Exploring AI-Driven Personalized Learning Platforms for Adapting Biology Curricula Based on Student Performance - Yususf, Muhammad Hayatu; Musa, Dangana; Abdullahi, Aliyu; Dede, Adamu Murza (Faculty of Natural and Applied Sciences Journal of Computing and Applications, 2025)
The integration of Artificial Intelligence (AI) in education has significantly transformed instructional methodologies, particularly in science disciplines such as biology. This study examined the effectiveness of AI driven personalized learning systems and AI powered virtual laboratories in enhancing students' comprehension, engagement, and ability to conduct biological experiments. A quasi experimental research design was employed, involving 300 senior secondary school students, who were divided into experimental (AI based learning) and control (traditional instruction) groups. The experimental group utilized an AI driven adaptive learning platform for conceptual understanding and an AI powered virtual laboratory for hands on experiments, while the control group received conventional classroom instruction. Pretest and posttest assessments measured changes in student comprehension, engagement, and experimental skills. The results revealed that while both groups improved after the intervention, the experimental group exhibited significantly higher gains in comprehension (mean gain = 33.13 vs. 17.58) and experimental skills (mean gain = 38.47 vs. 22.64). Statistical analyses further confirmed the effectiveness of AI driven learning, with t test results (t = 10.76, p < 0.001 for comprehension; t = 12.45, p < 0.001 for experimental skills) demonstrating a significant difference between the groups. The effect sizes (Cohen’s d = 1.25 for comprehension and 1.44 for experimental skills) indicated a strong impact of AI based learning, while ANCOVA results supported these findings, showing that AI driven personalized learning explained 16.2% of the variance in comprehension (Partial Eta² = 0.162) and AI powered virtual laboratories accounted for 18.9% of the variance in experimental skills (Partial Eta² = 0.189). These findings highlight the transformative potential of AI in biology education, suggesting that AI based learning environments foster deeper conceptual understanding, sustained engagement, and improved experimental proficiency. The study recommends the institutional adoption of AI powered instructional systems, the integration of blended learning approaches combining AI and traditional instruction, and further research into the long term impact of AI driven education. The implications of this study extend to educational policymakers, curriculum designers, and educators seeking to leverage AI for enhanced learning outcomes in STEM education.