Natural Language Processing for Interactive and Personalized Qur’anic Education
DOI:
https://doi.org/10.63876/ijtm.v2i2.130Keywords:
Natural Language Processing, Quran Learning, Intelligence Artificial, Arabic; Islamic Educational Technology, Analysis of the Qur'an TextAbstract
The development of artificial intelligence technology, particularly Natural Language Processing (NLP), has opened significant opportunities for transforming Qur’anic learning methods. NLP, as a branch of AI focused on the interaction between computers and human languages, offers new approaches to understanding, analyzing, and teaching the text of the Qur’an in a more interactive and personalized manner. This article examines the utilization of NLP technology in the context of Qur’anic education, from the application of Arabic word morphology analysis to paragraph search systems based on meaning, and the development of virtual assistants capable of answering questions about the contents of the Qur’an. This approach not only enhances accessibility and learning efficiency but also strengthens semantic and contextual understanding of the holy verses. The study also highlights linguistic challenges in processing classical Arabic, as well as the importance of quality annotations and digital corpora. Through a literature review and case study implementation, this article demonstrates that the integration of NLP in Qur’anic learning is a strategic step to enrich Islamic education methods in the digital era, while also bridging the younger generation to the values of the Qur’an through relevant technology.
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H. Alsamarrai, O. Tayan, M. Abbod, and Y. M. Alginahi, “Requirements Assessment for Organizations, Users, Software Developers, and Funders for the Propagation of the Holy Quran and Its Sciences,” in 2013 Taibah University International Conference on Advances in Information Technology for the Holy Quran and Its Sciences, IEEE, Dec. 2013, pp. 402–406. doi: https://doi.org/10.1109/NOORIC.2013.84.
F. Kurniawan, M. S. Khalil, M. K. Khan, and Y. M. Alginahi, “DWT+LSB-based fragile watermarking method for digital Quran images,” in 2014 International Symposium on Biometrics and Security Technologies (ISBAST), IEEE, Aug. 2014, pp. 290–297. doi: https://doi.org/10.1109/ISBAST.2014.7013137.
M. M. Farouk, K. M. Pufpaff, and M. Amir, “Industrial halal meat production and animal welfare: A review,” Meat Sci., vol. 120, pp. 60–70, Oct. 2016, doi: https://doi.org/10.1016/j.meatsci.2016.04.023.
G. Wang, X. Jiang, N. Liu, and X. Xu, “Language-enhanced object reasoning networks for video moment retrieval with text query,” Comput. Electr. Eng., vol. 102, p. 108137, Sep. 2022, doi: https://doi.org/10.1016/j.compeleceng.2022.108137.
S. Shahriar and U. Tariq, “Classifying Maqams of Qur’anic Recitations Using Deep Learning,” IEEE Access, vol. 9, pp. 117271–117281, 2021, doi: https://doi.org/10.1109/ACCESS.2021.3098415.
R. Malhas and T. Elsayed, “Arabic machine reading comprehension on the Holy Qur’an using CL-AraBERT,” Inf. Process. Manag., vol. 59, no. 6, p. 103068, Nov. 2022, doi: https://doi.org/10.1016/j.ipm.2022.103068.
M. Hadwan, H. A. Alsayadi, and S. AL-Hagree, “An End-to-End Transformer-Based Automatic Speech Recognition for Qur’an Reciters,” Comput. Mater. Contin., vol. 74, no. 2, pp. 3471–3487, 2023, doi: https://doi.org/10.32604/cmc.2023.033457.
A. Alosaimy and E. Atwell, “Diacritization of a Highly Cited Text: A Classical Arabic Book as a Case,” in 2018 IEEE 2nd International Workshop on Arabic and Derived Script Analysis and Recognition (ASAR), IEEE, Mar. 2018, pp. 72–77. doi: https://doi.org/10.1109/ASAR.2018.8480176.
P. Passban and M. Shokrollahi-Far, “Developing a shuffle grammar for parsing Arabic verbs,” in The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012), IEEE, May 2012, pp. 256–260. doi: https://doi.org/10.1109/AISP.2012.6313754.
M. M. Abu Shquier, “Novel Prototype for Handling Arabic Natural Language Processing: Smart Morphological Analyser,” in 2019 Second International Conference on Artificial Intelligence for Industries (AI4I), IEEE, Sep. 2019, pp. 1–8. doi: https://doi.org/10.1109/AI4I46381.2019.00010.
S. Wang, Y. Cao, X. Zheng, and T. Zhang, “A learning system for motion planning of free-float dual-arm space manipulator towards non-cooperative object,” Aerosp. Sci. Technol., vol. 131, p. 107980, Dec. 2022, doi: https://doi.org/10.1016/j.ast.2022.107980.
S. K. Lo, Q. Lu, L. Zhu, H.-Y. Paik, X. Xu, and C. Wang, “Architectural patterns for the design of federated learning systems,” J. Syst. Softw., vol. 191, p. 111357, Sep. 2022, doi: https://doi.org/10.1016/j.jss.2022.111357.
I. A. Ahmed, F. N. AL-Aswadi, K. M. G. Noaman, and W. Z. Alma’aitah, “Arabic Knowledge Graph Construction: A close look in the present and into the future,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 9, pp. 6505–6523, Oct. 2022, doi: https://doi.org/10.1016/j.jksuci.2022.04.007.
A. Alwehaibi, M. Bikdash, M. Albogmi, and K. Roy, “A study of the performance of embedding methods for Arabic short-text sentiment analysis using deep learning approaches,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 8, pp. 6140–6149, Sep. 2022, doi: https://doi.org/10.1016/j.jksuci.2021.07.011.
H. Alshalabi, S. Tiun, N. Omar, E. abdulwahab Anaam, and Y. Saif, “BPR algorithm: New broken plural rules for an Arabic stemmer,” Egypt. Informatics J., vol. 23, no. 3, pp. 363–371, Sep. 2022, doi: https://doi.org/10.1016/j.eij.2022.02.006.
M. M.Abdelgwad, T. H. A Soliman, A. I.Taloba, and M. F. Farghaly, “Arabic aspect based sentiment analysis using bidirectional GRU based models,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 9, pp. 6652–6662, Oct. 2022, doi: https://doi.org/10.1016/j.jksuci.2021.08.030.
M. G. H. Al Zamil and Q. Al-Radaideh, “Automatic extraction of ontological relations from Arabic text,” J. King Saud Univ. - Comput. Inf. Sci., vol. 26, no. 4, pp. 462–472, Dec. 2014, doi: https://doi.org/10.1016/j.jksuci.2014.06.007.
M. P. Gomez-Laich, R. T. Miller, and S. Pessoa, “Scaffolding analytical argumentative writing in a design class: A corpus analysis of student writing,” Linguist. Educ., vol. 51, pp. 20–30, Jun. 2019, doi: https://doi.org/10.1016/j.linged.2019.03.003.
R. Jenarthanan, Y. Senarath, and U. Thayasivam, “ACTSEA: Annotated Corpus for Tamil & Sinhala Emotion Analysis,” in 2019 Moratuwa Engineering Research Conference (MERCon), IEEE, Jul. 2019, pp. 49–53. doi: https://doi.org/10.1109/MERCon.2019.8818760.
E. H. Mohamed and E. M. Shokry, “QSST: A Quranic Semantic Search Tool based on word embedding,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 3, pp. 934–945, Mar. 2022, doi: https://doi.org/10.1016/j.jksuci.2020.01.004.
