Using AI to Check Recitation Fluency: Hype or Help?

In recent years, the integration of artificial intelligence (AI) into various sectors has brought about transformative changes. Among these developments is the use of AI to evaluate spoken language for fluency, readability, and accuracy. In the specific context of Quran recitation, AI-based tools are now being used to assess fluency—a foundational element in Tajweed and overall Quranic competence. This article critically examines whether these systems represent a significant advancement or whether their benefits are overshadowed by limitations, offering a balanced view of how AI performs in the assessment of recitation fluency.

Understanding Recitation Fluency

Recitation fluency, particularly in Quranic contexts, refers to the ability to read and articulate verses smoothly, with appropriate pace, rhythm, and accuracy. While Tajweed encompasses rules relating to pronunciation, elongation (mad), stopping (waqf), and articulation (makharij), fluency focuses on how naturally and consistently these rules are applied during recitation.

Fluency is a perceptual indicator of mastery. Skilled reciters typically blend proper articulation with ease of delivery—similar to how one might assess fluency in public speaking or reading aloud in another language. In competitions or educational settings, assessing fluency serves as a key discriminator for identifying learners’ progress and performance.

The Role of AI in Fluency Checking

AI systems for recitation assessment are increasingly grounded in technologies such as automatic speech recognition (ASR), natural language processing (NLP), and machine learning. These technologies analyse a user’s recorded recitation and generate reports or scores that estimate how fluent and accurate the delivery was.

Key components of such systems include:

• Speech Recognition: Converts the spoken recitation into phonetic and text-based data using models trained on similar audio inputs.
• Pronunciation Modelling: Compares user input to ideal pronunciations and highlights areas of deviation.
• Fluency Metrics: Measures attributes such as pacing, duration of pauses, hesitations, and articulation smoothness.
• Feedback System: Generates scores or flags for performance based on pre-set benchmarks.

Benefits of Using AI for Fluency Assessment

There are several advantages AI-based systems offer in evaluating Quran recitation fluency, particularly in contexts such as competitions, learning environments, and private practice.

1. Consistency in Evaluation

Unlike human judges who may unintentionally apply inconsistent standards due to fatigue or bias, AI systems apply uniform criteria across all assessments. This helps reduce discrepancies between different evaluations, particularly in large-scale competitions or when multiple judges are involved.

2. Time Efficiency

Manual evaluation of fluency requires substantial time and effort, especially when dealing with long passages or numerous candidates. AI can process and return assessments quickly, making it a scalable solution for large cohorts of students or participants.

3. Accessible Feedback

AI tools can offer instant feedback, allowing learners to identify issues and improve in near real-time. This kind of immediate reinforcement supports iterative learning and promotes self-correction without the constant need for external supervision.

4. Objective Benchmarking

Machine learning models trained on recordings of proficient reciters can establish standard performance benchmarks. Students can automatically compare their recitation against these benchmarks to understand their relative fluency level.

5. Supporting Non-Expert Users

In regions or contexts where access to experienced teachers is limited, AI tools can help newcomers gauge their fluency when reading alone or practising outside class. Parent-guided education or independent memorisation programmes particularly benefit from this feature.

Challenges and Limitations

While the use of AI in checking recitation fluency offers considerable promise, it is not without its shortcomings. These need to be carefully considered before adopting AI as a definitive or sole evaluation method.

1. Sensitivity to Input Quality

AI systems are heavily reliant on the quality of audio input. Poor recording quality, background noise, or accents can skew the results. This is particularly important in Quran recitation, where minor pronunciation differences may result in significant scoring variations.

2. Incomplete Understanding of Intention

AI can detect hesitations or prolonged pauses but cannot easily discern whether these interruptions were intentional (e.g., breathing pauses at waqf points) or errors. Teaching a model to understand the subtle context behind every pause requires immense data and domain-specific tuning.

3. Limitations in Handling Tajweed Complexity

While fluency assessment aligns loosely with pronunciation and rhythm, not all Tajweed rules are systematically recognisable by AI models. For example, understanding rules like idghaam bi-ghunnah (merging with nasalisation) or ikhfa’ requires interpretation of phonetic nuances that AI may misclassify, especially with low-quality microphones.

4. Cultural and Dialectal Variations

Even within the standard recitation style (Hafs ‘an ‘Asim), regional accents and learner backgrounds vary. A system trained heavily on a particular set of recitation voices may underperform when used by speakers outside that group, introducing fairness concerns.

5. Risk of Over-Reliance

There’s a legitimate concern that learners or organisers might overly depend on AI systems, neglecting expert human correction and mentoring. Fluency, while measurable, is only one part of holistic recitation assessment. Competence in Quran reading includes understanding meanings, applying Tajweed, and demonstrating humility—qualities that current AI cannot fully evaluate.

Integrating AI into Fluency Assessment Workflows

Rather than replacing human judgment entirely, AI is best used as a supplement to existing recitation marking practices. Here are some practical ways integration might occur:

• Pre-screening Tool: AI can be deployed before formal assessment to highlight strong and weak recitations in a large applicant pool, saving judges time and enabling targeted reviews.
• Self-practice Companion: Learners can practise with AI-based tools, receive fluency feedback, and later refine these efforts under a teacher’s guidance.
• Judge Support: During competitions, AI tools could serve as a second opinion that judges may consider before finalising scores, particularly in closely contested entries.

Case Examples and User Feedback

Initial deployments of AI-based fluency checkers in tajweed learning apps and online competition platforms have received mixed to favourable feedback. For example:

• Competitions using AI reports found that judges had greater confidence in their preliminary evaluations and used less time rechecking basic fluency metrics.
• Learners often appreciate visual feedback in the form of fluency heatmaps or scoring graphs which helped them adjust their pacing and pronunciation habits over time.
• However, feedback also notes occasional discrepancies where pauses made for breath were wrongly flagged as mistakes, indicating a need for better linguistic modelling.

As use cases expand, developers are increasingly refining models using larger and more diverse datasets. Some tools now allow customisation for specific competition rules or recognise variant narrations when explicitly selected.

The Future of Fluency Checking in Quranic Contexts

AI will likely continue to play a growing role in language instruction and spiritual education, provided it evolves alongside scholarly input and user feedback. The future of fluency checking may involve enhanced tools that combine voice recognition with real-time dialectal analysis, better tajweed classification systems, and vacuum-proof scoring when interference is detected.

Nevertheless, fluency in Quran recitation is not a technical checklist alone. Its essence includes spiritual presence, intention, and respect for divine speech—areas which AI may never fully grasp. As such, the best implementations of AI will serve not as judges, but as assistants.

Used wisely, AI can free up human time, standardise certain metrics, and help more people engage confidently with Quranic recitation. It can guide, not govern, a learner’s development—making it more of a technological help than mere hype.

If you need help with your Quran competition platform or marking tools, email info@qurancompetitions.tech.