Conference Agenda (All times are shown in Greenwich Mean Time (GMT) unless otherwise noted)
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
Search Systems and Artificial Intelligence: Enhancing Searching as Learning Approaches to Counter Misinformation
Time:
Sunday, 29/Oct/2023:
4:00pm - 5:30pm
Location:Bordeaux Suite, 2nd Floor, Novotel
Presentations
ID: 334 / [Single Presentation of ID 334]: 1 Panels 90 minutes Confirmation 1: I/we acknowledge that all session authors/presenters have read and agreed to the ASIS&T Annual Meeting Policies Topics: Information Behavior (information behavior; information-seeking behavior; information needs and use; information practices; usability; user experience; human-computer interaction; human-technology interaction; human-AI interaction) Keywords: searching as learning, information seeking, AI, misinformation, disinformation
Search Systems and Artificial Intelligence: Enhancing Searching as Learning Approaches to Counter Misinformation
Souvick Ghosh1, Jacek Gwizdka2, Dirk Lewandowski3, Rebecca Reynolds4, Soo Young Rieh2, Tamara Heck5, Aylin Imeri6
1San Jose State University, USA; 2The University of Texas at Austin, USA; 3Hamburg University of Applied Sciences, Germany; 4Rutgers, the State University of New Jersey, USA; 5DIPF | Leibniz Institute for Research and Information in Education, Germany; 6Heinrich Heine University Düsseldorf, Germany
Searching as a learning process implies that learning occurs during a search process and might happens incidentally, influenced by the context the search takes place and the system that is used. Searching and learning are not isolated but co-occurring events. Research investigates how search systems can be improved to foster learning processes, integrate information literacy enhancing methods and support user’s sense-making of information. Regarding the advancement of AI algorithms and their implementation in search systems, the concept of searching as a learning process can help to better understand human-computer interactions and future information-seeking processes. The panel advances current research on search systems for learning in non-formal settings, with a focus on investigating the relation between searching and learning processes that influence people’s understanding, assessing and use of information. It will focus on the contributions of information science research and the expectations of future searching behavior with respect to emerging advances in AI.
