ARTIFICIAL INTELLIGENCE IN SCIENCE CLASSROOMS: BRIDGING RESEARCH PROMISE AND CLASSROOM PRACTICE

Abstract

Artificial Intelligence (AI) is increasingly heralded as a catalyst for innovation in 
science classrooms. Research highlights its promise to personalize learning, support 
intelligent tutoring systems, automate assessment, enrich virtual laboratory 
experiences, and provide actionable insights for instructional decision-making. 
These advances suggest opportunities for deeper conceptual understanding, 
differentiated pathways, and heightened student engagement. However, classroom 
practice often falls short of this vision. Implementation remains fragmented, 
hindered by limited teacher preparedness, inadequate infrastructure, rigid curricular 
frameworks, and unresolved ethical concerns surrounding data privacy and 
algorithmic bias. This paper critically examines the widening divide between 
research promise and classroom practice in science education, focusing on the 
structural, pedagogical, and policy barriers that impede effective adoption. It further 
explores strategic pathways for sustainable integration, including targeted 
professional development, curriculum alignment, institutional support, and robust 
ethical governance. By situating AI within the daily methods and strategies 
employed by teachers, the paper underscores the need for systemic collaboration to 
translate research promise into meaningful classroom practice.