Author
Listed:
- Bin Hu
(Department of Information Science and Engineering, Changsha Normal University, China)
- Ifrah Malik
(International Islamic University, Islamabad, Pakistan)
- Qiyang Chen
(Department of Information Management & Business Analytics, Feliciano School of Business, Montclair State University, USA)
- Hong Xie
(Guangzhou University, China)
- Noman Sohail
(Linkoping University, Sweden)
- Razaz Waheeb Attar
(Management Department, College of Business Administration, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia)
Abstract
This study examined how digital literacy influences user interactions with artificial intelligence–driven semantic search engines compared with traditional keyword-based search systems. The authors assessed whether an artificial intelligence–driven search enhances efficiency, query quality, and user satisfaction across varying digital literacy levels, in particular complex information retrieval tasks. Sixty participants, categorized into three digital literacy groups (beginner, intermediate, and advanced) on the basis of the European Commission's Digital Competence Framework, completed six search tasks (three simple, three complex) using both traditional and artificial intelligence–driven search engines. Performance was measured by task completion time, query quality, and user satisfaction. Statistical analyses (analysis of variance, paired t tests) were conducted to compare outcomes across literacy levels and search engine types. Post-task interviews provided qualitative insights into user experiences.
Suggested Citation
Bin Hu & Ifrah Malik & Qiyang Chen & Hong Xie & Noman Sohail & Razaz Waheeb Attar, 2025.
"Bridging Digital Literacy Gaps With AI-Driven Semantic Search Technologies,"
International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 21(1), pages 1-17, January.
Handle:
RePEc:igg:jswis0:v:21:y:2025:i:1:p:1-17
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