Proceedings of the E-Learning and Smart Engineering Systems (ELSES 2024)

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AI for Multiple-Choice Tests Optimization: Towards Scores Regeneration Based on Effective Item Selection

Authors
Najoua Hrich1, 2, *, Charafeddin Elhaddouchi3, Mohamed Azekri4, Mohamed Khaldi1
1Research team in Computer Science and University Pedagogical Engineering, Tetouan, Morocco
2Higher Normal School, Abdelmalek Essaadi University, Tetouan, Morocco
3Regional Center of Education & Training Professions, Institutions for Higher Executive Training, Tangier, Morocco
4Regional Academy of Education & Training, Ministry of National Education Preschool and Sports, Tetouan, Morocco
*Corresponding author. Email: nhrich@uae.ac.ma
Corresponding Author
Najoua Hrich
Available Online 20 June 2025.
DOI
10.2991/978-2-38476-408-2_8How to use a DOI?
Keywords
Artificial Intelligence; Item Response Theory; Deep learning; Artificial Neural Network; Assessment; Multiple Choice Tests; Item Analysis
Abstract

In the assessment field, the synergy between artificial intelligence (AI) and item response theory (IRT) opens new revolutionary perspectives. AI brings unprecedented data analysis and processing capabilities, while IRT provides a robust theoretical framework for modeling learners’ responses to test items. This combination enables addressing the complex challenges of assessment more effectively and precisely than ever before.

This research explores the application of IRT in conjunction with AI in the assessment area. Focusing on score regeneration following the removal of improper items, our study employs AI to effectively identify and remove such items, thereby influencing candidate selection. Using key parameters of IRT, such as difficulty, discrimination, and chance, this research aims to develop a deep learning (DL) model for classifying and selecting items. The study specifically applies the Artificial Neural Network (ANN) method via binary classification models for this task. Experimental results show that the proposed model performs very well, with a high accuracy rate for item selection.

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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Volume Title
Proceedings of the E-Learning and Smart Engineering Systems (ELSES 2024)
Series
Atlantis Highlights in Social Sciences, Education and Humanities
Publication Date
20 June 2025
ISBN
978-2-38476-408-2
ISSN
2667-128X
DOI
10.2991/978-2-38476-408-2_8How to use a DOI?
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.

Cite this article

risenwbib
TY  - CONF
AU  - Najoua Hrich
AU  - Charafeddin Elhaddouchi
AU  - Mohamed Azekri
AU  - Mohamed Khaldi
PY  - 2025
DA  - 2025/06/20
TI  - AI for Multiple-Choice Tests Optimization: Towards Scores Regeneration Based on Effective Item Selection
BT  - Proceedings of the E-Learning and Smart Engineering Systems (ELSES 2024)
PB  - Atlantis Press
SP  - 88
EP  - 98
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-2-38476-408-2_8
DO  - 10.2991/978-2-38476-408-2_8
ID  - Hrich2025
ER  -