencies to minimize transmission of disease due to shared travel. 

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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. VIRTBOT: EXPLORING CHATBOT DESIGN FOR PROMOTING SCIENTIFIC INITIATIVES

   https://doi.org/10.48009/4_iis_2021_74-87  

   Lombardi, T. ; North, M. ; Orange, N. B. ; Coulanges, K. ; Johnson, J. ( January 2021 , Issues In Information Systems)

   Chatbots have proven to be effective tools in the fields of marketing, sales, customer relationship management and many other applications. This research explores the opportunities for chatbots to contribute to the promotion of scientific research and initiatives. The Etelman Observatory Research Center of the University of the Virgin Islands (UVI) houses the Virgin Islands Robotic Telescope (VIRT), a fully-automated, robotically controlled, and queue-driven 0.5 meter research grade telescope. The Etelman Observatory's mission is to be a world-class research and education center that engages with the local community through various outreach activities in all its initiatives. Given the challenges of physical presence during the COVID-19 crisis, Observatory personnel decided to adopt chatbot technology to engage interested parties over the Internet in its key scientific instrument for astrophysics -- VIRT. VIRTBot is a chatbot designed to provide VIRT with a voice that interested community members can engage with directly. The team implemented VIRTBot with Amazon Web Services (AWS) technologies in the cloud and deployed the solution online. Volunteers were surveyed about their knowledge of the Observatory's activities after reviewing either an FAQ or engaging with VIRTBot. The study demonstrated that the FAQ outperformed VIRTBot in terms of knowledge dissemination, but VIRTBot outperformed the FAQ in measures of interest and engagement. Our research suggests that, under the right conditions, chatbots improve engagement over traditional web resources in promoting STEM educational initiatives to the public. Keywords: Chatbot, Amazon Web Services 

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5. Study on U.S. Parents' Divisions of Labor During COVID-19, Waves 1-2

   https://doi.org/10.3886/E183142V2  

   Carlson, Daniel L. ; Petts, Richard J. ( January 2022 , ICPSR - Interuniversity Consortium for Political and Social Research)

   The COVID-19 pandemic has dramatically altered family life in the United States. Over the long duration of the pandemic, parents had to adapt to shifting work conditions, virtual schooling, the closure of daycare facilities, and the stress of not only managing households without domestic and care supports but also worrying that family members may contract the novel coronavirus. Reports early in the pandemic suggest that these burdens have fallen disproportionately on mothers, creating concerns about the long-term implications of the pandemic for gender inequality and mothers’ well-being. Nevertheless, less is known about how parents’ engagement in domestic labor and paid work has changed throughout the pandemic, what factors may be driving these changes, and what the long-term consequences of the pandemic may be for the gendered division of labor and gender inequality more generally.
   
   The Study on U.S. Parents’ Divisions of Labor During COVID-19 (SPDLC) collects longitudinal survey data from partnered U.S. parents that can be used to assess changes in parents’ divisions of domestic labor, divisions of paid labor, and well-being throughout and after the COVID-19 pandemic. The goal of SPDLC is to understand both the short- and long-term impacts of the pandemic for the gendered division of labor, work-family issues, and broader patterns of gender inequality.
   
   Survey data for this study is collected using Prolifc (www.prolific.co), an opt-in online platform designed to facilitate scientific research. The sample is comprised U.S. adults who were residing with a romantic partner and at least one biological child (at the time of entry into the study). In each survey, parents answer questions about both themselves and their partners. Wave 1 of SPDLC was conducted in April 2020, and parents who participated in Wave 1 were asked about their division of labor both prior to (i.e., early March 2020) and one month after the pandemic began. Wave 2 of SPDLC was collected in November 2020. Parents who participated in Wave 1 were invited to participate again in Wave 2, and a new cohort of parents was also recruited to participate in the Wave 2 survey. Wave 3 of SPDLC was collected in October 2021. Parents who participated in either of the first two waves were invited to participate again in Wave 3, and another new cohort of parents was also recruited to participate in the Wave 3 survey. This research design (follow-up survey of panelists and new cross-section of parents at each wave) will continue through 2024, culminating in six waves of data spanning the period from March 2020 through October 2024. An estimated total of approximately 6,500 parents will be surveyed at least once throughout the duration of the study.
   
   SPDLC data will be released to the public two years after data is collected; Waves 1 and 2 are currently publicly available. Wave 3 will be publicly available in October 2023, with subsequent waves becoming available yearly. Data will be available to download in both SPSS (.sav) and Stata (.dta) formats, and the following data files will be available: (1) a data file for each individual wave, which contains responses from all participants in that wave of data collection, (2) a longitudinal panel data file, which contains longitudinal follow-up data from all available waves, and (3) a repeated cross-section data file, which contains the repeated cross-section data (from new respondents at each wave) from all available waves. Codebooks for each survey wave and a detailed user guide describing the data are also available. Response Rates: Of the 1,157 parents who participated in Wave 1, 828 (72%) also participated in the Wave 2 study. Presence of Common Scales: The following established scales are included in the survey:
   

   • Self-Efficacy, adapted from Pearlin's mastery scale (Pearlin et al., 1981) and the Rosenberg self-esteem scale (Rosenberg, 2015) and taken from the American Changing Lives Survey
   • Communication with Partner, taken from the Marriage and Relationship Survey (Lichter & Carmalt, 2009)
     
   • Gender Attitudes, taken from the National Survey of Families and Households (Sweet & Bumpass, 1996)
     
   • Depressive Symptoms (CES-D-10)
   • Stress, measured using Cohen's Perceived Stress Scale (Cohen, Kamarck, & Mermelstein, 1983)
     

   Full details about these scales and all other items included in the survey can be found in the user guide and codebook
   The second wave of the SPDLC was fielded in November 2020 in two stages. In the first stage, all parents who participated in W1 of the SPDLC and who continued to reside in the United States were re-contacted and asked to participate in a follow-up survey. The W2 survey was posted on Prolific, and messages were sent via Prolific’s messaging system to all previous participants. Multiple follow-up messages were sent in an attempt to increase response rates to the follow-up survey. Of the 1,157 respondents who completed the W1 survey, 873 at least started the W2 survey. Data quality checks were employed in line with best practices for online surveys (e.g., removing respondents who did not complete most of the survey or who did not pass the attention filters). After data quality checks, 5.2% of respondents were removed from the sample, resulting in a final sample size of 828 parents (a response rate of 72%).
   
   In the second stage, a new sample of parents was recruited. New parents had to meet the same sampling criteria as in W1 (be at least 18 years old, reside in the United States, reside with a romantic partner, and be a parent living with at least one biological child). Also similar to the W1 procedures, we oversampled men, Black individuals, individuals who did not complete college, and individuals who identified as politically conservative to increase sample diversity. A total of 1,207 parents participated in the W2 survey. Data quality checks led to the removal of 5.7% of the respondents, resulting in a final sample size of new respondents at Wave 2 of 1,138 parents.
   
   In both stages, participants were informed that the survey would take approximately 20 minutes to complete. All panelists were provided monetary compensation in line with Prolific’s compensation guidelines, which require that all participants earn above minimum wage for their time participating in studies.
   To be included in SPDLC, respondents had to meet the following sampling criteria at the time they enter the study: (a) be at least 18 years old, (b) reside in the United States, (c) reside with a romantic partner (i.e., be married or cohabiting), and (d) be a parent living with at least one biological child. Follow-up respondents must be at least 18 years old and reside in the United States, but may experience changes in relationship and resident parent statuses. Smallest Geographic Unit: U.S. State

   This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. In accordance with this license, all users of these data must give appropriate credit to the authors in any papers, presentations, books, or other works that use the data. A suggested citation to provide attribution for these data is included below:            
   
   Carlson, Daniel L. and Richard J. Petts. 2022. Study on U.S. Parents’ Divisions of Labor During COVID-19 User Guide: Waves 1-2.  

   To help provide estimates that are more representative of U.S. partnered parents, the SPDLC includes sampling weights. Weights can be included in statistical analyses to make estimates from the SPDLC sample representative of U.S. parents who reside with a romantic partner (married or cohabiting) and a child aged 18 or younger based on age, race/ethnicity, and gender. National estimates for the age, racial/ethnic, and gender profile of U.S. partnered parents were obtained using data from the 2020 Current Population Survey (CPS). Weights were calculated using an iterative raking method, such that the full sample in each data file matches the nationally representative CPS data in regard to the gender, age, and racial/ethnic distributions within the data. This variable is labeled CPSweightW2 in the Wave 2 dataset, and CPSweightLW2 in the longitudinal dataset (which includes Waves 1 and 2). There is not a weight variable included in the W1-W2 repeated cross-section data file.
    

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