Analyzing Differences in Student Understanding and Retention When Taught by AI Versus Human Instructors

Abstract

This research examines the comparative effect of AI-based teaching and human-taught teaching on student learning and retention among university teachers. A quantitative research design was employed in collecting data from a sample population of 220 university teachers, with emphasis on the analysis of the correlation between varied modes of teaching and student learning outcomes. The correlation analysis results showed that AI-based and human-directed instruction had strong positive effects on understanding in students with slightly more influence in human-directed instruction. Multiple regression analysis also supported both human-directed and AI-directed teaching as predictors of student learning with greater effect in human-directed teaching. ANOVA analysis also showed a statistically significant difference between the two teaching modalities, emphasizing the respective strengths of each method. The research concluded that although AI-driven teaching is useful in providing personalized feedback and adapting to the learning pace of a student, human teaching dominates, particularly in the facilitation of long-term retention through emotional connection, involvement, and utilization of real-life contexts. It appears that a combination of human and AI teaching would be the best way to teach, both in terms of one-on-one learning and emotional connection. The demographic study indicated a varying level of awareness of AI tools in teaching among the respondents that reflects the increasing trend of adopting AI in teaching practice. The findings add to the ongoing debate regarding the efficacy of AI in teaching and its ability to supplement conventional teaching procedures for improving students' learning outcomes.