Speaker
Description
Alhassan, N.
Federal University of Education, Zaria
muhalbg@gmail.com
Track: Digital Pedagogy and Teaching Innovation
Traditional biology higher education relies heavily on passive lecture formats and resource-intensive physical laboratories, creating significant access bottlenecks and variable learning outcomes. This study investigates the pedagogical shift toward interactive, artificial intelligence (AI)-enhanced virtual biology simulations to bridge these gaps. Utilizing adaptive learning algorithms, generative AI tutoring assistants, and real-time predictive feedback, the proposed framework transforms abstract conceptual biology such as dynamic cellular pathways, metabolic regulation, and genetic recombination into immersive, self-paced laboratory environments. A mixed-methods experimental evaluation was conducted across undergraduate biology cohorts to measure conceptual retention, problem-solving speed, and self-reported student engagement. Quantitative results demonstrate a statistically significant enhancement in critical analytical skills and concept mastery among students interacting with AI-guided simulations compared to those in traditional lecture settings. Furthermore, the virtual lab architecture drastically mitigates infrastructure constraints, providing a scalable, low-bandwidth, and equitable solution for resource-limited educational institutions. By combining intelligent tutoring systems with experiential learning design, this paper provides a robust framework for higher education institutions seeking to modernize STEM curricula, enhance scientific literacy, and foster future-ready biological learning ecosystems aligned with global digital transformation initiatives.