A Comprehensive Review on AI-Driven Outcomes-Based Education and Curriculum Design

DOI

10.2991/978-94-6463-978-0_45How to use a DOI?

Keywords

Outcome-Based Education (OBE); Artificial Intelligence (AI); Curriculum Optimization; Predictive Analytics; Adaptive Learning Systems; Higher Education; Accreditation

Abstract

In order to sustain predetermined competencies, the international tendency towards the Outcome-Based Education (OBE), people need to transform radically the curriculum design, its implementation, and assessment ensuring that the graduates have acquired the predetermined competence levels. Despite the traditional OBE models providing an orderly approach regarding results mapping and achievement calculation, they are likely to be subject to manual tasks, rigid alignment and forecasting and customized support. The utilization of Artificial Intelligence (AI) and semantic technologies gives a chance to surpass those constraints radically. Towards this goal, this paper presents the synthesis of the latest state-of-the-art on intersection of OBE, ontological modeling, and AI in higher education through a systematic literature review of 25 classic works. The survey indicates that there is a clear tendency because the initial research has formed the fundamental principles of OBE and manual achievement models that were enhanced and turned into ontology-based semantic representations of curriculum and syllabus that enabled the appearance of machine-readable knowledge structures. A fundamental lack is found, however, between these descriptive, structural models, and the prescriptive, intelligent systems needed in dynamic optimization and personalization. The current approach is mainly post-hoc-based analysis as opposed to predictive forecasting or adaptive intervention. It is grounded on four key literature domains, that is,

• OBE Principles and Attainment Computation,

• Semantic Modeling and Ontologies,

• Curriculum Mapping and Alignment Tools,

• Early Intelligent Systems. Based on this analysis,

the research structure will be concluded with an investigation on the establishment of a hybrid AI-based OBE framework in the paper. The suggested system consists of a predictive analytic predict to achieve engine, an AI-based curriculum optimization platform, and personalized assessment system which is based on adaptive learning. In this survey, future development in OBE is the application of AI to create responsive evidence-based ecosystems that can raise the level of accreditation preparedness, improve the quality of learning, and provide individual learning paths at scale.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Manoj Bapurao Shinde
AU  - Ganesh R. Pathak
PY  - 2025
DA  - 2025/12/31
TI  - A Comprehensive Review on AI-Driven Outcomes-Based Education and Curriculum Design
BT  - Proceedings of the 1st Engineering Data Analytics and Management Conference (EAMCON 2025)
PB  - Atlantis Press
SP  - 529
EP  - 536
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-978-0_45
DO  - 10.2991/978-94-6463-978-0_45
ID  - Shinde2025
ER  -