E42 Teachers’ Perceptions of AI-Enhanced Data-Driven Decision-Making (DDDM) in Schools: A Systematic Review Table 1. Summary of Findings on Teachers' Perceptions of AI-Enhanced DDDM Question Category Number of Studies Example References How did the studies conceptualize teachers' perceptions of AI-enhanced DDDM? Positive perceptions 15 Cheah & Kim, 2025 Negative perceptions and skepticism 12 Kim, 2024 What barriers were identified across the studies? Insufficient training and data literacy 11 Nazaretsky et al., 2023 Technological and infrastructural constraints 9 Alsudairy & Eltantawy, 2024 Workload concerns 6 Liu et al., 2023 Trust and accuracy issues 7 Kim, 2024 What enablers and facilitators supported AI adoption? Professional development and training 14 Cheah & Kim, 2025 Institutional support and resources 10 Alsudairy & Eltantawy, 2024 Curricular and pedagogical alignment 8 Alsudairy & Eltantawy, 2024 Transparency and explainability 6 Cheah & Kim, 2025 What ethical and emotional considerations emerged? Data privacy and security 10 Nazaretsky et al., 2023 Algorithmic bias and fairness 9 Nazaretsky et al., 2023 Emotional ambivalence 7 Kim, 2024 What research designs and contexts characterized these studies? Quantitative survey studies 11 Alsudairy & Eltantawy, 2024 Qualitative case studies 7 Kim, 2024 Mixed-methods studies 7 Cheah & Kim, 2025 Note to Table. 1. Numbers indicate how many of the 25 reviewed studies reported in each category. Example references are illustrative and not exhaustive.
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