MEREVOLUSI PENILAIAN LITERASI AL-QUR'AN MELALUI KECERDASAN BUATAN: MENINGKATKAN KUALITAS EVALUASI DAN HASIL PEMBELAJARAN

Abd. Madjid; Akbar Nur Aziz; Siti Bahiroh

doi:10.31764/jmm.v10i4.40643

MEREVOLUSI PENILAIAN LITERASI AL-QUR'AN MELALUI KECERDASAN BUATAN: MENINGKATKAN KUALITAS EVALUASI DAN HASIL PEMBELAJARAN

Abd. Madjid, Akbar Nur Aziz, Siti Bahiroh

Abstract

Abstrak. Integrasi Kecerdasan Buatan (AI) dalam penilaian pendidikan menawarkan peluang untuk meningkatkan kualitas evaluasi dan hasil belajar, namun penerapannya pada pendidikan literasi Al-Qur'an masih terbatas. Penelitian ini bertujuan menguji implementasi platform penilaian berbasis AI, Q-Rate (ngaji.ai), dalam mendukung kualitas penilaian dan kemampuan literasi Al-Qur'an siswa. Penelitian menggunakan desain mixed methods kuasi-eksperimental dengan pendekatan one-group pretest-posttest yang melibatkan 30 siswa SMK dan tiga guru Pendidikan Agama Islam di Yogyakarta. Data dianalisis menggunakan statistik deskriptif, uji paired-samples t-test, ukuran efek, dan analisis tematik. Hasil menunjukkan skor literasi Al-Qur'an meningkat dari 28,40 menjadi 41,75 (47,01%), dengan perbedaan yang signifikan (t(29) = −8,75; p < 0,001) dan ukuran efek yang besar (Cohen's d = 1,20). Guru juga memberikan penilaian positif terhadap objektivitas, efisiensi, kualitas umpan balik, dan dokumentasi penilaian. Temuan ini menunjukkan bahwa penerapan penilaian berbasis AI berpotensi mendukung peningkatan kualitas evaluasi dan hasil belajar literasi Al-Qur'an. Namun, mengingat penggunaan desain one-group pretest-posttest tanpa kelompok kontrol, temuan ini perlu ditafsirkan secara hati-hati dan memerlukan konfirmasi melalui penelitian dengan desain eksperimental yang lebih kuat.

Abstrak: The integration of Artificial Intelligence (AI) in educational assessment offers opportunities to improve the quality of evaluation and learning outcomes, but its application to Qur'anic literacy education is still limited. This study aims to test the implementation of an AI-based assessment platform, Q-Rate (ngaji.ai), in supporting the quality of assessment and literacy skills of students. The study used a quasi-experimental mixed methods design with a one-group pretest-posttest approach involving 30 vocational school students and three Islamic Religious Education teachers in Yogyakarta. Data were analyzed using descriptive statistics, paired-samples t-test, effect size, and thematic analysis. The results showed that the Qur'an literacy score increased from 28.40 to 41.75 (47.01%), with a significant difference (t(29) = −8.75; p < 0.001) and a large effect size (Cohen's d = 1.20). Teachers also provide positive assessments of objectivity, efficiency, quality of feedback, and assessment documentation. These findings show that the application of AI-based assessments has the potential to support improving the quality of Qur'anic literacy evaluation and learning outcomes. However, given the use of a one-group pretest-posttest design without a control group, these findings need to be interpreted with caution and require confirmation through research with a more robust experimental design.

Keywords

Artificial Intelligence; Qur’anic Literacy Assessment; AI-Based Assessment; Islamic Education; Learning Outcomes; Educational Technology.

Full Text:

DOWNLOAD [PDF]

References

Alshammari, S. H., & Alkhwaldi, A. F. (2025). An integrated approach using social support theory and technology acceptance model to investigate the sustainable use of digital learning technologies. Scientific Reports, 15(1), 342.https://doi.org/10.1038/s41598-024-83450-z

Bennett, R. E. (2015). The changing nature of educational assessment. Review of Research in Education, 39(1), 370–407. https://doi.org/10.3102/0091732X14554179

Braun, V., & Clarke, V. (2022). Thematic analysis: A practical guide. SAGE, 1(33), 46–50. https://doi.org/10.53841/bpsqmip.2022.1.33.46

Campbell, S., Greenwood, M., Prior, S., Shearer, T., Walkem, K., Young, S., Bywaters, D., & Walker, K. (2020). Purposive sampling: Complex or simple? Research case examples. Journal of Research in Nursing, 25(8), 652–661. https://doi.org/10.1177/1744987120927206

Chen, X., Zou, D., Xie, H., Cheng, G., & Liu, C. (2022). Two decades of artificial intelligence in education: Contributors, collaborations, research topics, challenges, and future directions. Educational Technology & Society, 25(1), 28–47. https://doi.org/10.1007/s10639-022-11209-y

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates, 84(408), 1096. https://doi.org/10.2307/2290095

Cresswell, J. W. (2016). Revisiting Mixed Methods and Advancing Scientific Practices. In The Oxford Handbook of Multimethod and Mixed Methods Research Inquiry (pp. 57–71). Oxford Academic.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

Demartini, C. G., Sciascia, L., Bosso, A., & Manuri, F. (2024). Artificial intelligence bringing improvements to adaptive learning in education: A case study. Sustainability, 16(3), 1347. https://doi.org/10.3390/su16031347

Field, A. (2022). Discovering statistics using IBM SPSS Statistics (6th ed.). SAGE. https://doi.org/10.3389/fpsyt.2019.00864

George, B., & Wooden, O. (2023a). administrative sciences Managing the Strategic Transformation of Higher Education through Artificial Intelligence. Administrative Sciences, 13(196), 1–20. https://doi.org/10.3390/admsci13090196 Received:

George, B., & Wooden, O. (2023). Managing the strategic transformation of higher education through artificial intelligence. Administrative Sciences, 13(9), 196. https://doi.org/10.3390/admsci13090196

Giannini, S. (2023). Generative AI and the future of education. UNESCO. https://doi.org/10.54675/HOXG8740

Grosseck, G., Bran, R. A., & Țîru, L. G. (2023). Digital assessment: A survey of Romanian higher education teachers’ practices and needs. Education Sciences, 14(1), 32. https://doi.org/10.3390/educsci14010032

Hattie, J., & Timperley, H. (2017). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487

Hikmah, N. (2022). Implementation of learning to read writing the Quran (BTQ) Qutab system and their relevance in early children’s education. Edukasi Islami: Jurnal Pendidikan Islam, 11(1), 247–266. https://doi.org/10.30868/ei.v11i01.2252

Jurāne-Brēmane, A. (2023). Digital assessment in technology-enriched education: Thematic review. Education Sciences, 13(5), 522. https://doi.org/10.3390/educsci13050522

Kraft, M. A. (2020). Interpreting effect sizes of education interventions. Educational Researcher, 49(4), 241–253. https://doi.org/10.3102/0013189X20912798

Lin, V., Yeh, H.-C., & Chen, N.-S. (2022). A systematic review on oral interactions in robot-assisted language learning. Electronics, 11(2), 290. https://doi.org/10.3390/electronics11020290

Madjid, A., & Shodiq, S. F. (2026). Enhancing religious character through a technology-based learning model in boarding school: Examining the influence of wellbeing, critical thinking skills, and social skills as moderator variables. Frontiers in Education, 11, 1758690. https://doi.org/10.3389/feduc.2026.1758690

Mattar, J., Ramos, D. K., & Lucas, M. (2022). DigComp-based digital competence assessment tools: Literature review and instrument analysis. Education and Information Technologies, 27, 10843–10867. https://doi.org/10.1007/s10639-022-11034-3

Mubarok, A., Munawaroh, Saepudin, Mifatahudin, Sudrajat, S., & Rahmatullah, M. A. (2025). Efforts to improve the quality of Islamic religious education through the contextual teaching and learning (CTL) model in the Qur’an reading and writing (BTQ) material. Dirasah: Jurnal Pendidikan Islam, 6(2). https://doi.org/10.29210/810667700

Polit, D. F., & Beck, C. T. (2021). Nursing research: Generating and assessing evidence for nursing practice (11th ed.). Wolters Kluwer. https://doi.org/10.1093/eurjcn/zvab038

Redecker, C., & Punie, Y. (2017). European framework for the digital competence of educators: DigCompEdu. Publications Office of the European Union. https://doi.org/10.2760/159770

Salsabila, I. A., & Atikoh, N. (2024). Implementasi aplikasi “ngaji.ai” dalam pembelajaran Al-Qur’an. Sosaintek: Jurnal Ilmu Sosial Sains Dan Teknologi, 1(4), 265–272. https://doi.org/10.33367/sosaintek.v1i4.7318

Schoonenboom, J. (2024). Design patterns in mixed methods research. Journal of Mixed Methods Research, 18(3), 383-392.https://doi.org/10.1177/15586898241257284

Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality. Biometrika, 52(3–, 4, 591–611. https://doi.org/10.2307/2333709

Shodiq, S. F., & Makrufi, A. D. (2026). Empathy as a moderator among social competence, peer support, and adolescent responsibility in Indonesian adolescents: An empirical study. International Journal of Adolescence and Youth, 31(1), 2657347. https://doi.org/10.1080/02673843.2026.2657347

Sun, W. (2023). The impact of automatic speech recognition technology on second language pronunciation and speaking skills of EFL learners: A mixed methods investigation. Frontiers in psychology, 14, 1210187. https://doi.org/10.3389/fpsyg.2023.1210187

Wiliam, D. (2017). Assessment for learning: meeting the challenge of implementation. Assessment in Education: Principles, Policy & Practice, 25(6), 682–685. https://doi.org/10.1080/0969594x.2017.1401526

Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45(1), 166–183. https://doi.org/10.3102/0002831207312909

DOI: https://doi.org/10.31764/jmm.v10i4.40643