{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T15:21:14Z","timestamp":1762183274383,"version":"build-2065373602"},"reference-count":155,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T00:00:00Z","timestamp":1762128000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>Evaluating text readability is crucial for supporting both language learners and native readers in selecting appropriate materials. Cognitive psychology research, leveraging behavioral data such as eye-tracking and electroencephalogram (EEG) signals, has demonstrated effectiveness in identifying cognitive activities associated with text difficulty during reading. However, the distinctive linguistic characteristics of Arabic present unique challenges for applying such data in readability assessments. While behavioral signals have been explored for this purpose, their potential for Arabic remains underutilized. This study aims to advance Arabic readability assessments by integrating eye-tracking features into computational models. It presents a series of experiments that utilize both text-based and gaze-based features within machine learning (ML) and deep learning (DL) frameworks. The gaze-based features were extracted from the AraEyebility corpus, which contains eye-tracking data collected from 15 native Arabic speakers. The experimental results show that ensemble ML models, particularly AdaBoost with linguistic and eye-tracking handcrafted features, outperform ML models using TF-IDF and DL models employing word embedding vectorization. Among the DL models, convolutional neural networks (CNNs) achieved the best performance with combined linguistic and eye-tracking features. These findings underscore the value of cognitive data and emphasize the need for exploration to fully realize its potential in Arabic readability assessment.<\/jats:p>","DOI":"10.3390\/computation13110258","type":"journal-article","created":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T13:55:22Z","timestamp":1762178122000},"page":"258","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrating Linguistic and Eye Movements Features for Arabic Text Readability Assessment Using ML and DL Models"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7531-0666","authenticated-orcid":false,"given":"Ibtehal","family":"Baazeem","sequence":"first","affiliation":[{"name":"Artificial Intelligence and Robotics Institute, King Abdulaziz City for Science and Technology, Riyadh 13523, Saudi Arabia"},{"name":"College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7328-4935","authenticated-orcid":false,"given":"Hend","family":"Al-Khalifa","sequence":"additional","affiliation":[{"name":"College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5874-2611","authenticated-orcid":false,"given":"Abdulmalik","family":"Al-Salman","sequence":"additional","affiliation":[{"name":"College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Berrichi, S., Nassiri, N., Mazroui, A., and Lakhouaja, A. (2024). Exploring the Impact of Deep Learning Techniques on Evaluating Arabic L1 Readability. Artificial Intelligence, Data Science and Applications, Springer Nature.","DOI":"10.1007\/978-3-031-48573-2_1"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1177\/23727322231218891","article-title":"Reading Comprehension and Constructive Learning: Policy Considerations in the Age of Artificial Intelligence","volume":"11","author":"McCarthy","year":"2024","journal-title":"Policy Insights Behav. Brain Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Sanches, C.L., Augereau, O., and Kise, K. (2017, January 9\u201315). Using the Eye Gaze to Predict Document Reading Subjective Understanding. Proceedings of the 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), Kyoto, Japan.","DOI":"10.1109\/ICDAR.2017.377"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1007\/s11257-022-09346-7","article-title":"Gaze-based predictive models of deep reading comprehension","volume":"33","author":"Southwell","year":"2023","journal-title":"User Model. User-Adapt. Interact."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Biedert, R., Dengel, A., Elshamy, M., and Buscher, G. (2012, January 28\u201330). Towards robust gaze-based objective quality measures for text. Proceedings of the Symposium on Eye Tracking Research and Applications, Santa Barbara, CA, USA.","DOI":"10.1145\/2168556.2168593"},{"key":"ref_6","unstructured":"Balyan, R., McCarthy, K.S., and McNamara, D.S. (2018, January 21\u201323). Comparing Machine Learning Classification Approaches for Predicting Expository Text Difficulty. Proceedings of the Thirty-First International Flairs Conference, Melbourne, FL, USA."},{"key":"ref_7","first-page":"103","article-title":"Automatic readability measurements of the Arabic text: An exploratory study","volume":"35","year":"2010","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Nassiri, N., Lakhouaja, A., and Cavalli-Sforza, V. (2017, January 11\u201312). Modern Standard Arabic Readability Prediction. Proceedings of the Arabic Language Processing: From Theory to Practice (ICALP 2017), Fez, Morocco.","DOI":"10.1007\/978-3-319-73500-9_9"},{"key":"ref_9","unstructured":"Feng, L., Elhadad, N.M., and Huenerfauth, M. (April, January 30). Cognitively motivated features for readability assessment. Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics, Athens, Greece."},{"key":"ref_10","first-page":"19","article-title":"The Concept of Readability","volume":"26","author":"Dale","year":"1949","journal-title":"Elem. Engl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.procs.2016.04.017","article-title":"Readability of Arabic Medicine Information Leaflets: A Machine Learning Approach","volume":"82","author":"Alotaibi","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_12","first-page":"370","article-title":"AARI: Automatic Arabic readability index","volume":"11","author":"Jaradat","year":"2014","journal-title":"Int. Arab J. Inf. Technol."},{"key":"ref_13","unstructured":"Baazeem, I. (2015). Analysing the Effects of Latent Semantic Analysis Parameters on Plain Language Visualisation. [Master\u2019s Thesis, Queensland University]."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1075\/itl.165.2.01col","article-title":"Computational assessment of text readability: A survey of current and future research","volume":"165","year":"2014","journal-title":"ITL Int. J. Appl. Linguist."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mesgar, M., and Strube, M. (2015, January 4\u20135). Graph-based coherence modeling for assessing readability. Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics, Denver, CO, USA.","DOI":"10.18653\/v1\/S15-1036"},{"key":"ref_16","unstructured":"Balakrishna, S.V. (2015). Analyzing Text Complexity and Text Simplification: Connecting Linguistics, Processing and Educational Applications. [Ph.D. Thesis, der Eberhard Karls Universit\u00e4t T\u00fcbingen]."},{"key":"ref_17","unstructured":"Vajjala, S., Meurers, D., Eitel, A., and Scheiter, K. (2016, January 11). Towards grounding computational linguistic approaches to readability: Modeling reader-text interaction for easy and difficult texts. Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC), Osaka, Japan."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Vajjala, S., and Lucic, I. (2019, January 2). On understanding the relation between expert annotations of text readability and target reader comprehension. Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, Florence, Italy.","DOI":"10.18653\/v1\/W19-4437"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Sarti, G., Brunato, D., and Dell\u2019Orletta, F. (2021, January 10). That looks hard: Characterizing linguistic complexity in humans and language models. Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, Virtual.","DOI":"10.18653\/v1\/2021.cmcl-1.5"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/TPAMI.2023.3321337","article-title":"Automatic Gaze Analysis: A Survey of Deep Learning Based Approaches","volume":"46","author":"Ghosh","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","unstructured":"Mathias, S., Kanojia, D., Mishra, A., and Bhattacharyya, P. (2021, January 7\u201315). A Survey on Using Gaze Behaviour for Natural Language Processing. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20) Survey Track, Yokohama, Japan."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1037\/0033-295X.87.4.329","article-title":"A theory of reading: From eye fixations to comprehension","volume":"87","author":"Just","year":"1980","journal-title":"Psychol. Rev."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1016\/j.procs.2017.01.162","article-title":"Eye movement analyses for obtaining Readability Formula for Latvian texts for primary school","volume":"104","author":"Atvars","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"ref_24","unstructured":"Chen, Y., Zhang, W., Song, D., Zhang, P., Ren, Q., and Hou, Y. (2015, January 2). Inferring Document Readability by Integrating Text and Eye Movement Features. Proceedings of the SIGIR2015 Workshop on Neuro-Physiological Methods in IR Research, Santiago, Chile."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Copeland, L., Gedeon, T., and Caldwell, S. (2015, January 19\u201321). Effects of text difficulty and readers on predicting reading comprehension from eye movements. Proceedings of the 6th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), Gyor, Hungary.","DOI":"10.1109\/CogInfoCom.2015.7390628"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Garain, U., Pandit, O., Augereau, O., Okoso, A., and Kise, K. (2017, January 9\u201315). Identification of reader specific difficult words by analyzing eye gaze and document content. Proceedings of the 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), Kyoto, Japan.","DOI":"10.1109\/ICDAR.2017.221"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Baazeem, I., Al-Khalifa, H., and Al-Salman, A. (2021). Cognitively Driven Arabic Text Readability Assessment Using Eye-Tracking. Appl. Sci., 11.","DOI":"10.3390\/app11188607"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Al-Ajlan, A.A., Al-Khalifa, H.S., and Al-Salman, A.S. (2008, January 13\u201316). Towards the development of an automatic readability measurements for Arabic language. Proceedings of the Third International Conference on Digital Information Management, London, UK.","DOI":"10.1109\/ICDIM.2008.4746711"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Saddiki, H., Bouzoubaa, K., and Cavalli-Sforza, V. (2015, January 17\u201320). Text readability for Arabic as a foreign language. Proceedings of the 2015 IEEE\/ACS 12th International Conference of Computer Systems and Applications (AICCSA), Marrakech, Morocco.","DOI":"10.1109\/AICCSA.2015.7507232"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"114037","DOI":"10.1016\/j.eswa.2020.114037","article-title":"Eye tracking algorithms, techniques, tools, and applications with an emphasis on machine learning and Internet of Things technologies","volume":"166","author":"Klaib","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_31","first-page":"1","article-title":"Human eye tracking and related issues: A review","volume":"2","author":"Singh","year":"2012","journal-title":"Int. J. Sci. Res. Publ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1177\/0267658316637401","article-title":"Using eye-tracking in applied linguistics and second language research","volume":"32","author":"Conklin","year":"2016","journal-title":"Second Lang. Res."},{"key":"ref_33","unstructured":"(2021, November 20). Tobii. Available online: https:\/\/www.tobii.com."},{"key":"ref_34","unstructured":"(2021, March 31). SR Research EyeLink. Available online: https:\/\/www.sr-research.com."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Al-Edaily, A., Al-Wabil, A., and Al-Ohali, Y. (2013, January 21\u201326). Interactive Screening for Learning Difficulties: Analyzing Visual Patterns of Reading Arabic Scripts with Eye Tracking. Proceedings of the HCI International 2013\u2014Posters\u2019 Extended Abstracts, Las Vegas, NV, USA.","DOI":"10.1007\/978-3-642-39476-8_1"},{"key":"ref_36","unstructured":"Tobii Technology AB (2023, December 16). What Is Eye Tracking?. Available online: https:\/\/www.tobii.com\/learn-and-support\/get-started\/what-is-eye-tracking."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"602","DOI":"10.3758\/s13428-016-0734-0","article-title":"Presenting GECO: An eyetracking corpus of monolingual and bilingual sentence reading","volume":"49","author":"Cop","year":"2017","journal-title":"Behav. Res. Methods"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Gonzalez-Garduno, A.V., and S\u00f8gaard, A. (2017, January 8). Using gaze to predict text readability. Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, Copenhagen, Denmark.","DOI":"10.18653\/v1\/W17-5050"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Grabar, N., Farce, E., and Sparrow, L. (2018, January 8). Study of readability of health documents with eye-tracking approaches. Proceedings of the 1st Workshop on Automatic Text Adaptation (ATA), Tilburg, The Netherlands.","DOI":"10.18653\/v1\/W18-7003"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Mathias, S., Kanojia, D., Patel, K., Agarwal, S., Mishra, A., and Bhattacharyya, P. (2018, January 15\u201320). Eyes are the windows to the soul: Predicting the rating of text quality using gaze behaviour. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Long Papers), Melbourne, Australia.","DOI":"10.18653\/v1\/P18-1219"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1037\/xhp0000032","article-title":"Processing of Arabic diacritical marks: Phonological\u2013syntactic disambiguation of homographic verbs and visual crowding effects","volume":"41","author":"Hermena","year":"2015","journal-title":"J. Exp. Psychol. Hum. Percept. Perform."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1007\/s12144-019-00493-6","article-title":"Reading text with and without diacritics alters brain activation: The case of Arabic","volume":"39","author":"Sarsam","year":"2020","journal-title":"Curr. Psychol."},{"key":"ref_43","first-page":"95","article-title":"Approaches, Methods, and Resources for Assessing the Readability of Arabic Texts","volume":"22","author":"Nassiri","year":"2022","journal-title":"ACM Trans. Asian Low-Resour. Lang. Inf. Process."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.3758\/s13423-015-0809-4","article-title":"Effects of word length on eye movement control: The evidence from Arabic","volume":"22","author":"Paterson","year":"2015","journal-title":"Psychon. Bull. Rev."},{"key":"ref_45","unstructured":"Alrabiah, M., Alsalman, A., and Atwell, E. (2013, January 22). The design and construction of the 50 million words KSUCCA King Saud University Corpus of Classical Arabic. Proceedings of the WACL\u20192 Second Workshop on Arabic Corpus Linguistics, Lancaster, UK."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.procs.2017.10.106","article-title":"Automatic minimal diacritization of Arabic texts","volume":"117","author":"Alnefaie","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1007\/s10579-014-9274-3","article-title":"Creating language resources for under-resourced languages: Methodologies, and experiments with Arabic","volume":"49","author":"Kruschwitz","year":"2015","journal-title":"Lang. Resour. Eval."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Bouamor, H., Zaghouani, W., Diab, M., Obeid, O., Oflazer, K., Ghoneim, M., and Hawwari, A. (2015, January 30). A pilot study on Arabic multi-genre corpus diacritization. Proceedings of the Second Workshop on Arabic Natural Language Processing, Beijing, China.","DOI":"10.18653\/v1\/W15-3209"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Al-Edaily, A., Al-Wabil, A., and Al-Ohali, Y. (2013, January 1\u20133). Dyslexia Explorer: A Screening System for Learning Difficulties in the Arabic Language Using Eye Tracking. Proceedings of the Human Factors in Computing and Informatics, Maribor, Slovenia.","DOI":"10.1007\/978-3-642-39062-3_63"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Al-Wabil, A., and Al-Sheaha, M. (2010, January 14\u201316). Towards an interactive screening program for developmental dyslexia: Eye movement analysis in reading Arabic texts. Proceedings of the 12th International Conference on Computers Helping People with Special Needs, Vienna, Austria.","DOI":"10.1007\/978-3-642-14100-3_5"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1075\/sal.10.03alj","article-title":"Eye movements in Arabic reading","volume":"Volume 10","author":"AlJassmi","year":"2021","journal-title":"Studies in Arabic Linguistics"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Hermena, E.W., Bouamama, S., Liversedge, S.P., and Drieghe, D. (2021). Does diacritics-based lexical disambiguation modulate word frequency, length, and predictability effects? An eye-movements investigation of processing Arabic diacritics. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0259987"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Roman, G., and Pavard, B. (1987). A comparative study: How we read in Arabic and French. Eye Movements from Physiology to Cognition, Elsevier.","DOI":"10.1016\/B978-0-444-70113-8.50064-3"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1006\/brcg.1997.0917","article-title":"Inversion errors in Arabic number reading: Is there a nonsemantic route?","volume":"34","author":"Blanken","year":"1997","journal-title":"Brain Cogn."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Naz, S., Razzak, M.I., Hayat, K., Anwar, M.W., and Khan, S.Z. (2013, January 7\u20139). Challenges in baseline detection of Arabic script based languages. Proceedings of the Intelligent Systems for Science and Information: Extended and Selected Results from the Science and Information Conference, London, UK.","DOI":"10.1007\/978-3-319-04702-7_11"},{"key":"ref_56","unstructured":"Al Jarrah, E.Q. (2017). Using Language Features to Enhance Measuring the Readability of Arabic Text. [Master\u2019s Thesis, Yarmouk University]."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1007\/978-3-031-26254-8_73","article-title":"Impact of Feature Vectorization Methods on Arabic Text Readability Assessment","volume":"Volume 635","author":"Berrichi","year":"2023","journal-title":"Artificial Intelligence and Smart Environment (ICAISE 2022)"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1037\/h0057532","article-title":"A new readability yardstick","volume":"32","author":"Flesch","year":"1948","journal-title":"J. Appl. Psychol."},{"key":"ref_59","unstructured":"Gunning, R. (1968). The Technique of Clear Writing, McGraw-Hill Book Company. [2nd ed.]."},{"key":"ref_60","first-page":"639","article-title":"SMOG Grading\u2014A New Readability Formula","volume":"12","year":"1969","journal-title":"J. Read."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1037\/h0076540","article-title":"A computer readability formula designed for machine scoring","volume":"60","author":"Coleman","year":"1975","journal-title":"J. Appl. Psychol."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Kincaid, J.P., Fishburne, R.P., Rogers, R.L., and Chissom, B.S. (1975). Derivation of New Readability Formulas (Automated Readability Index, Fog Count and Flesch Reading Ease Formula) for Navy Enlisted Personnel, Naval Technical Training Command Millington TN Research Branch.","DOI":"10.21236\/ADA006655"},{"key":"ref_63","unstructured":"Chall, J.S., and Dale, E. (1995). Readability Revisited: The New Dale-Chall Readability Formula, Brookline Books."},{"key":"ref_64","unstructured":"El-Haj, M., and Rayson, P. (2016, January 23\u201328). OSMAN\u2015A Novel Arabic Readability Metric. Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC\u201916), Portoro\u017e, Slovenia."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.procs.2018.10.459","article-title":"Arabic Readability Research: Current State and Future Directions","volume":"142","author":"Saddiki","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_66","unstructured":"Dawood, B. (1977). The Relationship Between Readability and Selected Language Variables. [Master\u2019s Thesis, Baghdad University]."},{"key":"ref_67","unstructured":"Al-Heeti, K.N. (1985). Judgment Analysis Technique Applied to Readability Prediction of Arabic Reading Material. [Ph.D. Thesis, Northern Colorado University]."},{"key":"ref_68","first-page":"168","article-title":"A corpus-based readability formula for estimate of Arabic texts reading difficulty","volume":"21","author":"Daud","year":"2013","journal-title":"World Appl. Sci. J."},{"key":"ref_69","first-page":"2041","article-title":"Developing Readability Computational Formula for Arabic Reading Materials Among Non-native Students in Malaysia","volume":"Volume 194","author":"Ghani","year":"2021","journal-title":"The Importance of New Technologies and Entrepreneurship in Business Development: In the Context of Economic Diversity in Developing Countries: The Impact of New Technologies and Entrepreneurship on Business Development"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Mesgar, M., and Strube, M. (November, January 31). A neural local coherence model for text quality assessment. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium.","DOI":"10.18653\/v1\/D18-1464"},{"key":"ref_71","unstructured":"Vajjala, S., Majumder, B., Gupta, A., and Surana, H. (2020). Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems, O\u2019Reilly Media Inc."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Chen, X., and Meurers, D. (2016, January 16). Characterizing text difficulty with word frequencies. Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications, San Diego, CA, USA.","DOI":"10.18653\/v1\/W16-0509"},{"key":"ref_73","unstructured":"Rello, L. (2014). DysWebxia: A Text Accessibility Model for People with Dyslexia. [Ph.D. Thesis, Universitat Pompeu Fabra]."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1162\/tacl_a_00278","article-title":"Multiattentive Recurrent Neural Network Architecture for Multilingual Readability Assessment","volume":"7","author":"Azpiazu","year":"2019","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1162\/coli_a_00398","article-title":"Supervised and unsupervised neural approaches to text readability","volume":"47","author":"Martinc","year":"2021","journal-title":"Comput. Linguist."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1590\/1982-43272768201708","article-title":"Comparison of Reading Performance in Students with Developmental Dyslexia by Sex","volume":"27","author":"Oliveira","year":"2017","journal-title":"Paid\u00e9ia"},{"key":"ref_77","first-page":"75","article-title":"Efficient measuring of readability to improve documents accessibility for arabic language learners","volume":"19","author":"Bessou","year":"2021","journal-title":"J. Digit. Inf. Manag."},{"key":"ref_78","first-page":"7011","article-title":"Arabic Natural Language Processing and Machine Learning-Based Systems","volume":"7","author":"Alalyani","year":"2018","journal-title":"IEEE Access"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Shen, W., Williams, J., Marius, T., and Salesky, E. (2013, January 8). A language-independent approach to automatic text difficulty assessment for second-language learners. Proceedings of the 2nd Workshop on Predicting and Improving Text Readability for Target Reader Populations, Sofia, Bulgaria.","DOI":"10.21236\/ADA595522"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Salesky, E., and Shen, W. (2014, January 16). Exploiting Morphological, Grammatical, and Semantic Correlates for Improved Text Difficulty Assessment. Proceedings of the Ninth Workshop on Innovative Use of NLP for Building Educational Applications, Baltimore, MD, USA.","DOI":"10.3115\/v1\/W14-1819"},{"key":"ref_81","unstructured":"Forsyth, J.N. (2014). Automatic Readability Detection for Modern Standard Arabic. [Master\u2019s Thesis, Brigham Young University]."},{"key":"ref_82","unstructured":"Cavalli-Sforza, V., El Mezouar, M., and Saddiki, H. (2014, January 26\u201327). Matching an Arabic text to a learners\u2019 curriculum. Proceedings of the 2014 Fifth International Conference on Arabic Language Processing (CITALA 2014), Oujda, Morocco."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"3789","DOI":"10.1016\/j.jksuci.2020.12.021","article-title":"Arabic L2 readability assessment: Dimensionality reduction study","volume":"34","author":"Nassiri","year":"2022","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Nassiri, N., Lakhouaja, A., and Cavalli-Sforza, V. (2018). Arabic Readability Assessment for Foreign Language Learners, Springer International Publishing.","DOI":"10.1007\/978-3-319-91947-8_49"},{"key":"ref_85","unstructured":"Nassiri, N., Lakhouaja, A., and Cavalli-Sforza, V. (2020). Combining Classical and Non-classical Features to Improve Readability Measures for Arabic First Language Texts. International Conference on Advanced Intelligent Systems for Sustainable Development, Springer."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1007\/978-981-16-3637-0_54","article-title":"Evaluating the Impact of Oversampling on Arabic L1 and L2 Readability Prediction Performances","volume":"Volume 237","author":"Nassiri","year":"2022","journal-title":"Networking, Intelligent Systems and Security"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Al Aqeel, S., Abanmy, N., Aldayel, A., Al-Khalifa, H., Al-Yahya, M., and Diab, M. (2018). Readability of written medicine information materials in Arabic language: Expert and consumer evaluation. BMC Health Serv. Res., 18.","DOI":"10.1186\/s12913-018-2944-x"},{"key":"ref_88","unstructured":"Khallaf, N., and Sharoff, S. (2021, January 19). Automatic Difficulty Classification of Arabic Sentences. Proceedings of the Sixth Arabic Natural Language Processing Workshop (WANLP), Virtual, Kyiv, Ukraine."},{"key":"ref_89","first-page":"36","article-title":"Interpreting the Relevance of Readability Prediction Features","volume":"9","author":"Berrichi","year":"2023","journal-title":"Jordanian J. Comput. Inf. Technol."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Ouassil, M.A., Jebbari, M., Rachidi, R., Errami, M., Cherradi, B., and Raihani, A. (2024, January 28\u201329). Enhancing Arabic Text Readability Assessment: A Combined BERT and BiLSTM Approach. Proceedings of the 2024 International Conference on Circuit, Systems and Communication (ICCSC), Fes, Morocco.","DOI":"10.1109\/ICCSC62074.2024.10616953"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Saddiki, H., Habash, N., Cavalli-Sforza, V., and Al Khalil, M. (2018, January 19). Feature optimization for predicting readability of Arabic L1 and L2. Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications, Melbourne, Australia.","DOI":"10.18653\/v1\/W18-3703"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Mishra, A., and Bhattacharyya, P. (2017, January 4\u20139). Scanpath Complexity: Modeling Reading Effort Using Gaze Information. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11159"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Mathias, S., Murthy, R., Kanojia, D., Mishra, A., and Bhattacharyya, P. (2020, January 4\u20137). Happy are those who grade without seeing: A multi-task learning approach to grade essays using gaze behaviour. Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, Suzhou, China.","DOI":"10.18653\/v1\/2020.aacl-main.86"},{"key":"ref_94","unstructured":"Mathias, S., Murthy, R., Kanojia, D., and Bhattacharyya, P. (2021, January 18\u201321). Cognitively aided zero-shot automatic essay grading. Proceedings of the 17th International Conference on Natural Language Processing (ICON), Patna, India."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1080\/10888438.2023.2232063","article-title":"Scanpath regularity as an index of Reading comprehension","volume":"28","author":"Yu","year":"2024","journal-title":"Sci. Stud. Read."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Nicula, B., Panaite, M., Arner, T., Balyan, R., Dascalu, M., and McNamara, D.S. (2023). Automated Assessment of Comprehension Strategies from Self-explanations Using Transformers and Multi-task Learning. Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky, Springer Nature.","DOI":"10.1007\/978-3-031-36336-8_107"},{"key":"ref_97","unstructured":"Hollenstein, N. (2021). Leveraging Cognitive Processing Signals for Natural Language Understanding, ETH Zurich."},{"key":"ref_98","unstructured":"Hollenstein, N., Barrett, M., Troendle, M., Bigiolli, F., Langer, N., and Zhang, C. (2019). Advancing NLP with cognitive language processing signals. arXiv."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Sood, E., Tannert, S., M\u00fcller, P., and Bulling, A. (2020, January 6\u201312). Improving natural language processing tasks with human gaze-guided neural attention. Proceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Online.","DOI":"10.18653\/v1\/2020.conll-1.2"},{"key":"ref_100","unstructured":"Barrett, M. (2018). Improving Natural Language Processing with Human Data: Eye Tracking and Other Data Sources Reflecting Cognitive Text Processing. [Ph.D. Thesis, University of Copenhagen]."},{"key":"ref_101","unstructured":"Leal, S.E., Vieira, J.M.M., dos Santos Rodrigues, E., Teixeira, E.N., and Alu\u00edsio, S. (2020, January 8\u201313). Using eye-tracking data to predict the readability of Brazilian Portuguese sentences in single-task, multi-task and sequential transfer learning approaches. Proceedings of the 28th International Conference on Computational Linguistics, Barcelona, Spain."},{"key":"ref_102","first-page":"895","article-title":"Syntactic influences on eye movements during reading","volume":"Volume 3","author":"Clifton","year":"2011","journal-title":"The Oxford Handbook of Eye Movements"},{"key":"ref_103","unstructured":"Underwood, G. (1998). Chapter 3\u2014Eye Movements and Measures of Reading Time. Eye Guidance in Reading and Scene Perception, Elsevier Science Ltd."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1037\/0033-2909.124.3.372","article-title":"Eye movements in reading and information processing: 20 years of research","volume":"124","author":"Rayner","year":"1998","journal-title":"Psychol. Bull."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Wiechmann, D., Qiao, Y., Kerz, E., and Mattern, J. (2022, January 22\u201327). Measuring the impact of (psycho-) linguistic and readability features and their spill over effects on the prediction of eye movement patterns. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland.","DOI":"10.18653\/v1\/2022.acl-long.362"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Iba\u00f1ez, M., Reyes, L.L.A., Sapinit, R., Hussien, M.A., and Imperial, J.M. (2022). On Applicability of Neural Language Models for Readability Assessment in Filipino. Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners\u2019 and Doctoral Consortium, Proceedings of the 23rd International Conference, AIED 2022, Durham, UK, 27\u201331 July 2022, Springer. Proceedings, Part II.","DOI":"10.1007\/978-3-031-11647-6_118"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Howcroft, D.M., and Demberg, V. (2017, January 3\u20137). Psycholinguistic models of sentence processing improve sentence readability ranking. Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Valencia, Spain.","DOI":"10.18653\/v1\/E17-1090"},{"key":"ref_108","first-page":"229","article-title":"Investigating readability of french as a foreign language with deep learning and cognitive and pedagogical features","volume":"20","author":"Yancey","year":"2021","journal-title":"Lingue Linguaggio"},{"key":"ref_109","unstructured":"Olukoga, T.A., and Feng, Y. (2022). A Case Study on the Classification of Lost Circulation Events During Drilling using Machine Learning Techniques on an Imbalanced Large Dataset. arXiv."},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Baazeem, I., Al-Khalifa, H., and Al-Salman, A. (2025). AraEyebility: Eye-Tracking Data for Arabic Text Readability. Computation, 13.","DOI":"10.3390\/computation13050108"},{"key":"ref_111","unstructured":"Scikit-Learn (2023, October 05). Scikit-Learn Machine Learning in Python. Available online: https:\/\/scikit-learn.org\/stable\/."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Li, D., and Kanoulas, E. (2018, January 5\u20139). Bayesian Optimization for Optimizing Retrieval Systems. Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, Marina Del Rey, CA, USA.","DOI":"10.1145\/3159652.3159665"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1038\/nmeth.4551","article-title":"Machine learning: Supervised methods","volume":"15","author":"Bzdok","year":"2018","journal-title":"Nat. Methods"},{"key":"ref_114","unstructured":"Aldayel, A., Al-Khalifa, H., Alaqeel, S., Abanmy, N., Al-Yahya, M., and Diab, M. (2018, January 8). ARC-WMI: Towards Building Arabic Readability Corpus for Written Medicine Information. Proceedings of the 3rd Workshop on Open-Source Arabic Corpora and Processing Tools, Miyazaki, Japan."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.eswa.2017.12.029","article-title":"Increasing diversity in random forest learning algorithm via imprecise probabilities","volume":"97","author":"Mantas","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_116","unstructured":"Brownlee, J. (2023, December 21). Repeated k-Fold Cross-Validation for Model Evaluation in Python. Guiding Tech Media. Available online: https:\/\/machinelearningmastery.com\/repeated-k-fold-cross-validation-with-python\/."},{"key":"ref_117","unstructured":"Bengfort, B., Bilbro, R., and Ojeda, T. (2018). Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning, O\u2019Reilly Media Inc."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"13911","DOI":"10.1007\/s11227-021-03838-w","article-title":"A novel LSTM\u2013CNN\u2013grid search-based deep neural network for sentiment analysis","volume":"77","author":"Priyadarshini","year":"2021","journal-title":"J. Supercomput."},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Nguyen, N.T., Botzheim, J., Guly\u00e1s, L., Nunez, M., Treur, J., Vossen, G., and Kozierkiewicz, A. (2023). CNN-BiLSTM Model for Arabic Dialect Identification. Advances in Computational Collective Intelligence, Springer.","DOI":"10.1007\/978-3-031-41774-0"},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1007\/978-3-030-52856-0_31","article-title":"An Investigation and Evaluation of N-Gram, TF-IDF and Ensemble Methods in Sentiment Classification","volume":"Volume 325","author":"Rahman","year":"2020","journal-title":"Cyber Security and Computer Science (ICONCS)"},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Chen, J., Nakamori, Y., Yue, W., and Tang, X. (2016). Performance Comparison of TF*IDF, LDA and Paragraph Vector for Document Classification. Knowledge and Systems Sciences, Springer.","DOI":"10.1007\/978-981-10-2857-1_20"},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Mesgar, M., and Strube, M. (2016, January 12\u201317). Lexical coherence graph modeling using word embeddings. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego, CA, USA.","DOI":"10.18653\/v1\/N16-1167"},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Sabbeh, S.F., and Fasihuddin, H.A. (2023). A Comparative Analysis of Word Embedding and Deep Learning for Arabic Sentiment Classification. Electron., 12.","DOI":"10.3390\/electronics12061425"},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Ahmed, Z.A.T., Albalawi, E., Aldhyani, T.H.H., Jadhav, M.E., Janrao, P., and Obeidat, M.R.M. (2023). Applying Eye Tracking with Deep Learning Techniques for Early-Stage Detection of Autism Spectrum Disorders. Data, 8.","DOI":"10.3390\/data8110168"},{"key":"ref_126","unstructured":"Sarika, P.K. (2020). Comparing LSTM and GRU for Multiclass Sentiment Analysis of Movie Reviews. [Bachelor\u2019s Thesis, Blekinge Institute of Technology]."},{"key":"ref_127","unstructured":"Wilcox, E., Gauthier, J., Hu, J., Qian, P., and Levy, R. (August, January 29). On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behavior. Proceedings of the 42nd Annual Meeting of the Cognitive Science Society, Virtual."},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Aurnhammer, C., and Frank, S.L. (2019, January 24\u201327). Comparing gated and simple recurrent neural network architectures as models of human sentence processing. Proceedings of the 41st Annual Conference of the Cognitive Science Society (CogSci 2019), Montreal, QC, Canada.","DOI":"10.31234\/osf.io\/wec74"},{"key":"ref_129","unstructured":"Facebook Inc (2023, September 22). fastText Library for Efficient Text Classification and Representation Learning. Available online: https:\/\/fasttext.cc."},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Setyanto, A., Laksito, A., Alarfaj, F., Alreshoodi, M., Oyong, I., Hayaty, M., Alomair, A., Almusallam, N., and Kurniasari, L. (2022). Arabic Language Opinion Mining Based on Long Short-Term Memory (LSTM). Appl. Sci., 12.","DOI":"10.3390\/app12094140"},{"key":"ref_131","unstructured":"Uluslu, A.Y., and Schneider, G. (2023, January 16\u201317). Exploring Linguistic Features for Turkish Text Readability. Proceedings of the 6th International Conference on Natural Language and Speech Processing (ICNLSP-2023), Virtual."},{"key":"ref_132","unstructured":"Keras (2023, January 16). KerasTuner. Available online: https:\/\/keras.io\/keras_tuner\/."},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Copeland, L., and Gedeon, T. (2013, January 2\u20135). Measuring Reading Comprehension Using Eye Movements. Proceedings of the IEEE 4th International Conference on Cognitive Infocommunications (CogInfoCom), Budapest, Hungary.","DOI":"10.1109\/CogInfoCom.2013.6719207"},{"key":"ref_134","first-page":"35","article-title":"Predicting reading comprehension scores from eye movements using artificial neural networks and fuzzy output error","volume":"3","author":"Copeland","year":"2014","journal-title":"Artif. Intell. Res."},{"key":"ref_135","unstructured":"Singh, A.D., Mehta, P., Husain, S., and Rajkumar, R. (2016, January 11). Quantifying sentence complexity based on eye-tracking measures. Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC), Osaka, Japan."},{"key":"ref_136","doi-asserted-by":"crossref","unstructured":"Gonzalez-Garduno, A., and S\u00f8gaard, A. (2018, January 2\u20137). Learning to predict readability using eye-movement data from natives and learners. Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11978"},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1002\/rrq.498","article-title":"Using Eye-Tracking Measures to Predict Reading Comprehension","volume":"58","author":"Yu","year":"2023","journal-title":"Read. Res. Q."},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Makowski, S., J\u00e4ger, L.A., Abdelwahab, A., Landwehr, N., and Scheffer, T. (2018, January 10\u201314). A discriminative model for identifying readers and assessing text comprehension from eye movements. Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Dublin, Ireland.","DOI":"10.1007\/978-3-030-10925-7_13"},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Liu, F., and Lee, J.S. (2023, January 13). Hybrid models for sentence readability assessment. Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications, Toronto, ON, Canada.","DOI":"10.18653\/v1\/2023.bea-1.37"},{"key":"ref_140","doi-asserted-by":"crossref","unstructured":"Srivastava, H. (2022, January 26). Zero Shot Crosslingual Eye-Tracking Data Prediction using Multilingual Transformer Models. Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, Dublin, Ireland.","DOI":"10.18653\/v1\/2022.cmcl-1.11"},{"key":"ref_141","unstructured":"Caruso, M., Peacock, C.E., Southwell, R., Zhou, G., and D\u2019Mello, S.K. (2022, January 24\u201327). Going Deep and Far: Gaze-Based Models Predict Multiple Depths of Comprehension during and One Week Following Reading. Proceedings of the 15th International Conference on Educational Data Mining, International Educational Data Mining Society, Durham, UK."},{"key":"ref_142","unstructured":"Kennedy, A., Hill, R., and Pynte, J.E. (2003, January 20\u201324). The Dundee Corpus. Proceedings of the 12th European Conference on Eye Movements, Dundee, UK."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"102541","DOI":"10.1016\/j.ipm.2021.102541","article-title":"A little bird told me your gender: Gender inferences in social media","volume":"58","author":"Poulsen","year":"2021","journal-title":"Inf. Process. Manag."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"e12905","DOI":"10.1111\/cogs.12905","article-title":"What eye movements reveal about later comprehension of long connected texts","volume":"44","author":"Southwell","year":"2020","journal-title":"Cogn. Sci."},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"Goodkind, A., and Bicknell, K. (2018, January 7). Predictive power of word surprisal for reading times is a linear function of language model quality. Proceedings of the 8th Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2018), Salt Lake City, UT, USA.","DOI":"10.18653\/v1\/W18-0102"},{"key":"ref_146","unstructured":"Hadar, C.A., Shubi, O., Meiri, Y., and Berzak, Y. (2025). Decoding Open-Ended Information Seeking Goals from Eye Movements in Reading. arXiv."},{"key":"ref_147","doi-asserted-by":"crossref","unstructured":"Chiang, C.-H., and Lee, H.-Y. (2023, January 9\u201314). Can Large Language Models Be an Alternative to Human Evaluations?. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, Toronto, ON, Canada.","DOI":"10.18653\/v1\/2023.acl-long.870"},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1093\/eurjcn\/zvad087","article-title":"Using ChatGPT and Google Bard to improve the readability of written patient information: A proof of concept","volume":"23","author":"Moons","year":"2024","journal-title":"Eur. J. Cardiovasc. Nurs."},{"key":"ref_149","unstructured":"Klein, K.G., Frenkel, S., Shubi, O., and Berzak, Y. (2025). Eye Tracking Based Cognitive Evaluation of Automatic Readability Assessment Measures. arXiv."},{"key":"ref_150","first-page":"60","article-title":"Light Diacritic Restoration to Disambiguate Homographs in Modern Arabic Texts","volume":"21","author":"Azmi","year":"2021","journal-title":"ACM Trans. Asian Low-Resour. Lang. Inf. Process."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.chb.2014.07.003","article-title":"A calligraphic based scheme to justify Arabic text improving readability and comprehension","volume":"39","author":"Azmi","year":"2014","journal-title":"Comput. Hum. Behav."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"ETRA05","DOI":"10.1145\/3725830","article-title":"ScanDL 2.0: A Generative Model of Eye Movements in Reading Synthesizing Scanpaths and Fixation Durations","volume":"9","author":"Bolliger","year":"2025","journal-title":"Proc. ACM Hum.-Comput. Interact."},{"key":"ref_153","doi-asserted-by":"crossref","unstructured":"Shubi, O., Meiri, Y., Hadar, C.A., and Berzak, Y. (2024). Fine-grained prediction of reading comprehension from eye movements. arXiv.","DOI":"10.18653\/v1\/2024.emnlp-main.198"},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"24555","DOI":"10.1109\/ACCESS.2025.3537156","article-title":"Automatic Classification of Difficulty of Texts From Eye Gaze and Physiological Measures of L2 English Speakers","volume":"13","author":"Melo","year":"2025","journal-title":"IEEE Access"},{"key":"ref_155","unstructured":"Dini, L., Domenichelli, L., Brunato, D., and Dell\u2019Orletta, F. (August, January 27). From Human Reading to NLM Understanding: Evaluating the Role of Eye-Tracking Data in Encoder-Based Models. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vienna, Austria."}],"container-title":["Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-3197\/13\/11\/258\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T14:29:43Z","timestamp":1762180183000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-3197\/13\/11\/258"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,3]]},"references-count":155,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["computation13110258"],"URL":"https:\/\/doi.org\/10.3390\/computation13110258","relation":{},"ISSN":["2079-3197"],"issn-type":[{"value":"2079-3197","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,3]]}}}