Optimizing English Language Evaluation: A Hybrid AI Framework Using Neural Networks and Fuzzy Decision-Making Models

Aroosha; Farhat Mehmood; Bilal Khan; Komail Lodhi

Author(s)

Aroosha Department of English, University of Gujrat, Hafiz Hayat Campus, Gujrat 50800, Pakistan

Farhat Mehmood Department of computer science, University of Gujrat Hafiz Hayat campus, Gujrat 50800, Pakistan

Bilal Khan U.S.-Pakistan Center for Advanced Studies in Energy USPCASE, UET, Peshawar, 25000, Pakistan

Komail Lodhi Brightside Hall School, Peshawar 25000, Pakistan

Abstract

One of the most important aspects of educational evaluation is the accurate assessment of English language ability. This is especially crucial for ensuring that evaluations are fair and impartial. To improve the evaluation process of final scores for Iranian English as a Foreign Language (EFL) students enrolled in a reading comprehension course, this research proposes a hybrid Artificial Intelligence (AI) framework that incorporates both Neuro-Fuzzy Systems (NFS) and Artificial Neural Networks (ANN). The performance records of 66 students are included in the dataset, comprising scores for midterms, quizzes, finals, class participation, and bonus components. A neuro-fuzzy system, along with two-layer and three-layer ANN models, is used to train the hybrid framework to forecast students' final marks. These predictions are compared with scores assigned by instructors. Even though the three-layer ANN demonstrated higher accuracy than the two-layer version, the findings showed that NFS produced predictions most closely aligned with the aggregated instructor scores. These results highlight the potential of hybrid AI frameworks to enhance objectivity and reduce bias in academic evaluations. Overall, this study demonstrates that combining ANN with fuzzy decision-making models can improve intelligent scoring techniques, optimize evaluation procedures, and promote fairness in language assessment.

Publication Details

Page(s) 188-205

DOI DOI not available

Published Journal: The Sciencetech, Volume: 6, Issue: 2, Year: 2025

Request the full text from PASTIC

Keywords

Intelligent Scoring, Neuro Fuzzy System, Hybrid AI Architecture, Fuzzy Inference System, English Language Evaluation, artificial neural networks, Educational Data Mining

References

Afzal S. .2024 .Building energy consumption prediction and optimization using different neural network-assisted models; comparison of different networks and optimization algorithms. Engineering Applications of Artificial Intelligence, 127 : 107356.

Ahajjam S.,Alami A. .2022 .Predicting students' academic performance using machine learning techniques. Procedia Computer Science, 192 : 3471-3480.

Akiner M. E.,Ghasri M. .2024 .Comparative assessment of deep belief network and hybrid adaptive neuro-fuzzy inference system model based on a meta-heuristic optimization algorithm for precise predictions of the potential evapotranspiration. Environmental Science and Pollution Research., : .

Aljohani M. .2024 .An automated metaheuristic-optimized approach for diagnosing and classifying brain tumors based on a convolutional neural network. , 23 : 102459.

Alqaysi M. E.,Albahri A. S.,Hamid R. A. .2024 .Evaluation and benchmarking of hybrid machine learning models for autism spectrum disorder diagnosis using a 2-tuple linguistic neutrosophic fuzzy sets-based decision-making model. Neural Computing and Applications, 36(29) : 18161-18200.

Farangi M.,Zabbah I. .2023 .Intelligent scoring in an English reading comprehension course using artificial neural networks and neurofuzzy systems. Teaching English as a Second Language Quarterly, 42(4) : 1-21.

Fernandes E.,Holanda M.,Victorino M.,Borges V.,Carvalho R.,G. R. .2019 .Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil. Journal of Business Research, 94 : 335-343.

Yildiz Goktepe,Goktepe Korpeoglu S.,S. S. .2023 .Prediction of students' perceptions of problem-solving skills with a neuro-fuzzy model and hierarchical regression method: A quantitative study. Education and Information Technologies, 28(7) : 8879-8917.

Haq I. U.,Mohiz A.,Spadafora G.,Palermo A. M.,Bilotta E.,Liccardo A.,Lamonaca F. .2025 .The role of network of extended reality-enabled laboratories in enhancing STEM education: Bridging theory and practice in the digital classroom. , : 362-372.

Haq I. U.,Mohiz A.,Spadafora G.,Palermo A. M.,Bilotta E.,Felicetti F.,Lamonaca F. .2025 .AI in the network of extended realityenabled laboratories for STEM education: Current applications and future potential for adaptive learning. In International Workshop IFToMM for Sustainable Development Goals, : 373-382.

Iatrellis O.,Kotsiantis S.,Pintelas P. .2021 .Predicting students' graduation time using machine learning techniques: A case study. Education and Information Technologies, 26 : 69-88.

Li Q.,Liu M.,Zhao M.,Tang X. .2022 .Deep learning applications in predicting student performance: A case study in higher education. Educational Data Mining Journal, 14(1) : 22-39.

Li S.,Wang C.,Wang Y. .2024 .Fuzzy evaluation model for physical education teaching methods in colleges and universities using artificial intelligence. Scientific Reports, 14(1) : 4788.

Mahmood Y. .2024 .Optimizing natural gas pipeline risk assessment using hybrid fuzzy Bayesian networks and expert elicitation for effective decision-making strategies. Gas Science and Engineering, 125 : 205283.

Mehdi S.,Nachouki M. .2022 .Predicting students' grade point average using adaptive neuro-fuzzy inference systems (ANFIS). International Journal of Artificial Intelligence in Education, 32 : 193-215.

Mhady A. A.,Budayan C.,Gurgun A. P. .2024 .Estimate-atcompletion (EAC) prediction using Archimedes optimization with adaptive fuzzy and neural networks. Automation in Construction, 166 : 105653.

Nor Mohd,Mansor N. H.,Hasim N. I.,N. A. N. I. .2024 .Artificial neural networks: A new frontier in dental tissue regeneration. , : .

Mumali F.,Kalkowska J. .2024 .Intelligent support in manufacturing process selection based on artificial neural networks, fuzzy logic, and genetic algorithms: Current state and future perspectives. Computers & Industrial Engineering, 110272 : .

Ramesh B.,Lakshmanna K. .2024 .A novel early detection and prevention of coronary heart disease framework using hybrid deep learning model and neural fuzzy inference system. IEEE Access, 12 : 26683-26695.

Saa A.,Al-Emran M.,Shaalan K. .2020 .An artificial neural network model for predicting student performance in a private university in the UAE. International Journal of Emerging Technologies in Learning (iJET), 15(15) : 4-20.

Saville J. D.,Foster L. L. .2021 .Does technology self-efficacy influence the effect of training presentation mode on training selfefficacy? Computers in Human Behavior Reports. , 4 : 100124.

Sharafi M. .2024 .Development of long short-term memory along with differential optimization and neural networks for coagulant dosage prediction in water treatment plant. Journal of Water Process Engineering, 65 : 105784.

Son J.,Fujita H. .2019 .A MANFIS-S model for predicting university students' future performance. Knowledge-Based Systems, 178 : 151-166.

Tomasevic N.,Gasevic D.,Loughin T. M. .2020 .Impact of data preprocessing on the performance of predictive models for early identification of students at risk of failing a course. Computers & Education, 144 : 103706.

Uskov V. L.,Bakken J. P.,Byerly A. .2019 .Machine learning in STEM education: Research and applications. Advances in Intelligent Systems and Computing, 950 : 225-241.

Xie B. .2024 .An integrated framework for spherical fuzzy MAGDM and applications to English blended teaching quality evaluation. Journal of Intelligent & Fuzzy Systems. (Preprint), : 1-17.

Zerouali B. .2024 .Enhancing deep learning-based slope stability classification using a novel metaheuristic optimization algorithm for feature selection. Scientific Reports, 14(1) : 21812.