Integration of Artificial Intelligence in Islamic Education Curriculum
Keywords:
artificial intelligence, Islamic education, curriculum, personalized learning, content analysisAbstract
In the digital era, the integration of artificial intelligence (AI) technology in education is essential to improve the quality of learning. This study explores the application of AI in the Islamic education curriculum to improve student learning outcomes. The problem raised is the minimal use of AI technology in Islamic learning, which can optimize student potential. This study is a narrative literature study that examines 17 relevant articles and books with qualitative content analysis methods. Data were analyzed thematically to identify key themes related to the implementation of AI in Islamic education. The results of the study indicate that AI can improve students' understanding of the subject matter, facilitate more personalized learning, and provide faster and more accurate feedback. In addition, AI helps teachers recognize students' specific needs, so that teaching can be tailored to individual abilities. In conclusion, the integration of AI in the Islamic education curriculum has great potential to improve the quality of education, provided there is adequate training for teachers and the development of supporting technological infrastructure. This study recommends collaboration between educational institutions, technology developers, and the government to realize effective AI integration.
References
Abdullahi, T., Singh, R., & Eickhoff, C. (2024). Learning to Make Rare and Complex Diagnoses With Generative AI Assistance: Qualitative Study of Popular Large Language Models. JMIR Medical Education, 10(1). https://doi.org/10.2196/51391
Abonamah, A. A., Tariq, M. U., & Shilbayeh, S. (2021). On the Commoditization of Artificial Intelligence. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.696346
Acs, B., Rantalainen, M., & Hartman, J. (2020). Artificial intelligence as the next step towards precision pathology. Journal of Internal Medicine, 288(1), 62–81. https://doi.org/10.1111/joim.13030
Aeni, N., Darmawati, B., Muthmainnah, Yunus, M., Mulyanah, A., Sharma, A., Rachmat, & Hamka, D. W. (2024). Disclosing User Views: A Qualitative Investigation of Writing and Speaking Ability Development via Integration of Jenni AI and Jenni Speak. Lecture Notes in Networks and Systems, 1074 LNNS, 191–203. https://doi.org/10.1007/978-981-97-6103-6_13
Agbo, F. J., Oyelere, S. S., Suhonen, J., & Tukiainen, M. (2021). Scientific production and thematic breakthroughs in smart learning environments: a bibliometric analysis. Smart Learning Environments, 8(1). https://doi.org/10.1186/s40561-020-00145-4
Alamrani, M. H., Alammar, K. A., Alqahtani, S. S., & Salem, O. A. (2018). Comparing the Effects of Simulation-Based and Traditional Teaching Methods on the Critical Thinking Abilities and Self-Confidence of Nursing Students. Journal of Nursing Research, 26(3), 152–157. https://doi.org/10.1097/jnr.0000000000000231
Amirin, I., & Suparman. (2019). Worksheet development design to improve student problem solving ability and learning motivation. International Journal of Scientific and Technology Research, 8(12), 3965–3970.
Artificial Intelligence: A National Strategic Initiative. (2020). In Artificial Intelligence: A National Strategic Initiative. Springer Singapore. https://doi.org/10.1007/978-981-15-6548-9
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
Bhalla, N. (2019). The 3S process: A framework for teaching AI strategy in business education. Technology Innovation Management Review, 9(12), 36–42. https://doi.org/10.22215/timreview/1290
Bhattacharya, S. (2023). Machines and morals: Moral reasoning ability might indicate how close AI is to attaining true equivalence with human intelligence. Handbook of Critical Studies of Artificial Intelligence, 470–480. https://doi.org/10.4337/9781803928562.00049
Blanié, A., Amorim, M. A., & Benhamou, D. (2020). Comparative value of a simulation by gaming and a traditional teaching method to improve clinical reasoning skills necessary to detect patient deterioration: A randomized study in nursing students. BMC Medical Education, 20(1). https://doi.org/10.1186/s12909-020-1939-6
Brzozowska, M., Kolasińska-Morawska, K., Sułkowski, Ł., & Morawski, P. (2023). Artificial-intelligence-powered customer service management in the logistics industry. Entrepreneurial Business and Economics Review, 11(4), 109 – 121. https://doi.org/10.15678/EBER.2023.110407
Caliskan, A. (2023). Artificial Intelligence, Bias, and Ethics. In E. E. (Ed.), IJCAI International Joint Conference on Artificial Intelligence (Vols. 2023-August, pp. 7007 – 7013). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2023/799
Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00408-3
Chen, Y., & de Luca, G. (2021a). Technologies Supporting Artificial Intelligence and Robotics Application Development. Journal of Artificial Intelligence and Technology, 1(1), 1 – 8. https://doi.org/10.37965/jait.2020.0065
Chen, Y., & de Luca, G. (2021b). Technologies Supporting Artificial Intelligence and Robotics Application Development. Journal of Artificial Intelligence and Technology, 1(1), 1 – 8. https://doi.org/10.37965/jait.2020.0065
Cvetkovic, A., Savela, N., Latikka, R., & Oksanen, A. (2024). Do We Trust Artificially Intelligent Assistants at Work? An Experimental Study. Human Behavior and Emerging Technologies, 2024. https://doi.org/10.1155/2024/1602237
da Silva, R. J. R., Rodrigues, R. G., & Leal, C. T. P. (2019). Gamification in management education: A systematic literature review. BAR - Brazilian Administration Review, 16(2). https://doi.org/10.1590/1807-7692bar2019180103
Dann, C., O’Neill, S., Getenet, S., Chakraborty, S., Saleh, K., & Yu, K. (2024). Improving Teaching and Learning in Higher Education through Machine Learning: Proof of Concept’ of AI’s Ability to Assess the Use of Key Microskills. Education Sciences, 14(8). https://doi.org/10.3390/educsci14080886
Darmayanti, R. (2024). Research Design: RnD Research Method. Available at SSRN, 5012967.
Davis, A. E. (2020). The future of law firms (and lawyers) in the age of artificial intelligence; [O Futuro dos escritorios de advocacia (e dos advogados) na era da inteligencia artificial]. Revista Direito GV, 16(1), 1DUMMT. https://doi.org/10.1590/2317-6172201945
Dey, P., & Jana, D. K. (2023). Evaluation of the Convincing Ability through Presentation Skills of Pre-Service Management Wizards Using AI via T2 Linguistic Fuzzy Logic. Journal of Computational and Cognitive Engineering, 2(2), 133–142. https://doi.org/10.47852/bonviewJCCE2202158
Dirnfeld, R., De Donato, L., Somma, A., Azari, M. S., Marrone, S., Flammini, F., & Vittorini, V. (2024). Integrating AI and DTs: challenges and opportunities in railway maintenance application and beyond. Simulation, 100(9), 903–917. https://doi.org/10.1177/00375497241229756
Farrokhi, A., Shirazi, F., Hajli, N., & Tajvidi, M. (2020). Using artificial intelligence to detect crisis related to events: Decision making in B2B by artificial intelligence. Industrial Marketing Management, 91, 257 – 273. https://doi.org/10.1016/j.indmarman.2020.09.015
Forero, D. S., Ackermann, S., Betbeder, M. L., & Henriet, J. (2024). Automatic Real-Time Adaptation of Training Session Difficulty Using Rules and Reinforcement Learning in the AI-VT ITS. International Journal of Modern Education and Computer Science, 16(3), 56 – 71. https://doi.org/10.5815/ijmecs.2024.03.05
Gera, J., Marwaha, E. B., Thareja, R., & Jain, A. (2023). Predicting and Improving Behavioural Factors that Boosts Learning Abilities in Post-Pandemic Times using AI Techniques. International Journal of Advanced Computer Science and Applications, 14(11), 274–282. https://doi.org/10.14569/IJACSA.2023.0141127
Ghosh, S., Roy, P., Sarkar, S. K., Podder, A., & Roy, B. (2024). Challenges and barriers to integrating AI in library environments. Applications of Artificial Intelligence in Libraries, 109–138. https://doi.org/10.4018/979-8-3693-1573-6.ch005
Gómez-Caicedo, M. I., Gaitán-Angulo, M., Bacca-Acosta, J., Briñez Torres, C. Y., & Cubillos Díaz, J. (2022). Business analytics approach to artificial intelligence. Frontiers in Artificial Intelligence, 5. https://doi.org/10.3389/frai.2022.974180
Gonzalez, A.-J., de Lima, M. P., Preto, L., Amarante, N., & Barros, R. (2024). Playback Theatre applications: A systematic review of literature. Arts in Psychotherapy, 89. https://doi.org/10.1016/j.aip.2024.102152
Gornitzka, Å. (1999). Governmental policies and organisational change in higher education. Higher Education, 38(1), 5–31. https://doi.org/10.1023/A:1003703214848
Goyal, S. B., Rajawat, A. S., Mittal, R., & Shrivastava, D. P. (2024). Integrating AI-enabled post-quantum models in quantum cyber-physical systems opportunities and challenges. Applied Data Science and Smart Systems, 491–498. https://doi.org/10.1201/9781003471059-63
Guo, H., Yi, W., & Liu, K. (2024). Enhancing Constructivist Learning: The Role of Generative AI in Personalised Learning Experiences. International Conference on Enterprise Information Systems, ICEIS - Proceedings, 1, 767–770. https://doi.org/10.5220/0012688700003690
Guo, K., Pan, M., Li, Y., & Lai, C. (2024). Effects of an AI-supported approach to peer feedback on university EFL students’ feedback quality and writing ability. Internet and Higher Education, 63. https://doi.org/10.1016/j.iheduc.2024.100962
Harris, C. G. (2024). Challenges and Opportunities of Integrating Non-Fungible Tokens (NFTs) and Self-Sovereign AI (SSAI) in Blockchain-Based Metaverse Projects. 2024 9th International Conference on Big Data Analytics, ICBDA 2024, 288–296. https://doi.org/10.1109/ICBDA61153.2024.10607366
Hendarto, T., I.P., S., Darmayanti, R., & odi, M. M. (2024). Sinkronisasi cerdas researchgate. CV. Bildung Nusantara.
Hillis, E., Bhattarai, K., & Abrams, Z. (2024). Evaluating Generative AI’s Ability to Identify Cancer Subtypes in Publicly Available Structured Genetic Datasets. Journal of Personalized Medicine, 14(10). https://doi.org/10.3390/jpm14101022
Hong, J., Maciejewski, R., Trubuil, A., & Isenberg, T. (2024). Visualizing and Comparing Machine Learning Predictions to Improve Human-AI Teaming on the Example of Cell Lineage. IEEE Transactions on Visualization and Computer Graphics, 30(4), 1956 – 1969. https://doi.org/10.1109/TVCG.2023.3302308
Hooshyar, D., Azevedo, R., & Yang, Y. (2024). Augmenting Deep Neural Networks with Symbolic Educational Knowledge: Towards Trustworthy and Interpretable AI for Education. Machine Learning and Knowledge Extraction, 6(1), 593 – 618. https://doi.org/10.3390/make6010028
Hotimah, L. H. (2024). Strengthening morals in Islamic religious education for elementary school students. Assyfa Journal of Multidisciplinary Education, 1, 5–9.
Hsu, C.-Y., & Li, W. (2023). Explainable GeoAI: can saliency maps help interpret artificial intelligence’s learning process? An empirical study on natural feature detection. International Journal of Geographical Information Science, 37(5), 963 – 987. https://doi.org/10.1080/13658816.2023.2191256
Hu, G., & Yu, B. (2022). Artificial Intelligence and Applications. Journal of Artificial Intelligence and Technology, 2(2), 39 – 41. https://doi.org/10.37965/jait.2022.0102
Huang, R. S. T., Lu, K. J. Q., Meaney, C., Kemppainen, J., Punnett, A., & Leung, F.-H. (2023). Assessment of Resident and AI Chatbot Performance on the University of Toronto Family Medicine Residency Progress Test: Comparative Study. JMIR Medical Education, 9. https://doi.org/10.2196/50514
Jadli, A., Hain, M., & Hasbaoui, A. (2023). Artificial intelligence-based lead propensity prediction. IAES International Journal of Artificial Intelligence, 12(3), 1281 – 1290. https://doi.org/10.11591/ijai.v12.i3.pp1281-1290
Jahan, S., & Hossain, M. (n.d.). The role of non-governmental organizations in promoting education for unregistered communities. Journal of NGO and Education Research, 29(3), 260–275. https://doi.org/10.1080/10503297.2021.2134567
Juniar, A., & Tambunan, P. M. (2023). The effect of scientific process skills preservice chemistry teacher’s to creative thinking ability in analytical chemistry learning. AIP Conference Proceedings, 2673. https://doi.org/10.1063/5.0126386
Jürgensmeier, L., & Skiera, B. (2024). Generative AI for scalable feedback to multimodal exercises. International Journal of Research in Marketing, 41(3), 468 – 488. https://doi.org/10.1016/j.ijresmar.2024.05.005
Kajiwara, Y., Matsuoka, A., & Shinbo, F. (2023). Machine learning role playing game: Instructional design of AI education for age-appropriate in K-12 and beyond. Computers and Education: Artificial Intelligence, 5. https://doi.org/10.1016/j.caeai.2023.100162
Kant, S., Singh, S., Mehra, K., Vichoray, C., Kharayat, P. S., & Soni, M. (2024). Challenges and Resolutions for Integrating PV Solar Plants into the Grid using AI Techniques in a Uniform Environment. Proceedings of the 5th International Conference on Smart Electronics and Communication, ICOSEC 2024, 420–425. https://doi.org/10.1109/ICOSEC61587.2024.10722703
Karim, S., Hariyadi, A., Zoker, E. M., & Yambasu, A. R. (2023). Quality of education in government and government-assisted schools in Sierra Leone. AMCA Journal of Education and Behavioral Change, 3(2). https://doi.org/10.51773/ajeb.v3i2.248
Kerzel, U. (2021). Enterprise AI Canvas Integrating Artificial Intelligence into Business. Applied Artificial Intelligence, 35(1), 1 – 12. https://doi.org/10.1080/08839514.2020.1826146
Kim, H.-S., Jung, J., Hwang, R., Park, S.-C., Lee, S.-J., Kim, G.-T., & Lee, B.-W. (2024). Classification of PRPD Pattern in Cast-Resin Transformers Using CNN and Implementation of Explainable AI (XAI) With Grad-CAM. IEEE Access, 12, 53623 – 53632. https://doi.org/10.1109/ACCESS.2024.3365135
Kong, S. C., Ogata, H., Shih, J. L., & Biswas, G. (2021). The Role of Artificial Intelligence in STEM Education. In 29th International Conference on Computers in Education Conference, ICCE 2021 - Proceedings (Vol. 2, pp. 775–777). https://api.elsevier.com/content/abstract/scopus_id/85122956178
Krishnamoorthy, R., Kamala, K., Soubache, I. D., Karthik, M. V., & Begum, M. A. (2022). Integration of blockchain and artificial intelligence in smart city perspectives. Smart City Infrastructure: The Blockchain Perspective, 77–112. https://doi.org/10.1002/9781119785569.ch3
Kulikowski, C. A. (2019). Beginnings of Artificial Intelligence in Medicine (AIM): Computational Artifice Assisting Scientific Inquiry and Clinical Art - with Reflections on Present AIM Challenges. Yearbook of Medical Informatics, 28(1), 249 – 256. https://doi.org/10.1055/s-0039-1677895
Lestari, J. T., Darmayanti, R., & Arifin, Z. (2024). Integration of Artificial Intelligence in Islamic Education Curriculum. JPCIS: Journal of Pergunu and Contemporary Islamic Studies, 1(1).
Lin, C., & Qi, W. (2024). Integrating AI in Human-Robot Interaction: Emerging Challenges and Future Directions. 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops, ICASSPW 2024 - Proceedings, 379–382. https://doi.org/10.1109/ICASSPW62465.2024.10627168
Lippi, M., Contissa, G., Lonowska, A. J., Lagioia, F., Micklitz, H.-W., Palka, P., Sartor, G., & Torroni, P. (2020). The Force Awakens: Artificial intelligence for consumer law. Journal of Artificial Intelligence Research, 67, 169 – 190. https://doi.org/10.1613/jair.1.11519
Liu, Z., Sun, Y., Xing, C., Liu, J., He, Y., Zhou, Y., & Zhang, G. (2022). Artificial intelligence powered large-scale renewable integrations in multi-energy systems for carbon neutrality transition: Challenges and future perspectives. Energy and AI, 10. https://doi.org/10.1016/j.egyai.2022.100195
MacCarthy, M., & Shan, H. (2022). Machine infelicity in a poignant visitor setting: comparing human and AI’s ability to analyze discourse. Current Issues in Tourism, 25(8), 1289–1306. https://doi.org/10.1080/13683500.2021.1915252
Marra, G., Dumančić, S., Manhaeve, R., & De Raedt, L. (2024). From statistical relational to neurosymbolic artificial intelligence: A survey. Artificial Intelligence, 328. https://doi.org/10.1016/j.artint.2023.104062
Marti, D., Budathoki, A., Ding, Y., Lucas, G., & Nelson, D. (2024). How Does Acknowledging Users’ Preferences Impact AI’s Ability to Make Conflicting Recommendations? International Journal of Human-Computer Interaction. https://doi.org/10.1080/10447318.2024.2426035
Maruyama, Y. (2022). Categorical Artificial Intelligence: The Integration of Symbolic and Statistical AI for Verifiable, Ethical, and Trustworthy AI. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13154 LNAI, 127–138. https://doi.org/10.1007/978-3-030-93758-4_14
Morad, S. (2021). The validity and reliability of a tool for measuring educational innovative thinking competencies. Teaching and Teacher Education, 97. https://doi.org/10.1016/j.tate.2020.103193
Muntarbhorn, V. (2023). Microverse, Mezzoverse, Macroverse: Protection Against Discrimination in an Artificialised World? Asia Pacific Journal on Human Rights and the Law, 24(2), 186 – 197. https://doi.org/10.1163/15718158-24020006
Nandutu, I., Atemkeng, M., & Okouma, P. (2023). Integrating AI ethics in wildlife conservation AI systems in South Africa: a review, challenges, and future research agenda. AI and Society, 38(1), 245–257. https://doi.org/10.1007/s00146-021-01285-y
Nazaretsky, T., Ariely, M., Cukurova, M., & Alexandron, G. (2022). Teachers’ trust in AI-powered educational technology and a professional development program to improve it. British Journal of Educational Technology, 53(4), 914–931. https://doi.org/10.1111/bjet.13232
Nong, L., Liu, G., & Tan, C. (2021). An Empirical Study on the Implementation of AI Assisted Language Teaching for Improving Learner’s Learning Ability. Proceedings - 2021 10th International Conference of Educational Innovation through Technology, EITT 2021, 215–221. https://doi.org/10.1109/EITT53287.2021.00050
Onyejegbu, L. N. (2023). Challenges of Integrating AI Ethics into Higher Education Curricula in West Africa: Nigerian Universities Narrative. SpringerBriefs in Ethics, 57–66. https://doi.org/10.1007/978-3-031-23035-6_5
Owens, E., Sheehan, B., Mullins, M., Cunneen, M., Ressel, J., & Castignani, G. (2022). Explainable Artificial Intelligence (XAI) in Insurance. Risks, 10(12). https://doi.org/10.3390/risks10120230
Pedersen, T., & Johansen, C. (2020). Behavioural artificial intelligence: an agenda for systematic empirical studies of artificial inference. AI and Society, 35(3), 519 – 532. https://doi.org/10.1007/s00146-019-00928-5
Perkins, M., Furze, L., Roe, J., & Macvaugh, J. (2024). The Artificial Intelligence Assessment Scale (AIAS): A Framework for Ethical Integration of Generative AI in Educational Assessment. Journal of University Teaching and Learning Practice, 21(6). https://doi.org/10.53761/q3azde36
Pinski, M., Hofmann, T., & Benlian, A. (2024). AI Literacy for the top management: An upper echelons perspective on corporate AI orientation and implementation ability. Electronic Markets, 34(1). https://doi.org/10.1007/s12525-024-00707-1
Plantin, J. C., & Punathambekar, A. (2019). Digital media infrastructures: pipes, platforms, and politics. Media, Culture and Society, 41(2), 163–174. https://doi.org/10.1177/0163443718818376
Polster, L., Bilgram, V., & Gortz, S. (2024). AI-Augmented Design Thinking: Potentials, Challenges, and Mitigation Strategies of Integrating Artificial Intelligence in Human-Centered Innovation Processes. IEEE Engineering Management Review. https://doi.org/10.1109/EMR.2024.3512866
Prawita, W., Prayitno, B. A., & Sugiyarto. (2019). Effectiveness of a generative learning-based biology module to improve the analytical thinking skills of the students with high and low reading motivation. International Journal of Instruction, 12(1), 1459–1476. https://doi.org/10.29333/iji.2019.12193a
Raquib, A., Channa, B., Zubair, T., & Qadir, J. (2022). Islamic virtue-based ethics for artificial intelligence. Discover Artificial Intelligence, 2(1). https://doi.org/10.1007/s44163-022-00028-2
Reshetnikova, M. S., & Mikhaylov, I. A. (2023). Artificial Intelligence Development: Implications for China. Montenegrin Journal of Economics, 19(1), 139 – 152. https://doi.org/10.14254/1800-5845/2023.19-1.12
Rosenberg, L., & Willcox, G. (2020). Artificial swarm intelligence. Advances in Intelligent Systems and Computing, 1037, 1054 – 1070. https://doi.org/10.1007/978-3-030-29516-5_79
Safitri, N. D., In’am, A., Latipun, L., & Mahmood, T. (2022). When did the “google classroom platform” become an issue in Islamic religious education? AMCA Journal of Religion and Society, 2(2). https://doi.org/10.51773/ajrs.v2i2.290
Sajja, R., Ramirez, C. E., Li, Z., Demiray, B. Z., Sermet, Y., & Demir, I. (2024). Integrating Generative AI in Hackathons: Opportunities, Challenges, and Educational Implications. Big Data and Cognitive Computing, 8(12). https://doi.org/10.3390/bdcc8120188
Santhiya, S. D., Padmapriya, T., Salameh, A. A., Wildan, M. A., & Kishore, K. H. (2022). AI Enabled-6G: Artificial Intelligence (AI) for Integration of 6G Wireless Communications. International Journal of Communication Networks and Information Security, 14(3), 372–379.
Schneider, J., Abraham, R., Meske, C., & Vom Brocke, J. (2023). Artificial Intelligence Governance For Businesses. Information Systems Management, 40(3), 229 – 249. https://doi.org/10.1080/10580530.2022.2085825
Schrettenbrunnner, M. B. (2020). Artificial-Intelligence-Driven Management. IEEE Engineering Management Review, 48(2), 15 – 19. https://doi.org/10.1109/EMR.2020.2990933
Schwendicke, F., Samek, W., & Krois, J. (2020). Artificial Intelligence in Dentistry: Chances and Challenges. Journal of Dental Research, 99(7), 769–774. https://doi.org/10.1177/0022034520915714
Sharma, D. M., Ramana, K. V., Jothilakshmi, R., Verma, R., Maheswari, B. U., & Boopathi, S. (2023). Integrating generative AI into K-12 curriculums and pedagogies in India: Opportunities and challenges. Facilitating Global Collaboration and Knowledge Sharing in Higher Education With Generative AI, 133–161. https://doi.org/10.4018/9798369304877.ch006
Sipola, J., Saunila, M., & Ukko, J. (2023). Adopting artificial intelligence in sustainable business. Journal of Cleaner Production, 426. https://doi.org/10.1016/j.jclepro.2023.139197
Solehudin, R. H., Budiarti, E., Gunawan, R., & Darmayanti, R. (2025). Pengembangan instrumen penelitian [sumber elektronis] : analisis kebijakan komunitas perkotaan dalam perspektif interseksional. Kaizen Media Publishing, 1, 1–450.
Sresakoolchai, J., & Kaewunruen, S. (2022). Integration of Building Information Modeling (BIM) and Artificial Intelligence (AI) to Detect Combined Defects of Infrastructure in the Railway System. Lecture Notes in Civil Engineering, 202, 377–386. https://doi.org/10.1007/978-981-16-6978-1_30
Sukkar, A. W., Fareed, M. W., Yahia, M. W., Mushtaha, E., & de Giosa, S. L. (2024). Artificial Intelligence Islamic Architecture (AIIA): What Is Islamic Architecture in the Age of Artificial Intelligence? Buildings, 14(3). https://doi.org/10.3390/buildings14030781
Sun, J., Dunne, M. P., Hou, X. yu, & Xu, A. qiang. (2011). Educational stress scale for adolescents: Development, validity, and reliability with Chinese students. Journal of Psychoeducational Assessment, 29(6), 534–546. https://doi.org/10.1177/0734282910394976
Suresh Babu, C. V., & Barath Kumar, S. (2023). Navigating the Terrain: Current challenges and solutions in integrating generative AI into education. Generative AI in Teaching and Learning, 274–290. https://doi.org/10.4018/979-8-3693-0074-9.ch011
Susanti, S. S., Nursafitri, L., Hamzah, I., Zunarti, R., Darmanto, Fitriyah, Asy’arie, B. F., & Sa’ad, M. S. (2024). Innovative Digital Media in Islamic Religious Education Learning. Jurnal Pendidikan Agama Islam, 21(1), 40–59. https://doi.org/10.14421/jpai.v21i1.7553
Sutiene, K., Schwendner, P., Sipos, C., Lorenzo, L., Mirchev, M., Lameski, P., Kabasinskas, A., Tidjani, C., Ozturkkal, B., & Cerneviciene, J. (2024). Enhancing portfolio management using artificial intelligence: literature review. Frontiers in Artificial Intelligence, 7. https://doi.org/10.3389/frai.2024.1371502
Swindell, A., Greeley, L., Farag, A., & Verdone, B. (2024a). Against Artificial Education: Towards an Ethical Framework for Generative Artificial Intelligence (AI) Use in Education. Online Learning Journal, 28(2). https://doi.org/10.24059/olj.v28i2.4438
Swindell, A., Greeley, L., Farag, A., & Verdone, B. (2024b). Against Artificial Education: Towards an Ethical Framework for Generative Artificial Intelligence (AI) Use in Education. Online Learning Journal, 28(2). https://doi.org/10.24059/olj.v28i2.4438
Tariq, M. U. (2024). Integrating AI and blockchain in EV charging: Innovations and challenges. A Sustainable Future with E-Mobility: Concepts, Challenges, and Implementations, 253–269. https://doi.org/10.4018/9798369352472.ch013
Tsirulnikov, D., Suart, C., Abdullah, R., Vulcu, F., & Mullarkey, C. E. (2023). Game on: immersive virtual laboratory simulation improves student learning outcomes & motivation. FEBS Open Bio, 13(3), 396–407. https://doi.org/10.1002/2211-5463.13567
Ummaroh, A., Salmia, U., Pramesti, S. L. D., & Muyassaroh, A. (2023). Systematics Literature Review: Eksplorasi Etnomatematika pada Permainan Tradisional. Prosiding Santika: Seminar Nasional Tadris Matematika UIN K.H. Abdurrahman Wahid Pekalongan, 128–139. http://103.142.62.229/index.php/santika/article/view/1361
Uzumcu, O. (2024a). Do Innovative Teachers use AI-powered Tools More Interactively? A Study in the Context of Diffusion of Innovation Theory. Technology, Knowledge and Learning, 29(2), 1109–1128. https://doi.org/10.1007/s10758-023-09687-1
Uzumcu, O. (2024b). Do Innovative Teachers use AI-powered Tools More Interactively? A Study in the Context of Diffusion of Innovation Theory. Technology, Knowledge and Learning, 29(2), 1109–1128. https://doi.org/10.1007/s10758-023-09687-1
Xu, F. W. X., Tang, S. S., Soh, H. N., Pang, N. Q., & Bonney, G. K. (2023). Augmenting care in hepatocellular carcinoma with artificial intelligence. Artificial Intelligence Surgery, 3(1), 48 – 63. https://doi.org/10.20517/ais.2022.33
Yadav, D., & Gulati, A. (2024). Artificial Intelligence and Machine Learning in Healthcare. In Artificial Intelligence and Machine Learning in Healthcare. Taylor and Francis. https://doi.org/10.1007/978-981-99-6472-7
Yeadon, W., & Hardy, T. (2024). The impact of AI in physics education: a comprehensive review from GCSE to university levels. Physics Education, 59(2). https://doi.org/10.1088/1361-6552/ad1fa2
Yilmaz, S. K., Eskici, G., & Saraç, O. E. (2023). Validity-reliability of the e-Healthy Diet Literacy Scale in Turkish adults. Baltic Journal of Health and Physical Activity, 15(3). https://doi.org/10.29359/BJHPA.15.3.09
Yue Yim, I. H. (2024). A critical review of teaching and learning artificial intelligence (AI) literacy: Developing an intelligence-based AI literacy framework for primary school education. Computers and Education: Artificial Intelligence, 7. https://doi.org/10.1016/j.caeai.2024.100319
Zhang, P. (2024). A systematic literature review on vlog marketing: thematic analysis and future research directions. Asia Pacific Journal of Marketing and Logistics, 36(6), 1538–1555. https://doi.org/10.1108/APJML-10-2023-0994
Zhuang, Y. ting, Wu, F., Chen, C., & Pan, Y. he. (2017). Challenges and opportunities: from big data to knowledge in AI 2.0. Frontiers of Information Technology and Electronic Engineering, 18(1), 3–14. https://doi.org/10.1631/FITEE.1601883
Zulaikha, S., Mohamed, H., Kurniawati, M., Rusgianto, S., & Rusmita, S. A. (2020). Customer Predictive Analytics Using Artificial Intelligence. Singapore Economic Review. https://doi.org/10.1142/S0217590820480021
