Optimizing Qur'an Interpretation with Natural Language Processing Through Critical Review and Practical Implications
Bibliographic record
Abstract
Objective: The purpose of this study is to examine the potential and challenges of the integration of Natural Language Processing (NLP) technology in the study of Qur'an interpretation in response to the need for more adaptive interpretation in the digital era. Theoretical framework: The theoretical framework of this research is based on the interdisciplinary between Islamic studies, computational linguistics, and artificial intelligence, especially in the context of Natural Language Processing. Literature review: The literature review includes classical and contemporary literature on Qur'anic interpretation, as well as an exploration of digital projects that utilize NLP in the processing of religious texts. Methods: This study uses a qualitative descriptive approach based on a literature study to analyze the development of NLP technology and its relevance in interpretation studies. Results: The results show that NLP has great potential as a strategic tool in Islamic education, da'wah, and research, especially if it is developed ethically and collaboratively between experts from various disciplines. Implication: The implication of this study is the need to build a digital interpretation system that is not only technologically sophisticated, but also sensitive to the theological, cultural, and spiritual values of Muslims. Novelty: The novelty of this research lies in the proposal of NLP integration as an alternative approach in the study of contextual and adaptive interpretation of the Qur'an to the needs of the digital generation, as well as the emphasis on the importance of cross-field collaboration to ensure scientific accuracy and relevance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.117 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".