Opportunities and Challenges of AI in Education
DOI:
https://doi.org/10.63503/acset.978-81-995593-9-4.71Keywords:
Artificial Intelligence, Digital System, Educational Technology, Educational Analytics, Intelligent Tutoring System, Personalized Learning.Abstract
The emergence of AI is one of the recent trends that has affected several spheres of education. The use of artificial intelligence can change the way teaching and learning take place, as well as the way assessments and educational processes are managed. It looks like personalised systems and tutorships are among those that tend to be talked about. Besides, automated evaluation and predictive analytics are among the possible applications of AI. It may increase efficiency, but it is likely to become less accessible to all students. In my view, there are many opportunities available, but it is not clear whether they depend on a particular implementation. Some regard it as revolutionary, while others pay less attention to it. However, there are challenges to using AI in education. The first obvious issue is ethical considerations. Privacy concerns should be addressed due to the vast amounts of data required. Bias in algorithm design may lead to discrimination against certain social groups. There are inequalities in access among students. The use of AI in learning may decrease face-to-face interaction. I am not sure if these issues are directly connected, but they are definitely worth mentioning. The reviewed paper explores both opportunities and challenges in the application of AI in education. This paper reviews the recent literature, considers applications, examines the advantages and disadvantages, and provides recommendations for responsible implementation. It appears that artificial intelligence can be of great assistance in enhancing efficiency and effectiveness, but its proper use requires consideration of ethical, technological, and sociological issues, as well as learning methods. This section gets pretty complicated when you consider all these factors together. Some concepts appear to be more related to each other than shown here.
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[114] Gupta, S., Chaudhary, G., & Garg, P. (2013). Modified AODV Routing Protocol through Cache Memory for Finding New Routing Paths in MANETs—International Journal of Computer Science & Management Studies, 13(3).
[115] Gupta, A., & Garg, P. (2021). Emerging Techniques for Handling Pandemic Challenges. Enabling Healthcare 4.0 for Pandemics: A Roadmap Using AI, Machine Learning, IoT and Cognitive Technologies, 189-209.
[116] Chaudhary, A. P., Mishra, A., Kumar, D., & Garg, P. (2023, April). Human Emotion Recognition using Deep Learning. In the 2023 International Conference on Computational Intelligence, Communication Technology and Networking (CICTN) (pp. 191-197). IEEE.
[117] Nagpal, S., Garg, P., Gaba, S., & Aggarwal, A. (2023). 13 An improved genetic quantum cryptography model for network communication. Quantum-Safe Cryptography Algorithms and Approaches: Impacts of Quantum Computing on Cybersecurity, 177.
[118] Yadav, M., Swami, V., Kumar, N., & Garg, P. (2025). Comparative study of Repairable Juice Plants using RPGT. Reliability: Theory & Applications, 20(2 (84)), 776-783.
[119] Gupta, A., Garg, P., & Yadav, P. (2025). Role of Generative AI Towards Education and Learning: Present & Future. TPM–Testing, Psychometrics, Methodology in Applied Psychology, 32(S6 (2025): Posted 15 Sept), 1059-1076.
[120] Dalal, P., Beniwal, G., Sharma, V., Garg, P., & Ahmed, K. (2025). Predicting Student Motivation and Engagement through Machine Learning Models. TPM–Testing, Psychometrics, Methodology in Applied Psychology, 32(S7 (2025): Posted 10 October), 393-411.
[121] Gupta, A., Mund, A., Roy, S., Garg, P., & Yadav, D. K. (2025). Trust in AI Systems: A Social-psychological Investigation of Human–AI Collaboration. TPM–Testing, Psychometrics, Methodology in Applied Psychology, 32(S7 (2025): Posted 10 October), 428-446.
[122] Bhardwaj, A., Das, A., Garg, P., & Yadav, S. (2025). Material-Driven Performance Analysis of a Vertical Nanowire Tunnel FET for Analog Applications: Bhardwaj, Das, Garg, and Yadav. Journal of Electronic Materials, 1-12.
[123] Dalal, P., Sharma, B., Sharma, T., Garg, P., & Ahmed, K. (2025). Explainable AI for Understanding Human Decision-Making Patterns. TPM–Testing, Psychometrics, Methodology in Applied Psychology, 32(S7 (2025): Posted 10 October), 412-427.
[124] Sharma, K. K., Verma, P. K., Garg, P., & Shrotriya, V. K. (2025, October). Predicting costs and benefits of IoT-based energy management for optimizing sustainable energy storage in rural areas. In AIP Conference Proceedings (Vol. 3343, No. 1, p. 040017). AIP Publishing LLC.
[125] Ahmed, K., Baranwal, A., Sharma, N., Garg, P., & Singh, N. (2026). The Role of Federated Learning in AI-Powered Integrated Healthcare Solutions. In Enabling Collaborative Health Intelligence With Federated Learning (pp. 421-448). IGI Global Scientific Publishing.
[126] Gupta, S., Garg, P., Agarwal, J., Thakur, H. K., & Yadav, S. P. (2025). Federated learning-based intelligent systems to handle issues and challenges in IoVs (Part 2). Bentham Science Publishers. https://doi.org/10.2174/97898153222241250301
[127] Garg, P., Pranav, S., & Prerna, A. (2021). Green internet of things (G-IoT): A solution for sustainable technological development. In Green Internet of Things for Smart Cities (pp. 23-46). CRC Press.
[128] Srivastava, A. K., Shukla, A., Gupta, H., Saxena, K., & Garg, P. (2026). Towards an intelligent attendance management system with face recognition using the LBPH algorithm. In Recent Advances in Computational Methods in Science and Technology (pp. 8-15). CRC Press.
[129] Srivastava, A. K., Garg, P., & Pandey, H. (2026). Vedcure: Towards intelligent ayurvedic drug recommendation and disease prediction. In Recent Advances in Computational Methods in Science and Technology (pp. 16-23). CRC Press.
[130] Upadhyay, D., Garg, P., & Babbar, N. (2026). Blockchain and IoT-based smart contract framework for efficient and secure product life management. Discover Internet of Things.
[131] Singh, A., Parmar, R., Bhardwaj, P., Sharma, V., & Garg, P. (2026). Fusion of Aerial Networks with Advanced Computing Paradigms. Edge Computing and Aerial Platforms, 355-367.
[132] Kumari, M., Baranwal, A., Sonal, & Garg, P. (2026). Application of Aerial Edge Computing in Disaster Management. Edge Computing and Aerial Platforms, 103-122.
[133] Aditi, Saraswat, P., Sharma, V., & Garg, P. (2026). Advances in Aerial Platforms and Edge Computing. Edge Computing and Aerial Platforms, 123-143.
[134] Garg, P., Arora, K., Bawane, R., Gupta, C., & Ahmed, K. (2025). Detection and Prevention of Cyber Attacks and Threats using AI.
[135] Ahmed, K., Ahmed, A., Khan, J., Garg, P., Seth, S., & Mallik, S. (2025). Principal Component Analysis-Based Clustering of Insecticides and Molecular Docking of Pyrethroid Insecticides.
[136] Kumar, B., Kumar, A., Nanwal, J., Garg, P., & Patnaik, P. (2025, November). Ensemble of YOLOv5 and Segment Anything Model for Brain Tumor Detection. In 2025, the 2nd International Conference on Advanced Computing and Emerging Technologies (ACET) (pp. 1-5). IEEE.
[137] Arsalan, M., Anas, M., & Garg, P. (2025). Transparent AI for Drug Discovery and Development. Available at SSRN 5844242.
[138] Singh, A., Bhardwaj, P., Garg, P., & Singh, N. (2026). Introduction to explainable artificial intelligence in healthcare. In Explainable AI in Clinical Practice (pp. 23-44). Academic Press.
[139] Kapoor, S., Singh, A., Garg, P., & Ramasamy, L. K. (2026). Explainable artificial intelligence in a diagnostic support system. In Explainable AI in Clinical Practice (pp. 131-145). Academic Press.
[140] Ahmed, K., Anas, M., & Garg, P. (2026). Case studies on unlocking the potential of Industry 4.0 for sustainable manufacturing through generative AI-driven innovations. Available at SSRN 6356958.
[141] Garg, P., & Oruganti, S. K. (2026, March). AI Assisted Routing Optimization in Opportunistic IoT Networks using Machine Learning: A Comprehensive Review on Protocols & Simulators. In Sustainable Global Societies Initiative (Vol. 1, No. 4). Vibrasphere Technologies.
[142] Arsalan, M., Pokhrel, L., & Garg, P. (2026). Architecture, Components, and tools in Integrated AI-Augmented Intelligence: A design perspective. Components and tools in Integrated AI-Augmented Intelligence: A design perspective (March 19, 2026).
[143] Singh, H., Ahmed, K., & Garg, P. (2026). Human Versus Machine Customer Behavior and Functional Differences. Available at SSRN 6441098.
[144] Saraswat, P., & Garg, P. (2026). Soft Computing In AI Agents.
[145] Saraswat, P., & Garg, P. (2026). Water Quality Prediction Using IOT Sensors and Deep Networks.
[146] Arsalan, M., Ahmed, K., & Garg, P. (2026). Machine learning for Anomaly detection in sensor networks. Available at SSRN 6441518.
[147] Kumari, M., & Garg, P. (2026). Hybrid Cloud Infrastructure: Models, Benefits, Security, and Challenges. Benefits, Security, and Challenges (March 19, 2026).
[148] Singh, H., & Garg, P. (2026). Demystifying Artificial Distributed Intelligence (ADI). Available at SSRN 6442698.
[149] Saraswat, P., & Garg, P. (2026). Human AI Collaboration: The Future of Clinical Decision Making.
[150] Saraswat, P., & Garg, P. (2026). Breaking Data Boundaries: Federated Learning in Digital Healthcare.
[151] Singh, A., Parmar, R., Bhardwaj, P., Sharma, V., & Garg, P. (2026). Fusion of Aerial Networks with Advanced Computing Paradigms. Edge Computing and Aerial Platforms, 355-367.
[152] Kumari, M., Baranwal, A., Sonal, & Garg, P. (2026). Application of Aerial Edge Computing in Disaster Management. Edge Computing and Aerial Platforms, 103-122.
[153] Aditi, Saraswat, P., Sharma, V., & Garg, P. (2026). Advances in Aerial Platforms and Edge Computing. Edge Computing and Aerial Platforms, 123-143.
[154] Garg, P. (2026). Zero-Trust Security Enforcement through AI-Powered Anomaly Detection in Cloud Systems. Journal of Artificial Intelligence in Governance and Public Policy (JAIGPP), 1(1), 1-8.
[155] Singh, A. P., Sharma, A., & Garg, P. (2026, January). AI-Powered Adaptive Mock Interview Generation System. In 2026 International Conference on AI-Driven Smart Systems and Ubiquitous Computing (ICAUC) (pp. 1421-1426). IEEE.
[156] Raghav, A., Mishra, A., & Garg, P. (2026, January). Enhancing Healthcare Access: An AI-driven Chatbot for Doctor Appointment Management. In 2026 International Conference on AI-Driven Smart Systems and Ubiquitous Computing (ICAUC) (pp. 910-914). IEEE.
[157] Kumar, B., Chauhan, D., Singh, H., Verma, H., Sahu, K., & Garg, P. (2026, January). Decoding Linear A with Artificial Intelligence: A Comprehensive Machine Learning and NLP Framework. In 2026 International Conference on AI-Driven Smart Systems and Ubiquitous Computing (ICAUC) (pp. 1-7). IEEE.
[158] Gupta, V., Chakravarti, L., Akhtar, M. M., Maheshwari, P., Garg, P., & Tiwari, D. (2026, January). A Sentence-Level Risk Estimator for Identifying Hallucinations in Generative AI. In 2026 International Conference on AI-Driven Smart Systems and Ubiquitous Computing (ICAUC) (pp. 1619-1626). IEEE.
[159] Patnaik, P., & Garg, P. (2026). Principles of Artificial Intelligence: Cognitive Architectures, Large Language Models, and Computational Limits. Deep Science Publishing.
[160] Singh, K., & Garg, P. (2026). Trustworthy Deep Learning: Robustness, Uncertainty Quantification, and Adversarial Resilience. Deep Science Publishing.
