Conference Agenda (All times are shown in Mountain Daylight Time (MDT) unless otherwise noted)

Disruptive technologies such as ChatGPT and similar Large Language Models (LLMs) have transformed mundane everyday tasks of information users since their debut in late 2022. In this study, we leverage uses and gratifications theory to test a distinct set of motivations that drive users’ satisfaction and continued use intentions of ChatGPT and similar large language models. Data were collected using a national online survey of 323 adults residing in the United States. We conducted data analysis using Partial Least Squares (PLS-SEM) to investigate both direct and indirect impact of factors on users' gratification, thereby influencing the continued utilization of these tools for everyday information seeking. Results show four motivational factors - social influence, trust, personalization, and perceived usefulness - that positively influence users' satisfaction or sense of gratification, impacting their intentions to continue using these tools. This is one of the few early studies of ChatGPT and other LLMs from an information science perspective.

The proliferation of Artificial Intelligence Generated Content (AIGC) poses significant challenges to user experience and information accuracy, especially on search engine websites. The current solution is to identify AIGC by machine learning algorithms or publicly available AI detection tools, whereas, machine learning algorithms degrade in accuracy as more data is available and tools such as GPTZero perform poorly in the task of AIGC detection on social media. In this paper, we propose an EPCNN approach to identify AIGCs in search engine websites, which maintains good performance in large-scale samples. The ERNIE model integrates cross-domain knowledge and improves language understanding and generalization. We use ERNIE to extract text features, then use a feature pyramid network to capture semantic information at different levels, and finally use an end-to-end structure to connect ERNIE and the feature pyramid network to construct the EPCNN. Experimental results show that our proposed algorithm has high accuracy and the ability to handle large-scale data compared with machine learning algorithms and AI detection tools.

This study conducted a systematic review of human-AI interaction (HAI)over the past decade for the implemented theories and scenarios, and the tested hypotheses to discover the changes in the current transition to human-centered AI (HCAI). Moving from acceptance theories, Computers are social actors (CASA), anthropomorphism, and the integrative trust model are the most frequent theories. Augmentation scenarios of decision-making, teamwork, and human-AI collaborations are common in the latest HAI studies. Users' trust, acceptance, and intention to use an AI system are the main research targets in HAI studies. These trends show a clear transition toward HCAI. This paper also discusses opportunities tied to HAI studies based on the interconnections between the various theories, scenarios, and hypotheses.

The development of generative AI and Large Visual-Language Models, has the potential to overcome the reliance of blind or visually impaired (BVI) persons on human sources for visual information. Action research and semi-structured interviews were conducted with 19 BVI persons to explore their visual information needs and source selection. This study categorizes their needs into description, navigation, and manipulation. Accessibility, credibility, and interactivity of information sources are the primary criteria for BVI persons in information source selection. When visual task demand high accurate information, BVI persons are more likely to select human information sources due to their accessibility and credibility. When tasks are less urgent and requiring less accuracy information, the use of AI information sources increases due to the special accessibility. The purpose of the study is to understand the impact of AI technology on BVI persons, and provide theoretical and practical insights for the development of human-centered AI.