Abstract:
Artificial intelligence (AI) is rapidly revolutionizing the higher education sector
and transforming how students learn, understand, and interact with information
via AI-based learning tools. As there are no previous studies comparing
AI-based and traditional learning within the 3Rs framework, a research gap is
evident. Therefore, the current exploratory study investigates the effects of AIbased
learning applications on students’ information processing skills and draws
special attention to the 3Rs: review, retention, and retrieval. Six hundred fifty
(650) undergraduates were purposefully divided into two groups. All students
were provided an information-search assignment, for which they had to collect,
analyze, and use information on course-specific topics covered during the first
three months of the semester. One group was instructed to use AI-based learning
tools, while the other group was not. One week was given to each group
to complete the task. Immediately after completion, 121 students in each group
(242 total) were randomly selected using the Morgan table. The selected two
groups responded to a self-reported questionnaire, which measured their information
review skills, ability to store information in long-term memory, and capacity
to retrieve relevant knowledge. A comparison of retrieval fluency, retention,
and review skills was conducted through quantitative analysis. Statistical differences
between observational groups were found. Students who practiced AI-based
learning had greater retrieval fluency, while those who followed regular studies
had much stronger information review and long-term retention. These results
indicate that although AI-based tools can assist in quick access to information,
traditional learning methods are proven to be better for deep cognitive processing
and long-term memory encoding. The study highlights the necessity of a blended
and pedagogically oriented combination between AI-based and traditional learning
technologies in higher education.
