Abstract
This presentation introduces an innovative model for enhancing science and technology learning through the integration of digital biofeedback tools within classroom instruction. Grounded in clinical psychophysiology, the project investigates how real-time data from neurotechnology such as heart rate variability (HRV), skin conductance, and EEG-based attention metrics can inform personalized learning experiences in STEM subjects. The study was conducted in urban secondary schools using low-cost wearable devices linked to a teacher dashboard, allowing educators to visualize students’ physiological engagement and stress responses during biology and physics lessons. The intervention applied adaptive instruction methods, where teaching pace and delivery modes were modified based on collective biofeedback trends. Students were also trained to interpret their own physiological data, fostering digital literacy, emotional regulation, and metacognitive skills. Preliminary findings suggest improved concentration, higher content retention, and increased motivation among students exposed to neuro-informed instruction. This work pioneers a cross-disciplinary framework connecting neuroscience, digital technology, and science pedagogy, offering a transformative model for future-ready, equity-centered education.
Presenters
Yusra SaleemClinical Research Officer, Advance Educational Institute & Research Center, Sind (en), Pakistan
Details
Presentation Type
Theme
KEYWORDS
SCIENCE, TECHNOLOGY, NEUROTECHNOLOGY, BIOFEEDBACK, MODES OF INSTRUCTION, DIGITAL LITERACY
