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It’s critical to understand how to use artificial intelligence (AI) to foster innovation in the modern world as AI becomes more integrated into creative and problem-solving tasks. Using the sustainable washing machine as a primary example, this study designed and developed AI design assistant AIDA as a web-based chatbot to facilitate design ideation, leveraging large language models. AIDA prompts design tasks and assesses user-generated ideas for validity, novelty, and feasibility using RoBERTa-based models. As in the initial phase of an ongoing project, we conducted a human-subject experiment to validate a baseline version of AIDA and examined user performance and perceptions. The participants demonstrated smooth interaction with AIDA and consistent performance. They reported mostly positive perceived usefulness, enjoyment, and trust. Moreover, females and participants equal to or over 25 showed a comparable level of trust for general automated systems and AIDA, whereas male and under 25 participants were more skeptical about AIDA. This research offers a framework for technical development, tailored interactions, and real-time feedback, as well as insights into the use of AI chatbots to mediate engineering design. By analyzing user behavior and survey responses, we identified future directions in designing AI systems in engineering education and early-stage design. 

Firms’ public communication on social media during disasters can benefit both disaster response efficiency and the perception of the corporate image. Despite its importance, limited guidelines are available to inform firms’ disaster communication strategies. The current study examines firms’ communication on social media in various disasters and how it impacts public engagement. We employ a novel natural language processing (NLP) approach, Semantic Projection with Active Retrieval (SPAR), to analyze Facebook posts made by Russell 3000 firms between 2009 and 2022 concerning various disasters. We show that firm communication can be measured based on two dimensions derived from the Competing Values Framework (CVF): internal versus external and stable versus flexible. We find that social media messages that emphasize operational continuity (internal/stable-oriented) are more popular during biological disasters. By contrast, messages that stress innovations and adaptations to disasters (external/flexible-oriented) elicit more engagement in weather-related disasters. The study offers a framework to characterize and guide firms’ design of disaster communication on social media in different disaster contexts. Our SPAR method is also available to firms to analyze their social media data and uncover the underlying patterns in communication across different contexts. 

As the integration of artificial intelligence (AI) into team decision-making continues to expand, it is both theoretically and practically pressing for researchers to understand the impact of the technology on team dynamics and performance. To investigate this relationship, we conducted an online experiment in which teams made decisions supported by chatbots and employed computational methods to analyze team interaction processes. Our results indicated that compared to those assisted by chatbots in later phases, teams receiving chatbot assistance during the initial phase of their decision-making process exhibited increased cognitive diversity (i.e., diversity in shared information) and information elaboration (i.e., exchange and integration of information). Ultimately, teams assisted by chatbots early on performed better. These results imply that introducing AI at the beginning of the process can enhance team decision-making by promoting effective information sharing among team members. 

As AI increasingly assists teams in decision-making, the study examines how technology shapes team processes and performance. We conducted an online experiment of team decision-making assisted by chatbots and analyzed team interaction processes with computational methods. We found that teams assisted by a chatbot offering information in the first half of their decision-making process performed better than those assisted by the chatbot in the second half. The effect was explained by the variation in teams’ information-sharing process between the two chatbot conditions. When assisted by the chatbot in the first half of the decision-making task, teams showed higher levels of cognitive diversity (i.e., the difference in the information they shared) and information elaboration (i.e., exchange and integration of information). The findings demonstrate that if introduced early, AI can support team decision-making by acting as a catalyst to promote team information sharing. 

Whereas artificial intelligence (AI) is increasingly used to facilitate team decision-making, little is known about how the timing of AI assistance may impact team performance. The study investigates this question with an online experiment in which teams completed a new product development task with assistance from a chatbot. Information needed for making the decision was distributed among the team members. The chatbot shared information critical to the decision in either the first half or second half of team interaction. The results suggest that teams assisted by the chatbot in the first half of the decision-making task made better decisions than those assisted by the chatbot in the second half. Analysis of team member perceptions and interaction processes suggests that having a chatbot at the beginning of team interaction may have generated a ripple effect in the team that promoted information sharing among team members. 

Abstract People may experience emotions before interacting with automated agents to seek information and support. However, existing literature has not well examined how human emotional states affect their interaction experience with agents or how automated agents should react to emotions. This study proposes to test how participants perceive an empathetic agent (chatbot) vs. a non-empathetic one under various emotional states (i.e., positive, neutral, negative) when the chatbot mediates the initial screening process for student advising. Participants are prompted to recall a previous emotional experience and have text-based conversations with the chatbot. The study confirms the importance of presenting empathetic cues in the design of automated agents to support human-agent collaboration. Participants who recall a positive experience are more sensitive to the chatbot’s empathetic behavior. The empathetic behavior of the chatbot improves participants’ satisfaction and makes those who recall a neutral experience feel more positive during the interaction. The results reveal that participants’ emotional states are likely to influence their tendency to self-disclose, interaction experience, and perception of the chatbot’s empathetic behavior. The study also highlights the increasing need for emotional acknowledgment of people who experience positive emotions so that design efforts need to be designated according to people’s dynamic emotional states.