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Rapid developments in educational technology in higher education are intended to make learning more engaging and effective. At the same time, cognitive load theory stresses limitations of human cognitive architecture and urges educational developers to design learning tools that optimise learners' mental capacities. In a 2-month study we investigated university students' learning with an AI-enriched digital biology textbook that integrates a 5000-concept knowledge base and algorithms offering the possibility to ask questions and receive answers. The study aimed to shed more light on differences between three sub-types (intrinsic, germane and extraneous) of cognitive load and their relationship with learning gain, self-regulated learning and usability perception while students interacted with the AI-enriched book during an introductory biology course. We found that students displayed a beneficial learning pattern with germane cognitive load significantly higher than both intrinsic and extraneous loads showing that they were engaged in meaningful learning throughout the study. A significant correlation between germane load and accessing linked suggested questions available in the AI-book indicates that the book may support deep learning. Additionally, results showed that perceived non-optimal design, which deflects cognitive resources away from meaningful processing accompanied lower learning gains. Nevertheless, students reported substantially more favourable than unfavourable opinions of the AI-book. The findings provide new approaches for investigating cognitive load types in relation to learning with emerging digital tools in higher education. The findings also highlight the importance of optimally aligning educational technologies and human cognitive architecture.

In the past ten years, increasing evidence has demonstrated that scientific teaching and active learning improve student retention and learning gains in the sciences. Graduate teaching assistants (GTAs), who play an important role in undergraduate education at many universities, require training in these methods to encourage implementation, long-term adoption, and advocacy. Here, we describe the design and evaluation of a two-day training workshop for first-year GTAs in the life sciences. This workshop combines instruction in current research and theory supporting teaching science through active learning as well as opportunities for participants to practice teaching and receive feedback from peers and mentors. Postworkshop assessments indicated that GTA participants' knowledge of key topics increased during the workshop. In follow-up evaluations, participants reported that the workshop helped them prepare for teaching. This workshop design can easily be adapted to a wide range of science disciplines. Overall, the workshop prepares graduate students to engage, include, and support undergraduates from a variety of backgrounds when teaching in the sciences.