Published April 16, 2026
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Integrating Deep Learning Into School Curriculum: Challenges, Strategies, and Future Directions
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Universitas Islam Negeri Syekh Ali Hasan Ahmad Addary Padangsidimpuan
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The integration of deep learning (DL) into school curricula offers significant opportunities to enhance educational quality through personalized learning, automated assessment, and data-driven decision-making. This study identifies four primary implementation challenges: pedagogical misalignment between traditional teaching methods and DL's analytical approach (32% of studies), infrastructure limitations including hardware and internet access (45%), ethical issues concerning student data privacy and algorithmic bias (28%), and inadequate teacher readiness (40%). Through a systematic literature review of 45 peer-reviewed articles from Q1 journals published between 2018–2023, this research proposes strategic solutions including project-based curriculum design, cloud computing adoption, comprehensive teacher training programs, and a robust ethical framework. Comparative case studies from Germany, the Netherlands, the United States, Singapore, Finland, and Indonesia demonstrate that successful DL integration requires policy support, technological accessibility, and pedagogical adaptation. The research recommends a phased implementation model tailored to local contexts, emphasizing multi-stakeholder collaboration among educators, policymakers, and technology providers. These findings contribute to developing inclusive and sustainable AI education policies that address technological and human resource challenges across diverse educational settings.
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2026-04-16The integration of deep learning (DL) into school curricula offers significant opportunities to enhance educational quality through personalized learning, automated assessment, and data-driven decision-making. This study identifies four primary implementation challenges: pedagogical misalignment between traditional teaching methods and DL's analytical approach (32% of studies), infrastructure limitations including hardware and internet access (45%), ethical issues concerning student data privacy and algorithmic bias (28%), and inadequate teacher readiness (40%). Through a systematic literature review of 45 peer-reviewed articles from Q1 journals published between 2018–2023, this research proposes strategic solutions including project-based curriculum design, cloud computing adoption, comprehensive teacher training programs, and a robust ethical framework. Comparative case studies from Germany, the Netherlands, the United States, Singapore, Finland, and Indonesia demonstrate that successful DL integration requires policy support, technological accessibility, and pedagogical adaptation. The research recommends a phased implementation model tailored to local contexts, emphasizing multi-stakeholder collaboration among educators, policymakers, and technology providers. These findings contribute to developing inclusive and sustainable AI education policies that address technological and human resource challenges across diverse educational settings.
References
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