Assessing AI Integration in Islamic Higher Education: A Mixed-Methods Fishbone Diagram Analysis
pdf

Keywords

Artificial Intelligence's adoption
Artificial Intelligence's impact
effectiveness of learning
efficiency of learning
ethical AI policies

How to Cite

Aan Ansori, Damyati, F., & Dhestyani, S. A. (2025). Assessing AI Integration in Islamic Higher Education: A Mixed-Methods Fishbone Diagram Analysis. IJID (International Journal on Informatics for Development), 13(2), 504–516. https://doi.org/10.14421/ijid.2024.4862

Abstract

The integration of Artificial Intelligence in higher education has shown significant potential to improve the efficiency and effectiveness of learning. The strategic implementation of AI in Indonesian State Islamic Higher Education Institutions fosters innovative pedagogy and improved academic performance. This study employs the Fishbone Diagram approach to systematically analyze Artificial Intelligence's impact on Indonesian State Islamic Higher Education Institutions education, identifying key factors influencing implementation. The method employs a reverse-cause analysis, mapping factors contributing to a primary issue, and identifying underlying causes and sub-factors. Findings highlight the crucial roles of technological infrastructure, human resource readiness, supportive policies, adaptive curriculum design, and organizational culture. This study underscores the necessity of integrated AI adoption frameworks in Indonesian Islamic higher education, harmonizing technological advancement with Islamic pedagogical principles. This study offers a foundational framework guiding Indonesian State Islamic Higher Education Institutions in developing sustainable and ethical AI policies. Comprehensive AI policies and strategies are essential for PTKIN to harmonize innovation with Islamic principles.

https://doi.org/10.14421/ijid.2024.4862
pdf

References

S. Paek and N. Kim, “Analysis of worldwide research trends on the impact of artificial intelligence in education,” Sustainability, vol. 13, no. 14, p. 7941, 2021.

H. Lee, “The rise of ChatGPT: Exploring its potential in medical education,” Anat. Sci. Educ., vol. 17, no. 5, pp. 926–931, 2024.

B. Lepri, N. Oliver, E. Letouzé, A. Pentland, and P. Vinck, “Fair, transparent, and accountable algorithmic decision-making processes: The premise, the proposed solutions, and the open challenges,” Philos. Technol., vol. 31, no. 4, pp. 611–627, 2018.

N. Moustafa, N. Koroniotis, M. Keshk, A. Y. Zomaya, and Z. Tari, “Explainable intrusion detection for cyber defences in the internet of things: Opportunities and solutions,” IEEE Commun. Surv. Tutorials, vol. 25, no. 3, pp. 1775–1807, 2023.

D. D. Shinde, S. Ahirrao, and R. Prasad, “Fishbone diagram: application to identify the root causes of student–staff problems in technical education,” Wirel. Pers. Commun., vol. 100, pp. 653–664, 2018.

P. Clarke and R. V O’connor, “The situational factors that affect the software development process: Towards a comprehensive reference framework,” Inf. Softw. Technol., vol. 54, no. 5, pp. 433–447, 2012.

M. Peleg and P. Haug, “Guidelines and workflow models,” in Clinical Decision Support and Beyond, Elsevier, 2023, pp. 309–348.

E. M. Loredana, “The analysis of causes and effects of a phenomenon by means of the ‘fishbone’ diagram,” Ann Econ Ser, vol. 5, pp. 97–103, 2017.

M. Coccia, “Fishbone diagram for technological analysis and foresight,” Int. J. Foresight Innov. Policy, vol. 14, no. 2–4, pp. 225–247, 2020.

A. Mascia et al., “A failure mode and effect analysis (FMEA)-based approach for risk assessment of scientific processes in non-regulated research laboratories,” Accredit. Qual. Assur., vol. 25, pp. 311–321, 2020.

L. A. Clark and D. Watson, “Constructing validity: Basic issues in objective scale development.,” 2016.

Z. Xu, Y. Dang, and P. Munro, “Knowledge-driven intelligent quality problem-solving system in the automotive industry,” Adv. Eng. Informatics, vol. 38, pp. 441–457, 2018.

S. N. Siddiki, J. L. Carboni, C. Koski, and A. Sadiq, “How policy rules shape the structure and performance of collaborative governance arrangements,” Public Adm. Rev., vol. 75, no. 4, pp. 536–547, 2015.

F. M. S. Al-Zwainy, I. A. Mohammed, and I. F. Varouqa, “Diagnosing the causes of failure in the construction sector using root cause analysis technique,” J. Eng., vol. 2018, no. 1, p. 1804053, 2018.

S. W. J. Kozlowski and B. S. Bell, “Work groups and teams in organizations: Review update,” 2013.

A. Shaygan and Ö. M. Testik, “A fuzzy AHP-based methodology for project prioritization and selection,” Soft Comput., vol. 23, pp. 1309–1319, 2019.

F. Lamnabhi-Lagarrigue et al., “Systems & control for the future of humanity, research agenda: Current and future roles, impact and grand challenges,” Annu. Rev. Control, vol. 43, pp. 1–64, 2017.

F. Pakdil and F. Pakdil, “Analyze Phase: Other Data Analysis Tools,” Six Sigma Students A Probl. Methodol., pp. 291–331, 2020.

M. J. Ershadi, R. Aiasi, and S. Kazemi, “Root cause analysis in quality problem solving of research information systems: a case study,” Int. J. Product. Qual. Manag., vol. 24, no. 2, pp. 284–299, 2018.

A. Banawi and M. M. Bilec, “A framework to improve construction processes: Integrating Lean, Green and Six Sigma,” Int. J. Constr. Manag., vol. 14, no. 1, pp. 45–55, 2014.

M. M. Mandelburger and J. Mendling, “Cognitive diagram understanding and task performance in systems analysis and design,” MIS Q., vol. 45, no. 4, pp. 2101–2157, 2021.

S. A. Khan, M. A. Kaviani, B. J. Galli, and P. Ishtiaq, “Application of continuous improvement techniques to improve organization performance: A case study,” Int. J. Lean Six Sigma, vol. 10, no. 2, pp. 542–565, 2019.

D. Korać and D. Simić, “Fishbone model and universal authentication framework for evaluation of multifactor authentication in mobile environment,” Comput. Secur., vol. 85, pp. 313–332, 2019.

L. Liliana, “A new model of Ishikawa diagram for quality assessment,” in Iop conference series: Materials science and engineering, 2016, vol. 161, no. 1, p. 12099.

K. Berladir, T. Hovorun, J. Trojanowska, V. Ivanov, and A. Iakovets, “Failure Analytics of Defects in Mechanical Engineering Parts Using Root Cause Analysis: Case Study,” in International Scientific-Technical Conference MANUFACTURING, 2024, pp. 328–341.

B. Mahapatro, Human resource management. New Age International (P) ltd., 2021.

Z. Shan, S. Qin, Q. Liu, and F. Liu, “Key manufacturing technology & equipment for energy saving and emissions reduction in mechanical equipment industry,” Int. J. Precis. Eng. Manuf., vol. 13, pp. 1095–1100, 2012.

W. Van Der Aalst and K. M. Van Hee, Workflow management: models, methods, and systems. MIT press, 2004.

K. I. Praseeda, B. V. V. Reddy, and M. Mani, “Embodied energy assessment of building materials in India using process and input–output analysis,” Energy Build., vol. 86, pp. 677–686, 2015.

K. Keong Choong, “Understanding the features of performance measurement system: a literature review,” Meas. Bus. Excell., vol. 17, no. 4, pp. 102–121, 2013.

J. A. Ramírez, R. L. Boroschek, R. Aguilar, and C. E. Ventura, “Daily and seasonal effects of environmental temperature and humidity on the modal properties of structures,” Bull. Earthq. Eng., ol. 20, no. 9, pp. 4533–4559, 2022.

R. Chambers, Ideas for development. Routledge, 2013.

R. E. Wood, “Task complexity: Definition of the construct,” Organ. Behav. Hum. Decis. Process., vol. 37, no. 1, pp. 60–82, 1986.

A. Booth, M.-S. James, M. Clowes, and A. Sutton, “Systematic approaches to a successful literature review,” 2021.

R. Sagor, The action research guidebook: A four-stage process for educators and school teams. Corwin Press, 2011.

T. H. Poister, M. P. Aristigueta, and J. L. Hall, Managing and measuring performance in public and nonprofit organizations: An integrated approach. John Wiley & Sons, 2014.

W. J. Kettinger, J. T. C. Teng, and S. Guha, “Business process change: a study of methodologies, techniques, and tools,” MIS Q., pp. 55–80, 1997.

O. Enaizan et al., “Electronic medical record systems: Decision support examination framework for individual, security and privacy concerns using multi-perspective analysis,” Health Technol. (Berl)., vol. 10, pp. 795–822, 2020.

J. N. Davies, M. Verovko, O. Verovko, and I. Solomakha, “Personalization of e-learning process using ai-powered chatbot integration,” in International scientific-practical conference, 2020, pp. 209–216.

D. Sargiotis, “Data Security and Privacy: Protecting Sensitive Information,” in Data Governance: A Guide, Springer, 2024, pp. 217–245.

A. Ali, “Assessing Artificial Intelligence Readiness of Faculty in Higher Education: Comparative Case Study of Egypt.” The American University in Cairo (Egypt), 2023.

I. Gligorea, M. Cioca, R. Oancea, A.-T. Gorski, H. Gorski, and P. Tudorache, “Adaptive learning using

artificial intelligence in e-learning: a literature review,” Educ. Sci., vol. 13, no. 12, p. 1216, 2023.

A. Ezzaim, A. Dahbi, A. Aqqal, and A. Haidine, “AI-based learning style detection in adaptive learning systems: a systematic literature review,” J. Comput. Educ., pp. 1–39, 2024.

S. Hernawati, M. Hafizh, and M. N. A. Rahardja, “Adjusting the ideal Islamic religious education curriculum to the development of AI-based technology,” Progres. J. Pemikir. dan Pendidik. Islam, vol. 13, no. 01, pp. 129–144, 2024.

A. Nguyen, H. N. Ngo, Y. Hong, B. Dang, and B.-P. T. Nguyen, “Ethical principles for artificial intelligence in education,” Educ. Inf. Technol., vol. 28, no. 4, pp. 4221–4241, 2023.

N. Miailhe, C. Hodes, A. Jain, N. Iliadis, S. Alanoca, and J. Png, “AI for sustainable development goals,” Delphi, vol. 2, p. 207, 2019.

B. P. Woolf, Building intelligent interactive tutors: Student-centered strategies for revolutionizing e-learning. Morgan Kaufmann, 2010.

L. Chen, P. Chen, and Z. Lin, “Artificial intelligence in education: A review,” Ieee Access, vol. 8, pp. 75264–75278, 2020.

C. Bailey, D. Mankin, C. Kelliher, and T. Garavan, Strategic human resource management. Oxford university press, 2018.

R. Luckin and M. Cukurova, “Designing educational technologies in the age of AI: A learning sciences‐driven approach,” Br. J. Educ. Technol., vol. 50, no. 6, pp. 2824–2838, 2019.

Y.-A. Bachiri, H. Mouncif, and B. Bouikhalene, “Artificial intelligence empowers gamification: Optimizing student engagement and learning outcomes in e-learning and moocs.,” Int. J. Eng. Pedagog., vol. 13, no. 8, 2023.

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.