Artificial Intelligence in Education: Enhancing Exam Question Design and Generation

Anas A. Balla , Hussein A. A. Ghanim , Ibrahim Abdallah Hageltoum , Salwa Idris

The rapid expansion of digital education has created an urgent demand for scalable, high-quality assessment design. Traditional exam question authoring is time-consuming, inconsistent, and difficult to adapt to large and diverse learner populations. This paper proposes and analyzes an AI-driven framework for automated exam question generation that integrates Large Language Models, Natural Language Processing, and pedagogical intelligence. Unlike existing Automated Question Generation systems, the proposed framework explicitly aligns generated questions with Bloom’s Taxonomy, incorporates human-in-the-loop validation, and embeds explainability and fairness checks as core design components. The framework supports multiple question formats including multiple-choice, short-answer, and coding questions while enabling adaptive difficulty adjustment. A conceptual evaluation compares AI-generated questions with expert-authored items in terms of validity, reliability, and efficiency, demonstrating substantial reductions in instructor workload without compromising pedagogical quality. This work advances AI-enabled assessment by presenting a modular, explainable, and educator-controlled system suitable for large-scale and adaptive educational environments.

Published Paper   E-Certificate

"Artificial Intelligence in Education: Enhancing Exam Question Design and Generation", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.11, Issue 1, page no.b90-b98, January-2026, Available :https://ijsdr.org/papers/IJSDR2601110.pdf

Volume 11 Issue 1, January-2026

Pages : b90-b98

Paper Reg. ID: IJSDR_306945

Published Paper Id: IJSDR2601110

Downloads: 000300

Research Area: Science and Technology

Country: Kassala, Kassala, Sudan

Published Paper PDF: https://ijsdr.org/papers/IJSDR2601110

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2601110

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition