Plus Ultra Paradigm: A Comparative Analysis of AI-Integrated Feynman and Leitner Active Recall Methods on Cavite State University - CCAT Laboratory Science High School Grade 7 Students' Conceptual Understanding of Cellular Biology

This study examined the effectiveness of AI-integrated active recall strategies, specifically the Feynman method and the Leitner system, in improving Grade 7 students’ conceptual understanding of cellular biology. A within-subjects design was employed involving 30 Grade 7 students from Cavite State University–CCAT Laboratory Science High School. Prior to the introduction of the AI-integrated learning strategies, a pre-test was administered to determine baseline understanding, yielding a mean score of 24.67 (SD = 2.397). Students were then exposed to AI-integrated Feynman and Leitner learning activities using a digital learning platform (NotebookLM) before taking the post-tests. After the Feynman intervention, post-test scores increased to a mean of 26.30 (SD = 2.548), and this improvement was statistically significant, t(29) = −2.601, p = .014. In contrast, following the Leitner intervention, students obtained a mean post-test score of 23.93 (SD = 1.780), which did not differ significantly from the pre-test results, t(29) = 1.352, p = .187. Survey results further indicated that students preferred the AI-integrated Feynman method for understanding cellular biology concepts. Overall, the findings suggest that explanation-based learning supported by AI feedback is more effective than spaced repetition in promoting short-term conceptual understanding and academic performance in complex biological topics.