From Satisfaction to AI Integration: Stakeholder Perceptions of Student Classification and Progress Monitoring in Qatar’s Schools

Abstract

This study examined stakeholders’ satisfaction with current student classification and progress monitoring systems and explored their perceptions of the potential role of artificial intelligence (AI) in enhancing these processes. A cross-sectional survey was administered to 313 stakeholders, including teachers, administrators, decision-makers, and educational service providers. Descriptive statistics, multiple regression analysis, and group comparisons were employed to examine satisfaction levels, predictors of satisfaction, and expectations regarding AI integration. Despite high satisfaction with the current systems (85%), nearly 80% of stakeholders rated AI integration as essential. The most frequently expected functions of an AI-enabled system were predicting student challenges (33.2%), generating detailed analyses and reports (32.9%), customizing individual learning plans (22.7%), and providing immediate feedback (11.2%). Anticipated challenges focused on acceptance and adaptation by teachers and students (40.9%) and concerns about privacy and system integration. Regression analysis revealed that perceptions of classification practices (β = 0.473, p < 0.001) were a stronger predictor of satisfaction than perceptions of progress monitoring practices (β = 0.315, p < 0.001). Demographic analyses revealed greater dissatisfaction among non-teaching staff, females, and mid-career professionals. The findings show that stakeholders are broadly satisfied with existing systems while simultaneously demanding AI-driven innovation, suggesting satisfaction reflects acceptance rather than alignment with stakeholders’ needs and expectations.