Towards Personalized Education in Life Sciences: Tailoring Instruction to Students’ Prior Knowledge and Interest Through Machine Learning

Undergraduate life science education faces high attrition rates, especially among students from underrepresented groups. These disparities are often linked to differences in prior knowledge, self-efficacy, and interest, which are rarely addressed in traditional lecture-based instruction. This work explores the use of machine learning-based Intelligent Tutoring Systems (ITSs) to support personalized instruction in biology education by examining stochasticity in molecular systems. Accordingly, we developed and validated a Random Forest classification model and used it to assign instructional materials based on students’ prior knowledge and interests. We then applied the model in an introductory biology classroom and individually estimated the most promising instructional format. Results show that the most effective instruction can be reliably predicted from student performance and interest profiles, and model-based assignments may help reduce pre-existing opportunity gaps. Thus, machine-learning-driven instruction holds promise for enhancing equity in life science education by aligning materials with students’ needs, potentially reducing differences in achievement, self-efficacy, and cognitive load, which might be relevant to promoting underrepresented students. To facilitate a straightforward implementation for educators facing similar challenges associated with teaching molecular stochasticity, we developed an open-access ITS tool and provided a scalable approach for developing similar personalized learning tools.