Advancing Educational Outcomes with Artificial Intelligence: Challenges, Opportunities, And Future Directions

Advancing Educational Outcomes with Artificial Intelligence: Challenges, Opportunities, And Future Directions S. Esakkiammal K. Kasturi

Artificial intelligence (AI) into education is becoming a transformative agent offering new chances for enhancing administrative processes, teaching, and learning. Particularly machine learning (ML) and deep learning (DL), recent advances in artificial intelligence technologies have shown great potential in predicting academic achievement, improving teaching strategies, and so supporting decision-making inside educational institutions. Notwithstanding these advances, there are obvious problems and limits that have to be addressed if we are to fully exploit the potential of artificial intelligence in the field of education. Recent research reveals significant limits like poor contextual adaptability of artificial intelligence models, insufficient integration of emerging technologies like augmented reality (AR), and challenges in improving distance learning. Although the integration of AR into educational systems is still under investigated, current artificial intelligence models usually rely on generalised datasets lacking the diversity of educational environments. The shift to online learning has underscored even more the requirement of solid, contextually relevant models to manage assessment strategies, student interaction, and technology acceptance. By means of a comprehensive examination of the corpus of present literature, this paper evaluates the present position of artificial intelligence applications in education so highlighting research needs and constraints. Emphasising their capacity to solve the discovered challenges, the survey focusses on ML and DL application. By means of analysis of current studies and recommended future research routes, this study aims to offer pragmatic insights and recommendations for enhancing the efficiency of artificial intelligence in educational environments.
12 27 2024 https://creativecommons.org/licenses/by/4.0 10.22399/ijcesen.799 https://www.ijcesen.com/index.php/ijcesen/article/view/799 https://www.ijcesen.com/index.php/ijcesen/article/download/799/455 https://www.ijcesen.com/index.php/ijcesen/article/download/799/455 10.3389/fpsyg.2020.580820 Luan, H., Geczy, P., Lai, H., Gobert, J., Yang, S. J., Ogata, H., ... & Tsai, C. C. (2020). Challenges and future directions of big data and artificial intelligence in education. Frontiers in psychology. 11, 580820. https://doi.org/10.3389/fpsyg.2020.580820. 10.1109/TLT.2023.3314610 Ahmad, K., Iqbal, W., El-Hassan, A., Qadir, J., Benhaddou, D., Ayyash, M., & Al-Fuqaha, A. (2023). Data-Driven Artificial Intelligence in Education: A Comprehensive review. IEEE Transactions on Learning Technologies. 17;12–31. https://doi.org/10.1109/tlt.2023.3314610. 10.1016/j.caeai.2021.100025 Zhang, K., & Aslan, A. B. (2021). AI technologies for education: Recent research & future directions. Computers and Education Artificial Intelligence. 2, 100025. https://doi.org/10.1016/j.caeai.2021.100025 10.1109/ACCESS.2020.2988510 L. Chen, P. Chen and Z. Lin. (2020). Artificial intelligence in education: A review. IEEE Access. 8; 75264-75278. doi: 10.1109/ACCESS.2020.2988510. 10.1007/s11356-021-13110-6 Bashir, M. F., Ma, B., Bashir, M. A., Bilal, & Shahzad, L. (2021). Scientific data-driven evaluation of academic publications on environmental Kuznets curve. Environmental Science and Pollution Research. 28;16982-16999. https://doi.org/10.1007/s11356-021-13110-6. 10.60087/jaigs.v1i1.173 Elam, K. M. (2024). Exploring the challenges and future directions of big data and AI in education. General science (JAIGS). 1(1);81–93. https://doi.org/10.60087/jaigs.v1i1.173. 10.1186/s40561-023-00231-3 Dimitriadou, E., & Lanitis, A. (2023). A critical evaluation, challenges, and future perspectives of using artificial intelligence and emerging technologies in smart classrooms. Smart Learning Environments. 10(1);12. https://doi.org/10.1186/s40561-023-00231-3. 10.3390/su14052497 Bachmann, N., Tripathi, S., Brunner, M., & Jodlbauer, H. (2022). The contribution of data-driven technologies in achieving the sustainable development goals. Sustainability. 14(5);2497. https://doi.org/10.3390/su14052497. Moinuddin, M., Usman, M., & Khan, R. (2024). Strategic Insights in a Data-Driven Era: Maximizing Business Potential with Analytics and AI. Revista Espanola de Documentacion Cientifica. 18(02);117-133. 10.1016/j.caeai.2020.100001 Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence. 1, 100001. https://doi.org/10.1016/j.caeai.2020.100001 10.1016/j.heliyon.2020.e04081 Cruz-Jesus, F., Castelli, M., Oliveira, T., Mendes, R., Nunes, C., Sa-Velho, M., & Rosa-Louro, A. (2020). Using artificial intelligence methods to assess academic achievement in public high schools of a European Union country. Heliyon. 6(6), e04081. https://doi.org/10.1016/j.heliyon.2020.e04081. 10.1007/s10639-020-10346-6 Gil, P. D., Da Cruz Martins, S., Moro, S., & Costa, J. M. (2020). A data-driven approach to predict first-year students’ academic success in higher education institutions. Education and Information Technologies. 26(2);2165–2190. https://doi.org/10.1007/s10639-020-10346-6. 10.1111/exsy.12820 Teng, Y., Zhang, J., & Sun, T. (2022). Data‐driven decision‐making model based on artificial intelligence in higher education system of colleges and universities. Expert Systems, 40(4). https://doi.org/10.1111/exsy.12820. 10.1016/j.ijis.2020.09.001 Guan, C., Mou, J., & Jiang, Z. (2020). Artificial intelligence innovation in education: A twenty-year data-driven historical analysis. International Journal of Innovation Studies. 4(4);134-147. https://doi.org/10.1016/j.ijis.2020.09.001 10.1186/s40561-022-00192-z Yağcı, M. (2022). Educational data mining: prediction of students' academic performance using machine learning algorithms. Smart Learning Environments. 9(1);11. https://doi.org/10.1186/s40561-022-00192-z. 10.1007/s00521-022-07997-6 Duong, H. T. H., Tran, L. T. M., To, H. Q., & Van Nguyen, K. (2023). Academic performance warning system based on data driven for higher education. Neural Computing and Applications. 35(8);5819-5837. https://doi.org/10.1007/s00521-022-07997-6 10.1109/TLT.2023.3314610 Ahmad, K., Iqbal, W., El-Hassan, A., Qadir, J., Benhaddou, D., Ayyash, M., & Al-Fuqaha, A. (2024). Data-driven artificial intelligence in education: A comprehensive review. IEEE Transactions on Learning Technologies. 17;12-31. doi: 10.1109/TLT.2023.3314610. 10.1109/ICDSNS62112.2024.10691297 P. Kalpana, K. Malleboina, M. Nikhitha, P. Saikiran and S. N. Kumar. (2024). Predicting Cyberbullying on Social Media in the Big Data Era Using Machine Learning Algorithm. 2024 International Conference on Data Science and Network Security (ICDSNS), Tiptur, India. 1-7, https://doi.org/10.1109/ICDSNS62112.2024.10691297. 10.1109/ICDSNS62112.2024.10691181 P. Kalpana, P. Srilatha, G. S. Krishna, A. Alkhayyat and D. Mazumder. (2024). Denial of Service (DoS) Attack Detection Using Feed Forward Neural Network in Cloud Environment. 2024 International Conference on Data Science and Network Security (ICDSNS). Tiptur, India. 1-4, https://doi.org/10.1109/ICDSNS62112.2024.10691181. 10.22399/ijcesen.526 Ponugoti Kalpana, L. Smitha, Dasari Madhavi, Shaik Abdul Nabi, G. Kalpana, and S. Kodati. (2024). A Smart Irrigation System Using the IoT and Advanced Machine Learning Model: A Systematic Literature Review. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.526. 10.1007/s10639-023-11589-9 Alshamsi, A. M., El-Kassabi, H., Serhani, M. A., & Bouhaddioui, C. (2023). A multi-criteria decision-making (MCDM) approach for data-driven distance learning recommendations. Education and Information Technologies. 28(8);10421-10458. https://doi.org/10.1007/s10639-023-11589-9. 10.18280/ts.400523 Kalpana, P., Anandan, R. (2023). A capsule attention network for plant disease classification. Traitement du Signal. 40(5);2051-2062. https://doi.org/10.18280/ts.400523. 10.1044/2023_LSHSS-23-00082 Powell, R., Schultz, J., Harvey, R., & Meaux, A. (2024). Maximizing student outcomes in schools: Data-driven Individualized Education Program goals and objectives aligned to the standards. Language, Speech, and Hearing Services in Schools. 55(2);303-322. doi: 10.1044/2023_LSHSS-23-00082. 10.22399/ijcesen.535 V. P. and M. R. A. (2024). A Scalable, Secure, and Efficient Framework for Sharing Electronic Health Records Using Permissioned Blockchain Technology. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.535 10.4108/eetpht.9.4518 Vinayasree, P., & Reddy, A. M. (2023). Blockchain-Enabled Hyperledger Fabric to Secure Data Transfer Mechanism for Medical Cyber-Physical System: Overview, Issues, and Challenges. EAI Endorsed Trans Perv Health Tech. 9. DOI: 10.4108/eetpht.9.4518 10.22399/ijcesen.519 Rama Lakshmi BOYAPATI, & Radhika YALAVARTHI. (2024). RESNET-53 for Extraction of Alzheimer’s Features Using Enhanced Learning Models. International Journal of Computational and Experimental Science and Engineering, 10(4);879-889. https://doi.org/10.22399/ijcesen.519 10.22399/ijcesen.410 Sheela Margaret D, Elangovan N, Sriram M, & Vedha Balaji. (2024). The Effect of Customer Satisfaction on Use Continuance in Bank Chatbot Service. International Journal of Computational and Experimental Science and Engineering, 10(4);1069-1077. https://doi.org/10.22399/ijcesen.410 10.22399/ijcesen.383 jaber, khalid, Lafi, M., Alkhatib, A. A., AbedAlghafer, A. K., Abdul Jawad, M., & Ahmad, A. Q. (2024). Comparative Study for Virtual Personal Assistants (VPA) and State-of-the-Art Speech Recognition Technology. International Journal of Computational and Experimental Science and Engineering, 10(3);427-433. https://doi.org/10.22399/ijcesen.383 10.22399/ijcesen.780 J. Prakash, R. Swathiramya, G. Balambigai, R. Menaha, & J.S. Abhirami. (2024). AI-Driven Real-Time Feedback System for Enhanced Student Support: Leveraging Sentiment Analysis and Machine Learning Algorithms. International Journal of Computational and Experimental Science and Engineering, 10(4);1567-1574. https://doi.org/10.22399/ijcesen.780 10.22399/ijcesen.516 ÖZNACAR, T., & ERGENE, N. (2024). A Machine Learning Approach to Early Detection and Malignancy Prediction in Breast Cancer. International Journal of Computational and Experimental Science and Engineering, 10(4);911-917. https://doi.org/10.22399/ijcesen.516 10.22399/ijcesen.479 M, V., V, J., K, A., Kalakoti, G., & Nithila, E. (2024). Explainable AI for Transparent MRI Segmentation: Deep Learning and Visual Attribution in Clinical Decision Support. International Journal of Computational and Experimental Science and Engineering, 10(4);575-584. https://doi.org/10.22399/ijcesen.479 10.22399/ijcesen.522 Venkatraman Umbalacheri Ramasamy. (2024). Overview of Anomaly Detection Techniques across Different Domains: A Systematic Review. International Journal of Computational and Experimental Science and Engineering, 10(4);898-910. https://doi.org/10.22399/ijcesen.522 10.22399/ijcesen.606 Nuthakki, praveena, & Pavankumar T. (2024). Comparative Assessment of Machine Learning Algorithms for Effective Diabetes Prediction and Care. International Journal of Computational and Experimental Science and Engineering, 10(4);1337-1343. https://doi.org/10.22399/ijcesen.606 10.22399/ijcesen.342 Türkmen, G., Sezen, A., & Şengül, G. (2024). Comparative Analysis of Programming Languages Utilized in Artificial Intelligence Applications: Features, Performance, and Suitability. International Journal of Computational and Experimental Science and Engineering, 10(3);461-469. https://doi.org/10.22399/ijcesen.342 10.22399/ijcesen.385 Jafar Ismail, R., Samar Jaafar Ismael, Dr. Sara Raouf Muhamad Amin, Wassan Adnan Hashim, & Israa Tahseen Ali. (2024). Survey of Multiple Destination Route Discovery Protocols. International Journal of Computational and Experimental Science and Engineering, 10(3);420-426. https://doi.org/10.22399/ijcesen.385 10.22399/ijcesen.460 guven, mesut. (2024). Dynamic Malware Analysis Using a Sandbox Environment, Network Traffic Logs, and Artificial Intelligence. International Journal of Computational and Experimental Science and Engineering, 10(3);480-490. https://doi.org/10.22399/ijcesen.460 10.22399/ijcesen.386 Serap ÇATLI DİNÇ, AKMANSU, M., BORA, H., ÜÇGÜL, A., ÇETİN, B. E., ERPOLAT, P., … ŞENTÜRK, E. (2024). Evaluation of a Clinical Acceptability of Deep Learning-Based Autocontouring: An Example of The Use of Artificial Intelligence in Prostate Radiotherapy. International Journal of Computational and Experimental Science and Engineering, 10(4);1181-1186. https://doi.org/10.22399/ijcesen.386