From Algorithmic Feedback to Inclusive Learning: Exploring Teacher Readiness and AI-Supported Formative Assessment for Diverse Learners

Abstract

Artificial intelligence (AI) is reshaping formative assessment by enabling timely, adaptive, and personalized feedback, yet its contribution to inclusive learning depends substantially on teachers’ readiness and the institutional conditions supporting implementation. This study examined the relationships among teacher readiness, AI-supported formative assessment practices, personalized feedback, and support for diverse learners in inclusive classrooms in Punjab, Pakistan. Using a cross-sectional survey design, data were collected from 481 teachers through a 50-item questionnaire and analyzed using descriptive statistics, independent-samples t-tests, ANOVA, correlation, exploratory factor analysis, and multiple regression. Findings indicated relatively high levels of teacher readiness and AI implementation, whereas institutional and technical support remained comparatively weak. Significant differences in AI related practices were observed across school location, professional training, and levels of AI use. Correlation and regression analyses further demonstrated that personalized feedback was a significant predictor of teachers’ reported support for diverse learners. The findings suggest that AI adoption alone does not ensure inclusive assessment; rather, its educational value depends on teachers’ capacity to translate AI generated insights into responsive pedagogical decisions. The study contributes context-sensitive evidence for developing teacher professional learning, institutional infrastructure, ethical governance, and human oversight mechanisms that position AI as a complement to not a substitute for teacher judgment in inclusive education.