Investigating Personalized Learning Paths to Address Educational Disparities Using Advanced Artificial Intelligence Systems
Adaptive Algorithms; Bias Mitigation; Content Embedding; Cosine Similarity; Cultural Nuances; Cultural Relevance Score; Cultural Sensitivity; Data Auditing; Diversity in Training Datasets; Named Entity Recognition (NER); Natural Language Processing (NLP); Personalized Learning Paths; Sentiment Analysis
This innovative study reimagines the role of Natural Language Processing (NLP) in individualized education by highlighting the critical need to incorporate cultural subtleties. While natural language processing (NLP) offers great potential for improving classroom instruction, current research frequently fails to account for the complex issues caused by cultural variation. This research fills a significant need by providing a novel framework for the detection and incorporation of cultural subtleties into individualized learning programs. Further research into common biases is driving the development of natural language processing models with greater cultural sensitivity and awareness, such as gender bias in Named Entity Recognition (NER) and sentiment bias in cultural preferences. In order to correct past biases and promote gender neutrality in educational content, the research makes use of an adaptive NER algorithm and a diverse training dataset. Similarly, to guarantee nuanced and fair sentiment evaluations, the study suggests regularly evaluating and retraining sentiment algorithms with datasets that represent multiple cultures. A Cultural Relevance Score of 0.9, Adaptive Content Embedding vectors [0.3, 0.6, -0.2.], and an impressive Cosine Similarity of 0.85 are some of the evaluation measures that highlight the effectiveness of the research. These measurements show encouraging gains, which confirms that the research might help make schools more welcoming and sensitive to different cultures. The research has the potential to revolutionize individualized education by making it more accessible and engagingfor students from all backgrounds. 2024 IEEE.
Chauhan M.; Rahman H.; Ibrahim U.M.M.; Reddy D.S.; Padmanabhan A.S.; Thirumalaikumari T.; Babu G.C.
International Conference on E-Mobility, Power Control and Smart Systems: Futuristic Technologies for Sustainable Solutions, ICEMPS 2024
Institute of Electrical and Electronics Engineers Inc.
Chauhan M., Symbiosis Law School-NOIDA, India, Symbiosis International (Deemed University), Pune, India; Rahman H., Malang, Indonesia; Ibrahim U.M.M., Malang, Indonesia; Reddy D.S., Vardhaman College of Engineering, Department of Mathematics, Telangana, Hyderabad, India; Padmanabhan A.S., Law Christ University, Bangalore, India; Thirumalaikumari T., Saveetha College of Liberal Arts and Sciences, Chennai, India; Babu G.C., Gokaraju Rangaraju Institute of Engineering and Technology, Department of Cse, Telangana, Hyderabad, India