Open Access Open Access  Restricted Access Subscription Access

Enhancing Personalized Learning: The Role of Adaptive AI-Driven Natural Language Processing in Real-Time Feedback for Online Education

Kayode Sheriffdeen

Abstract


Personalized learning is a transformative approach in online education, offering tailored experiences to meet individual learner needs. This study explores the integration of adaptive Artificial Intelligence (AI) and Natural Language Processing (NLP) to enhance real-time feedback mechanisms in digital learning environments. Adaptive AI leverages learner data to adjust instructional content dynamically, while NLP facilitates nuanced understanding of learner inputs such as written assignments, queries, and interactions. By analyzing language patterns and comprehension gaps, AI-driven systems provide instant, context-sensitive feedback, fostering deeper engagement and understanding. The research underscores the potential of these technologies to reduce instructor workload, scale individualized support, and improve learner outcomes, highlighting ethical considerations like data privacy and algorithmic fairness. This synergy between adaptive AI and NLP marks a pivotal step toward scalable, efficient, and inclusive education, redefining online pedagogy for diverse learners worldwide.


Full Text:

PDF

References


Anderson, T., & Dron, J. (2011). Three generations of distance education pedagogy. The International Review of Research in Open and Distributed Learning, 12(3), 80-97.

Explores pedagogical frameworks in online education, emphasizing the importance of personalization.

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019.

Introduces BERT, a state-of-the-art NLP model, and its capabilities in understanding context and semantics.

VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221.

Reviews the efficacy of intelligent tutoring systems compared to traditional teaching methods.

Aleven, V., Roll, I., McLaren, B. M., & Koedinger, K. R. (2016). Automated feedback for student learning: A meta-analysis. Educational Technology Research and Development, 64, 1195–1225.

Provides insights into the role and impact of automated feedback in educational settings.

Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review.

Maximizing Learning Trajectories: An Investigation into AI-Driven Natural Language Processing Integration in Online Educational Platforms. (2024). International Research Journal of Modernization in Engineering Technology and Science. https://doi.org/10.56726/irjmets18093


Refbacks

  • There are currently no refbacks.