Title: Ai Driven Academic Certificate Verification System
Authors: Imani Ntakimazi Kabelele, Kharoun Khalfan Hhalla, Yohana Mwamasangula, Joseph Ronjino Mkakilwa, Irene Rayman Mlangali, Ibrahim Fredy Mwandemani, Halid Salum Athumani
Volume: 10
Issue: 5
Pages: 193-199
Publication Date: 2026/05/28
Abstract:
This review paper critically examines the application of Artificial Intelligence (AI) and complementary digital technologies in the development of an academic certificate verification system tailored to the Tanzanian educational context. Academic credential fraud remains a persistent challenge in developing economies, undermining institutional credibility, labor market integrity, and national development goals. This paper synthesizes existing research across four core technological domains: AI and machine learning-based fraud detection, deep learning for document image analysis, Optical Character Recognition (OCR) for automated data extraction, and blockchain technology for tamper-proof credential storage. Through systematic analysis, the study identifies critical gaps: fragmented technological approaches, absence of localized training datasets for Tanzanian certificate formats, limited explainability of AI decision models, and poor alignment with national regulatory frameworks such as those of the National Examinations Council of Tanzania (NECTA) and the Tanzania Commission for Universities (TCU). The paper proposes a unified, context-aware verification framework that integrates intelligent fraud detection, secure decentralized credential storage, real-time verification capabilities, and governance-aligned architecture. Strategic recommendations are provided for researchers, policymakers, and institutional developers to advance equitable, reliable, and sustainable academic credential authentication in Tanzania