Rapid developments in educational technology awaken hopes for making science education more engaging and effective for learners. Cognitive Load Theory stresses limitations of human cognitive architecture and urges developers to design learning tools that help learners optimize their mental capacities. In a 1.5-month study we investigated university biology students’ use of an AI-enriched digital biology book comprising of a 5000-concept knowledge base and algorithms that offer the possibility to ask questions and receive answers. Our aim was to identify and investigate differences between three types of cognitive load (CL), namely, intrinsic (ICL), germane (GCL) and extraneous (ECL), as well as their correlation with usability perception and learning gain. Findings show that non-optimal design (increase in ECL), which draws learners’ cognitive resources from the task is linked with a lower learning gain and a lower usability perception (e.g. satisfaction). The results contribute to research on differentiating three types of cognitive load and their relationship with usability and learning biology from digital tools. The findings also emphasize the importance of optimally designing emerging educational technologies, especially in the context of complex science topics.