Artificial Intelligence (AI) Student Assistants in the Classroom: Designing Chatbots to Support Student Success

2023 | Information Systems Frontiers | Citations: 1

Authors: Chen, Yu; Jensen, Scott; Albert, Leslie J.; Gupta, Sambhav; Lee, Terri

Abstract: In higher education, low teacher-student ratios can make it difficult for studen ... Expand

Abstract: In higher education, low teacher-student ratios can make it difficult for students to receive immediate and interactive help. Chatbots, increasingly used in various scenarios such as customer service, work productivity, and healthcare, might be one way of helping instructors better meet student needs. However, few empirical studies in the field of Information Systems (IS) have investigated pedagogical chatbot efficacy in higher education and fewer still discuss their potential challenges and drawbacks. In this research we address this gap in the IS literature by exploring the opportunities, challenges, efficacy, and ethical concerns of using chatbots as pedagogical tools in business education. In this two study project, we conducted a chatbot-guided interview with 215 undergraduate students to understand student attitudes regarding the potential benefits and challenges of using chatbots as intelligent student assistants. Our findings revealed the potential for chatbots to help students learn basic content in a responsive, interactive, and confidential way. Findings also provided insights into student learning needs which we then used to design and develop a new, experimental chatbot assistant to teach basic AI concepts to 195 students. Results of this second study suggest chatbots can be engaging and responsive conversational learning tools for teaching basic concepts and for providing educational resources. Herein, we provide the results of both studies and discuss possible promising opportunities and ethical implications of using chatbots to support inclusive learning. Collapse

Topics: artificial intelligence IT career anonymity user experience accounting

Methods: chatbot survey qualitative interview personal interview machine learning

Pro or Con? Introducing AI Ethics Debates in an Undergraduate MIS Course

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Chen, Yu; Jensen, Scott; Roldan, Malu; Harper, Shannon

Abstract: Artificial intelligence (AI) is becoming pervasive across industries, making it ... Expand

Abstract: Artificial intelligence (AI) is becoming pervasive across industries, making it important for management information systems (MIS) students to understand the ethical issues involved. We present an active learning approach to teaching the fast-changing topic of AI ethics using a debate format. This approach was piloted in an undergraduate MIS course of 30 students. Over a five-week period, student teams were assigned to argue either the opportunities (pro) or dangers (con) viewpoint for five different AI technologies. A post-project survey indicated this format helps students gain a better understanding of the applications, opportunities, and potential misuse of AI. Students found this to be an engaging and fun way to explore the multiple dimensions of AI ethics that also required them to employ critical thinking, collaboration, research, and communication skills. We share our findings about the benefits of co-creating knowledge in the classroom using a debate format to explore an evolving topic. Collapse

Topics: responsible artificial intelligence artificial intelligence innovation management digital innovation robotic

Methods: survey personal interview image processing design methodology

Roadside Unit-based Unknown Object Detection in Adverse Weather Conditions for Smart Internet of Vehicles

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Chen, Yu-Chia; Jhong, Sin-Ye; Hsia, Chih-Hsien

Abstract: For Internet of Vehicles applications, reliable autonomous driving systems usual ... Expand

Abstract: For Internet of Vehicles applications, reliable autonomous driving systems usually perform the majority of their computations on the cloud due to the limited computing power of edge devices. The communication delay between cloud platforms and edge devices, however, can cause dangerous consequences, particularly for latency-sensitive object detection tasks. Object detection tasks are also vulnerable to significantly degraded model performance caused by unknown objects, which creates unsafe driving conditions. To address these problems, this study develops an orchestrated system that allows real-time object detection and incrementally learns unknown objects in a complex and dynamic environment. A you-only-look-once–based object detection model in edge computing mode uses thermal images to detect objects accurately in poor lighting conditions. In addition, an attention mechanism improves the system’s performance without significantly increasing model complexity. An unknown object detector automatically classifies and labels unknown objects without direct supervision on edge devices, while a roadside unit (RSU)-based mechanism is developed to update classes and ensure a secure driving experience for autonomous vehicles. Moreover, the interactions between edge devices, RSU servers, and the cloud are designed to allow efficient collaboration. The experimental results indicate that the proposed system learns uncategorized objects dynamically and detects instances accurately. Collapse

Topics: cloud computing autonomous driving system database system internet technology internet of things

Methods: computational algorithm cluster analysis design artifact experiment artificial neural network

Conversation Analytics: Can Machines Read between the Lines in Real-Time Strategic Conversations?

2023 | HICSS | Citations: 0

Authors: Chen, Y.; Rui, H.; Whinston, A.

Abstract: Strategic conversations involve one party with an informational advantage and t ... Expand

Abstract: Strategic conversations involve one party with an informational advantage and the other with an interest in the information. This paper proposes machinelearning based measures to quantify the degrees of evasiveness and incoherence of the informed party during real-time strategic conversations. The specific empirical context is the questions and answers (Q&A) part of earnings conference calls during which managers endure high pressure as they face analysts' scrutinizing questions. Being reluctant to disclose adverse information, managers may resort to evasive answers and sometimes respond less coherently due to increased cognitive load. Using data from the earnings calls of the S&P 500 companies from 2006 to 2018, we show that the proposed measures predict worse nextquarter earnings. Moreover, the stock market perceives incoherence as a negative signal. This paper contributes methodologically by developing two novel machinepowered measures to automatically evaluate behavioral cues during real-time strategic conversations. The proposed analytical tools are particularly beneficial to resource-constrained and informationally disadvantaged parties such as retail investors who may not be able to effectively trade on signals buried deep in unstructured conversational data. Collapse

Topics: database system artificial intelligence electronic finance missing data accounting

Methods: computational algorithm BERT deep learning machine learning next sentence prediction

Theories: theory of economic growth

Stand for Something or Fall for Everything: Predict Misinformation Spread with Stance-Aware Graph Neural Networks

2023 | International Conference on Information Systems | Citations: 0

Authors: Chen, Zihan; Sun, Jingyi; Liu, Rong Emily; Mai, Feng

Abstract: Although pervasive spread of misinformation on social media platforms has becom ... Expand

Abstract: Although pervasive spread of misinformation on social media platforms has become a pressing challenge, existing platform interventions have shown limited success in curbing its dissemination. In this study, we propose a stance-aware graph neural network (stance-aware GNN) that leverages users' stances to proactively predict misinformation spread. As different user stances can form unique echo chambers, we customize four information passing paths in stance-aware GNN, while the trainable attention weights provide explainability by highlighting each structure's importance. Evaluated on a realworld dataset, stance-aware GNN outperforms benchmarks by 32.65% and exceeds advanced GNNs without user stance by over 4.69%. Furthermore, the attention weights indicate that users' opposition stances have a higher impact on their neighbors' behaviors than supportive ones, which function as social correction to halt misinformation propagation. Overall, our study provides an effective predictive model for platforms to combat misinformation, and highlights the impact of user stances in the misinformation propagation. Collapse

Topics: misinformation Twitter filter bubble social media social influence

Methods: machine learning RoBERTa experiment deep learning graph neural network

Impact of Own Brand Product Introduction on Optimal Pricing Models for Platform and Incumbent Sellers

2023 | Information Systems Research | Citations: 0

Authors: Cheng, Hsing Kenneth; Jung, Kyung Sung; Kwark, Young; Pu, Jingchuan

Abstract: Sales on the e-commerce platform in the United States have experienced explosive ... Expand

Abstract: Sales on the e-commerce platform in the United States have experienced explosive growth and are projected to surpass $740 billion in 2023. The expansion of the platform’s traditional role as a reseller into an online marketplace and the introduction of its own brand products have stoked a huge fear among the incumbent sellers. The platform’s unfair anticompetitive practice further aggravates the situation. Consequently, politicians and regulators have proposed prohibiting platforms from introducing own brand products to protect the incumbent sellers. This study addresses two questions of critical interest to both the policymakers and the incumbent sellers. First, how does the platform’s introducing its own brand product affect the incumbent sellers? Second, how effective is the proposed policy in terms of protecting the incumbent sellers? We examine the impact of the platform’s own brand introduction on the incumbent sellers under two prevailing sell-on and sell-to pricing contracts. We find that the proposed legislation “that prohibits platforms from both offering a marketplace for commerce and participating in that marketplace” does not have the desired outcome of helping the incumbent sellers. Instead, it forces the platform to adopt only the sell-to contract with the own brand introduction that hurts the sellers under most market conditions. Interestingly, when the own brand introduction is banned under the sell-to contract, the incumbent sellers can be better off because the platform’s strategic reaction to the enforcement can lead to the best scenario for the incumbent sellers. If the ban is imposed on both the sell-on and sell-to contracts, the platform’s best response is to add another new brand competing with the incumbent sellers, which can also help the incumbent sellers, however, not as much as in the case of the enforcement only under the sell-to contract. History: Ram Gopal, Senior Editor; Hong Xu, Associate Editor. Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2022.1175. Collapse

Topics: price management electronic commerce duopoly competitive advantage marketing management

Methods: cross sectional research

Theories: game theory

The Value of Social Media Customer Support During the Pandemic

2023 | HICSS | Citations: 0

Authors: Cheng, Huai-Tzu; Pan, Yang; Hirschheim, Rudy

Abstract: Many companies are utilizing social media as the primary avenue for customer ser ... Expand

Abstract: Many companies are utilizing social media as the primary avenue for customer service during the pandemic. However, how customers’ behaviors and interactions with customer service agents on social media are impacted by the lockdowns has not been well understood. In this study, we examine the impact of lockdowns and physical distancing on changes in customers’ behaviors, such as emotional expressions in tweets and customers’ satisfaction with social media customer service. Using a difference-in-differences research design, we find that with the lockdowns and physical distancing, customers expressed more negative emotions when tweeting the company they were having issues with. Surprisingly, compared to before the pandemic period, customers’ emotional expressions became more positive and they were more likely to express their satisfaction after interacting with customer service agents. Interestingly, our findings reveal that gender differences exist in these scenarios. We also discuss the theoretical and practical implications of these findings. Collapse

Topics: Twitter customer service social media pandemic customer satisfaction

Methods: experimental group two sample t-test with equal variance propensity score matching parametric test robustness check

Intellectual capital and team resilience capability of information system development project teams

2023 | Information & Management | Citations: 16

Authors: Cheng, Kuang-Ting; Hsu, Jack Shih-Chieh; Li, Yuzhu; Brading, Ryan

Abstract: Adversities are inherent in the information system development (ISD) process and ... Expand

Abstract: Adversities are inherent in the information system development (ISD) process and often put projects to a halt. However, it is unclear what capabilities the team needs to resist and bounce back from adverse events. The purpose of this study is to propose that team resilience capability (TRC), containing affective, cognitive, and behavioral factors is vital for effective project performance. Further, by adopting the conservation of resource perspective, we theorize that intellectual capital, including human capital, technology capital, and political capital fosters TRC. Survey data collected from 149 ISD project teams confirmed our ideas that TRC is strongly tied with project performance and is more affected by human capital, followed by political capital and tech­ nology capital. Collapse

Topics: human resource management project productivity systems development information technology capability pandemic

Methods: survey partial least squares regression regression analysis method literature study business process modeling

Pay-for-performance schemes and hospital HIT adoption

2023 | Decision Support Systems | Citations: 0

Authors: Cheng, Ningning; Li, Hongfei; Bang, Youngsok

Abstract: Pay-for-performance (P4P) schemes are implemented to incentivize or penalize hos ... Expand

Abstract: Pay-for-performance (P4P) schemes are implemented to incentivize or penalize hospitals for their safe care­ giving. Given that health information technology (HIT) results in better healthcare outcomes, P4P schemes are expected to promote hospital HIT adoption. However, P4P schemes could also discourage hospitals from adopting HIT because they may take away resources initially allocated for HIT adoption. This paper is one of the first to empirically investigate the double-edged role of P4P schemes in HIT adoption. We leverage a natural experiment in the US healthcare system, which introduced P4P schemes in 2013. Our empirical analysis reveals an unintended side effect of P4P schemes that could potentially impede HIT adoption. We also find that P4P schemes have a negative spillover effect on nonparticipating hospitals in the same multihospital system (MHS) as participating hospitals, particularly for small nonparticipating hospitals. Our findings provide important impli­ cations for health policy design and MHS management. Collapse

Topics: health information system database system technology adoption organization learning missing data

Methods: longitudinal research experimental group natural experiment survey robustness check

Investigating Students’ Satisfaction with Online Collaborative Learning During the COVID-19 Period: An Expectation-Confirmation Model

2023 | Group Decision and Negotiation | Citations: 0

Authors: Cheng, Xusen; Bao, Ying; Yang, Bo; Chen, Sihua; Zuo, Yiting; Siponen, Mikko

Abstract: The recent outbreak of COVID-19 posed discontinuous disruption to traditional le ... Expand

Abstract: The recent outbreak of COVID-19 posed discontinuous disruption to traditional learning modes worldwide. In order to keep social distance, online collaborative learning has become a necessity during the pandemic. However, our understanding of students’ well-being and satisfaction with online collaborative learning is limited, especially during the COVID-19 period. Leveraging expectation confirmation theory, this study focuses on the triggers and inhibitors of students’ cognitive load during online collaborative learning process and their subsequent satisfaction with the learning mode during the pandemic. We used a mixed-method approach in this study. We conducted a qualitative study with interview data and a quantitative study with surveys. The results indicate several psychological and cognitive antecedents of students’ cognitive load during online collaborative learning. Findings also indicate that a high level of cognitive load will decrease students’ perceived usefulness of the online learning platform and expectation confirmation, thus leading to a low level of satisfaction with online collaborative learning. This study can provide theoretical and practical implications for a better understanding of online student groups’ satisfaction with online collaborative learning during the COVID-19 period. Collapse

Topics: online learning system pandemic perceived usefulness anonymity social network

Methods: survey mixed method qualitative interview statistical hypothesis test partial least squares path modeling

Theories: expectation confirmation theory