Shahaf Rocker Yoel, Ayelet Becher E43 Teachers' Perceptions of AI-Enhanced DDDM Across the corpus, teachers' perceptions of AI-enhanced DDDM were diverse, ranging from enthusiasm to skepticism. Positive views, reported in 15 of the 25 studies, emphasized AI's contribution to instructional efficiency, personalization, and inclusive pedagogy. Teachers valued AI tools that automated assessment, offered predictive insights, or supported differentiated instruction aligned with curricular goals (Alsudairy & Eltantawy, 2024; Cheah & Kim, 2025). STEM and special education teachers described AI as a resource for creativity and individualized support. Across the studies, the most frequently used tools included learning analytics dashboards, intelligent tutoring systems, and generative AI applications such as ChatGPT, each offering distinct benefits and challenges in supporting data-informed teaching (Cheah & Kim, 2025; Nazaretsky et al., 2023). Conversely, 12 studies documented negative or cautious perceptions. Teachers voiced concerns about the opacity of algorithmic recommendations, potential bias, and the erosion of professional autonomy (Kim, 2024; Liu et al., 2023). Several participants described AI-generated advice as conflicting with their professional judgment, leading to partial or selective adoption. Teachers' trust in the usefulness and reliability of AI tools varied across contexts. Dashboards and tutoring systems were praised for visualizing student progress, but also criticized for data overload and limited transparency. Generative AI tools, such as ChatGPT, added new layers of ambivalence: teachers valued their creative support yet questioned their accuracy and ethics. Overall, perceptions were shaped by the degree to which AI aligned with teachers' professional values and school environments. Barriers and Enablers of Adoption Eighteen studies reported systemic barriers to implementing AI-enhanced DDDM. The most frequent included insufficient training and data literacy (11 studies), technological and infrastructural limitations (9), increased workload (6), and distrust in AI accuracy (7). Teachers frequently described feeling unprepared to interpret AI-generated data or to integrate insights meaningfully into classroom practice (Nazaretsky et al., 2023). At the same time, 20 studies identified enablers that fostered adoption. Professional development was the most influential facilitator, with teachers gaining confidence through mentoring, workshops, and collaborative learning communities (Cheah & Kim, 2025). Institutional support, such as dedicated technical assistance, leadership vision, and structured time for experimentation, also emerged as critical, particularly when school leaders aligned AI initiatives with curricular and assessment priorities (Alsudairy & Eltantawy, 2024). When AI systems were clearly connected to curricular goals and assessment needs, teachers demonstrated higher levels of engagement. Explainable AI models that clarified how recommendations were produced significantly improved trust and sustained use (Nazaretsky et al., 2023). Ethical and Emotional Considerations Ethical and emotional dimensions appeared in nearly all studies, with 22 explicitly addressing these themes. The most prevalent issues concerned data privacy and security (10 studies), followed by algorithmic bias and fairness (9), and emotional ambivalence (7). Teachers worried about the misuse of student information and the lack of clear regulatory frameworks, particularly in developing regions (Nazaretsky et al., 2023). Concerns about algorithmic bias were pronounced in STEM-related contexts, where teachers questioned the neutrality of predictive models (Liu et al., 2023). Emotionally, teachers expressed both excitement and apprehension. Many viewed AI as an innovative tool with transformative potential but simultaneously feared loss of control or professional identity (Kim, 2024). This duality highlights that adoption is not merely technical but deeply tied to educators' sense of agency, ethics, and trust.
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