Shahaf Rocker Yoel, Ayelet Becher E45 Figure 3. Distribution of the 25 included studies by year of publication. Cross-analysis revealed contextual influences on teachers' perceptions. In highly regulated systems (e.g., the U.S., China), ethical concerns about privacy and fairness predominated (Liu et al., 2023; Nazaretsky et al., 2023). In contrast, studies from developing regions emphasized AI's pedagogical promise for inclusion and resource optimization (Alsudairy & Eltantawy, 2024). Secondary teachers often cited workload and autonomy concerns, while primary and special education teachers emphasized training needs. These patterns suggest that geography, educational level, and discipline collectively shape how teachers interpret AI's role in DDDM. Discussion This review examined current research on teachers' perceptions of AI-enhanced DDDM in schools. Teachers' perceptions are central to determining whether data-informed innovation leads to meaningful pedagogical change, as their trust, agency, and ethical reasoning mediate AI integration into professional practice. Teachers' perceptions show duality: enthusiasm for AI's benefits and skepticism about trust and autonomy. This ambivalence reflects broader patterns in educational innovation, where technological optimism coexists with professional caution (Ertmer & OttenbreitLeftwich, 2010; Mandinach & Schildkamp, 2021). Within DDDM, the key tension lies in how AI redistributes authority between human judgment and algorithmic insight. Perceptions of usefulness and reliability vary by tool. Learning analytics dashboards are informative yet overwhelming (Schildkamp et al., 2017). Intelligent Tutoring Systems are valued for personalization but criticized for opacity (Holmes et al., 2022). Generative AI evokes both enthusiasm and concern about bias and accuracy (Luckin, 2023). Reliability depends less on technical performance and more on teachers' data literacy, institutional support, and confidence in interpreting algorithmic outputs. Persistent barriers, including limited training, workload, and infrastructure, echo familiar challenges in DDDM. Yet, meaningful change depends on whether schools frame AI not as a technical skill but as part of a broader culture of inquiry. Professional learning communities and leadership support can build ethical awareness and organizational capacity for AI-informed decision-making (Ertmer, 1999; Mandinach & Gummer, 2016). Ethical and emotional dimensions remain central to teachers' acceptance of AI (Holmes et al., 2022). Concerns about privacy, fairness, and bias are intertwined with feelings of excitement and anxiety. Many teachers advocate "intelligence augmentation" rather than automation, emphasizing the importance of keeping human expertise and trust at the core of AI-enhanced DDDM (Luckin, 2018; Zawacki-Richter et al., 2019).
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