Vol.15, No.2, May 2026.                                                                                                                                                                          ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333

TEM Journal

TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS

Association for Information Communication Technology Education and Science

Machine Intelligence for Predictive, Adaptive Curriculum Design in Higher Education

Wasib Bin Latif, Ida Md Yasin, Mohammad Bin Amin, Gouranga Chandra Debnath, Md. Abdullah Al Mamun

© 2026 Wasib Bin Latif, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)

Citation Information: TEM Journal. Volume 15, Issue 2, Pages 1988-1998, ISSN 2217-8309, DOI: 10.18421/TEM152-86, May 2026.

Received: 04 June 2025.
Revised: 18 November 2025.
Accepted: 23 January 2026.
Published: 27 May 2026.

Abstract:

Higher education institutions face accelerating skills change, expanding digital delivery, and rising expectations for workforce relevance. This study examines how machine intelligence can support predictive–adaptive, data-driven curriculum design by integrating learning analytics, adaptive learning platforms, and institutional decision systems. Evidence from the analysis indicates that predictive models strengthen alignment between program outcomes and labor-market needs, while adaptive learning features improve student engagement and learning support. At the institutional level, analytics-enabled planning enhances resource allocation, training design, and operational efficiency. Qualitative insights further highlight perceived benefits (personalization, timely feedback, and transparency) alongside persistent barriers, including data quality constraints, implementation capacity, governance and ethics, and uneven access that may reinforce inequities if not addressed. The findings imply that effective adoption requires clear data governance, stakeholder readiness, faculty development, and equity-by-design safeguards. Overall, machine intelligence is positioned as an enabling infrastructure for continuous curriculum improvement when paired with responsible oversight and inclusive implementation.

Keywords – EduTech, machine intelligence, predictive analytics, adaptive learning, data-driven curriculum design.

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