EXPLORING QUANTUM ARTIFICIAL INTELLIGENCE FOR TRANSFORMING TEACHING AND LEARNING: THEORETICAL AND PEDAGOGICAL PERSPECTIVES

Abstract

Quantum Artificial Intelligence (QAI), an emerging field that integrates quantum computing and AI, has the potential to revolutionize education by enabling ultra-fast data processing, adaptive learning, and intelligent decision-making in pedagogical environments. This quantitative study investigates the relationship between educators’ readiness for QAI, perceived usefulness of QAI tools, and their impact on teaching and learning transformation. Using a cross-sectional survey design, data were collected from 180 teachers across higher education institutions implementing AI and advanced computational technologies. Pearson correlation and multiple regression analyses revealed that educators’ readiness for QAI (β = .45, p < .001) and perceived usefulness (β = .38, p < .001) significantly predict the extent of pedagogical transformation. The findings suggest that both attitudinal and cognitive factors are critical for integrating QAI into teaching and learning. Recommendations include targeted professional development, infrastructure support, and fostering a culture of innovation to maximize QAI’s pedagogical potential.