Impact of AI-Blended Learning and AI-Personalized Learning on Undergraduate Biology Students' Attitude and Performance in Climate Change Education

Abstract

This study employed a quasi-experimental design to investigate the impact of AI-Blended Learning and AI-Personalized Learning on undergraduate biology students' attitudes and performance in climate change education. The research addressed two research questions and tests two corresponding null hypotheses. The population consists of 300 level undergraduate biology students at Federal University Gusau, with a sample size of 70 students selected through random sampling. Participants were divided into three groups; AI-Blended Learning (20 students), AI-Personalized Learning (20 students), and Traditional Classroom Instruction (30 students). The intervention lasted four weeks. AI-Blended Learning group used ChatGPT-3.5 alongside traditional classroom instruction, while the AI-Personalized Learning group solely relied on ChatGPT-3.5 for their instruction. Data were collected using two instruments; the Climate Change Attitude Assessment (CCAA) and the Climate Change Achievement Test (CCAT). Both CCAA and CCAT were validated by experts and have reliability coefficients of 0.84 and 0.82, respectively. Data collected were analyzed using mean and standard deviation for the research questions, and Analysis of Covariance (ANCOVA) tests for the null hypotheses at a significance level of 0.05. Findings revealed that AI-Blended Learning significantly improved students' attitudes and performance compared to AI-Personalized Learning and Traditional Classroom Instruction. It is recommended that, lecturers should adopt AI-Blended Learning with ChatGPT-3.5 to improve student engagement and learning outcomes in environmental education.