BALANCING INNOVATION AND INTEGRITY: LEADERSHIP CHALLENGES IN AI-DRIVEN EDUCATION
DOI:
https://doi.org/10.63878/qrjs979Keywords:
Artificial Intelligence in Education; Technology Acceptance Model; Trust in AI; Risk Barriers; Usage Barriers; Adoption Intention; Educational Leadership; UK Education; Sequential Mediation; AI Ethics.Abstract
This study examined relationships among risk barriers, usage barriers, enhanced performance, trust prosperity towards AI, attitude towards AI technology, and adoption intention within UK educational contexts. A cross-sectional survey of 387 educators tested a sequential mediation model grounded in the Technology Acceptance Model. All seven hypotheses were supported. Risk barriers (β = -0.41) and usage barriers (β = -0.32) significantly reduced trust prosperity towards AI, which positively predicted attitude (β = 0.44). Attitude was the strongest predictor of adoption intention (β = 0.48), followed by enhanced performance (β = 0.29). The model explained 51% of variance in adoption intention, with sequential mediation confirming significant indirect effects through the trust→attitude pathway. Descriptive statistics revealed high barrier perceptions (risk: M = 5.24/7; usage: M = 5.07/7), with only 37% receiving AI training. Findings demonstrate that successful AI adoption depends critically on trust and institutional capacity to mitigate barriers, not merely performance gains. For educational leaders, balancing innovation with integrity requires proactive investment in transparency, training, and trust-building.
