Enhancing Searching as Learning (SAL) with Generative Artificial Intelligence: A Literature Review

Searching as Learning (SAL), a learning process with potential knowledge gain during searches in a digital environment, is an emerging field in human-computer interaction research, especially with recent technological advancements in generative artificial intelligence (GenAI). According to SAL, the act of searching for the information itself can be a valuable learning experience. Many studies have investigated SAL’s learning perspective and facets supported by traditional search systems (e.g., web browsers), to access, search, and retrieve information to fulfill users’ learning intentions. However, the applications of GenAI, as well as their roles and disruption to the existing SAL process, are unclear. To address this gap, this study aims to shed light on the applicability of GenAI in enhancing the SAL process by conducting a systematic literature review. First, we seek to define the concepts of ‘learning’ and ‘searching’ by examining the components of SAL in the literature and then detailing how SAL would have occurred. Next, the systematic literature review, guided by PRISMA, uses the PICO (Population, Intervention, Comparison, and Outcome) framework to develop searchable keywords and guide the literature review. Five major databases were searched, and literature that fulfilled the PICO’s criteria was included for review. Preliminary analysis shows that GenAI could improve and ease SAL human-computer interfaces that inevitably change the process and influence users’ learning behavior, such as how information is retrieved and consumed. Consequently, these opportunities posed concerns about information reliability, accuracy, and long-term effects on user behavior.