Generative AI-Assisted Curriculum Design for Life Science Education: An Analysis of Conceptual Clarity, Cognitive Engagement, and Learning Achievement
Keywords:
Generative Artificial Intelligence, Curriculum Design, Life Science Education, Conceptual Clarity, Cognitive Engagement, Learning Achievement, Educational TechnologyAbstract
This study examines the use of teacher-supervised generative artificial intelligence (GenAI) to design curriculum by employing the GenAI in a reproducible dataset. The study uses a pretest/posttest control group design to evaluate a GenAI-assisted curriculum in life science. The sample consists of 140 students (70 in each group) and an 8-week instructional period. The indicators of effectiveness assessed the students’ understanding and included engagement and achievement. The analyses employ both qualitative and quantitative techniques. The techniques include but are not limited to: analysis of covariance (ANCOVA), paired and independent samples tests, effect size calculations, 95% confidence intervals, Pearson product-moment correlations, and multiple regressions. The control and experimental groups did not differ statistically on any of the baseline measures (all p > .27). The experimental group showed more improvement on all measures of engagement, clarity, and achievement (mean difference = 5.35, 4.08, and 5.96, respectively). After accounting for the students’ pre-test achievement, the effect of group membership on students’ achievement was statistically significant (p < .001). GenAI in teacher-mediated models of curriculum design can be effective in engaging students in scientific practices, for example, to create a graph or determine and justify a scientific conclusion.
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