The art and science of Quranic recitation (Tajweed), a discipline governed by meticulous phonetic, rhythmic, and theological principles, confronts substantial educational challenges in today's digital age. Although modern technology offers unparalleled opportunities for learning, existing automated systems for evaluating recitation have struggled to gain broad acceptance or demonstrate educational effectiveness. This literature review examines this crucial disparity, offering a thorough analysis of scholarly research, digital platforms, and commercial tools developed over the past twenty years. Our analysis uncovers a fundamental flaw in current approaches that adapt Automatic Speech Recognition (ASR) systems, which emphasize word identification over qualitative acoustic evaluation. These systems suffer from limitations such as reliance on biased datasets, demographic disparities, and an inability to deliver meaningful feedback for improvement. Challenging these data-centric methodologies, we advocate for a paradigm shift toward a knowledge-based computational framework. By leveraging the unchanging nature of the Quranic text and the well-defined rules of Tajweed, we propose that an effective evaluation system should be built upon rule-based acoustic modeling centered on canonical pronunciation principles and articulation points (Makhraj), rather than depending on statistical patterns derived from flawed or biased data. The review concludes that the future of automated Quranic recitation assessment lies in hybrid systems that combine linguistic expertise with advanced audio processing. Such an approach paves the way for developing reliable, fair, and pedagogically effective tools that can authentically assist learners across the globe.

翻译：古兰经诵读学（泰吉威德）作为一门受精密语音学、韵律学及神学原则规训的艺术与科学，在当今数字时代面临着重大的教育挑战。尽管现代技术提供了前所未有的学习机遇，现有的自动化诵读评估系统却难以获得广泛认可或展现教育有效性。本文献综述审视了这一关键差距，对过去二十年间发展的学术研究、数字平台及商业工具进行了系统分析。我们的分析揭示了当前基于自动语音识别（ASR）系统改良方法存在根本缺陷——这些方法侧重于词汇识别而非声学质量评估。现有系统存在多重局限：依赖有偏数据集、存在人口统计学差异、以及无法提供有效的改进反馈。针对这些以数据为中心的方法论，我们主张向知识驱动的计算框架进行范式转变。通过利用古兰经文本的恒定性及泰吉威德规则的明确性，我们提出有效的评估系统应建立在以规范发音原则和发音部位（麦克拉吉）为核心的规则化声学模型基础上，而非依赖从有缺陷或有偏数据中提取的统计模式。本综述结论指出：自动化古兰经诵读评估的未来在于融合语言学专业知识与先进音频处理的混合系统。此类方法将为开发可靠、公平且具有教学实效的工具铺平道路，真正助力全球学习者。