Cui, X., Qian, J., Xu, J., Li, S., & Shen, J.
Building digital textbooks using a crowdsourced model of professionals, students, and AI produces highly rated, deep learning resources.
Researchers designed and tested a crowdsourcing model for developing higher education digital textbooks across three subsystems: knowledge, process, and organization. The model leverages contributions from subject matter experts, everyday users, and artificial intelligence. Evaluation of the resulting textbook showed high user satisfaction across content quality, technical design, accessibility, and support for deep learning.
When developing digital learning materials, consider adopting a structured crowdsourcing approach that integrates expert oversight with learner and AI contributions. Utilize this collaborative framework to scale up content creation while maintaining quality through multi-layered feedback. Ensure your design process defines clear roles for AI co-creation and human verification to maximize resource usability.
AI image generators speed up design ideation but introduce gender stereotypes, highlighting the need for critical visual literacy.
Generative AI is rapidly reshaping higher education, forcing institutions to rethink teaching, learning, and institutional strategy.
Grouping learners based on complementary knowledge strengths, tracked dynamically, significantly improves collaborative learning outcomes.
Engaging graduate students in co-creating design-based research transforms them into active, networked practice-based scholars.
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