The integration of Blockchain Technology (BCT) and Deep Learning (DL) has emerged as a transformative approach to addressing the challenges of data security, transparency, and personalization in smart learning environments (SLEs). Blockchain provides decentralized, tamper-proof storage of learner records and ensures data authenticity, while deep learning offers powerful predictive analytics for personalized and adaptive education. This paper presents a literature review on blockchain-enabled deep learning frameworks in education, examining recent advances, architectural models, and practical implementations. The review highlights how existing studies have applied blockchain for credential verification, secure content sharing, and distributed trust management, while deep learning techniques have been employed for student performance prediction, intelligent tutoring, and adaptive feedback. Despite these advancements, significant research gaps remain, particularly in the areas of real-time deployment, scalability, latency, interoperability across institutions, and privacy-preserving learning analytics. By systematically analyzing current trends, this review identifies open challenges and future directions for developing efficient, secure, and scalable blockchain.DL frameworks tailored to smart learning environments.
M et al. (Thu,) studied this question.