AI-Driven Adaptive Learning Systems for Vocational Education: Enhancing Personalized Skill Development

Dr. Michael Chang

Abstract


The integration of Artificial Intelligence (AI) into vocational education offers the potential to revolutionize traditional learning models by enhancing adaptability and personalization. This paper explores the application of AI-driven adaptive learning systems in vocational training programs, focusing on how machine learning algorithms can tailor educational content to individual learners' needs, preferences, and learning speeds. The study examines various AI technologies, including natural language processing (NLP) and intelligent tutoring systems, and their effectiveness in improving skill acquisition and retention. Furthermore, the paper discusses the challenges and ethical considerations in implementing AI-based systems in vocational education, such as data privacy and algorithmic fairness.

References


Reddy, M. S., Sarisa, M., Konkimalla, S., Bauskar, S. R., Gollangi, H. K., Galla, E. P., & Rajaram, S. K. (2021). Predicting tomorrow’s Ailments: How AI/ML Is Transforming Disease Forecasting. ESP Journal of Engineering & Technology Advancements, 1(2), 188-200.

Mahida, A., Mandala, V., Bauskar, S. R., Konkimalla, S., & Reddy, M. S. (2024). Real-Time Fraud Mitigation in Digital Payments: Big Data and AI-Driven Biometric Authentication. Nanotechnology Perceptions, 20, 1176-1193.

Madhavaram, C. R., Galla, E. P., Reddy, M. S., Sarisa, M., & Nagesh, V. (2021). Predicting Diabetes Mellitus in Healthcare: A Comparative Analysis of Machine Learning Algorithms on Big Dataset. Journal homepage: https://gjrpublication. com/gjrecs, 1(01).

Bauskar, S. R., Reddy, M. S., Sarisa, M., & KONKIMALLA, S. The Future of Cloud Computing_ Al-Driven Deep Learning and Neural Network Innovations. BUDHA PUBLISHER.

Konkimalla, S., SARISA, M., REDDY, M. S., & BAUSKAR, S. DATA ENGINEERING IN THE AGE OF AI GENERATIVE MODELS AND DEEP LEARNING UNLEASHED. BUDHA PUBLISHER.

Reddy, M., Konkimalla, S., Rajaram, S. K., Bauskar, S. R., Sarisa, M., & Sunkara, J. R. (2022). Using AI And Machine Learning To Secure Cloud Networks: A Modern Approach To Cybersecurity. Available at SSRN 5045776.

Patra, G. K., Kuraku, C., Konkimalla, S., Boddapati, V. N., & Sarisa, M. (2023). Sentiment Analysis of Customer Product Review Based on Machine Learning Techniques in E-Commerce. Journal of Artificial Intelligence & Cloud Computing. SRC/JAICC-408. DOI: doi. org/10.47363/JAICC/2023 (2), 389(1), 7211-7224.

Patra, G. K., Kuraku, C., Konkimalla, S., Boddapati, V. N., Sarisa, M., & Reddy, M. S. (2024). An Analysis and Prediction of Health Insurance Costs Using Machine Learning-Based Regressor Techniques. Journal of Data Analysis and Information Processing, 12(4), 581-596.

Rajaram, S. K., Konkimalla, S., Sarisa, M., Gollangi, H. K., Madhavaram, C. R., & Reddy, M. S. (2023). AI/ML-Powered Phishing Detection: Building an Impenetrable Email Security System. ISAR Journal of Science and Technology, 1(2), 10-19.

Gummadi, V., Ramadevi, N., Udayaraju, P., Ravulu, C., Seelam, D. R., & Swamy, S. V. (2024, September). A Deep Learning-based Optimization Model for Advertisement Campaign. In 2024 5th International Conference on Smart Electronics and Communication (ICOSEC) (pp. 1783-1790). IEEE.

Gummadi, V., Udayaraju, P., Kolasani, D., Kotaru, C., Sayana, R., & Neethika, A. (2024, December). NLP Based TAG Algorithm for Enhancing Customer Data Platform and Personalized Marketing. In 2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS) (pp. 60-67). IEEE.

Gummadi, V., Udayaraju, P., Sarabu, V. R., Ravulu, C., Seelam, D. R., & Venkataramana, S. (2024, October). Enhancing Communication and Data Transmission Security in RAG Using Large Language Models. In 2024 4th International Conference on Sustainable Expert Systems (ICSES) (pp. 612-617). IEEE.

Mane, S., & Immidi, K. (2024). Strategic Insights and Best Practices for Upgrading to SAP S/4HANA: A Comprehensive Framework for Business Transformation. International Journal of Creative Research In Computer Technology and Design, 6(6).

Mane, S. (2024). Optimizing Returns and Refunds Management in SAP: Leveraging Data-Driven Insights and Advanced Automation. International Journal of Machine Learning and Artificial Intelligence, 5(5), 1-13.

Mane, S., & Immidi, K. (2023). Enhancing SAP Available-to-Promise (ATP) Capabilities through AI Integration: A Transformative Approach to Supply Chain Optimization. International Journal of Creative Research In Computer Technology and Design, 5(5), 1-24.

Mane, S. (2023). Optimizing SAP Sales Order Processing: Strategies, Technologies, and Impact on Operational Efficiency. International Journal of Interdisciplinary Finance Insights, 2(2), 1-32.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 International Journal of Sustainable Development in Computing Science

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A Double-Blind Peer Reviewed Journal