Potential Abstract:
Abstract: This research study explores the intersection of data science, artificial intelligence, and cultural representation in educational participation. The increasing diversity within educational settings calls for innovative approaches to ensure equitable opportunities for all students. Through a mixed-methods approach, this study aims to investigate how artificially intelligent systems can be leveraged to enhance cultural representation in educational practices. By analyzing data on student participation and engagement across various cultural backgrounds, this research seeks to identify patterns, biases, and opportunities for improvement. The study will utilize machine learning algorithms to process and analyze large-scale datasets, providing valuable insights into potential disparities in educational participation based on cultural factors. Additionally, qualitative methods such as interviews and focus groups will be conducted to gather perspectives from key stakeholders, including students, teachers, and administrators.
The findings of this research are expected to contribute to the development of more inclusive and culturally responsive educational practices. By leveraging data science and artificial intelligence, educators can gain a deeper understanding of the diverse needs and perspectives of students from different cultural backgrounds. This research also aims to provide practical recommendations for implementing culturally relevant strategies to promote greater participation and engagement among all students. Ultimately, this study aims to bridge the gap between technology and cultural diversity in education, emphasizing the importance of inclusive practices in promoting educational equity.
Potential References:
- Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications
- Information organization and representation in digital cultural heritage in Brazil: Systematic mapping of information infrastructure in digital collections for data science …
- Building diversity, equity, and inclusion within radiology artificial intelligence: representation matters, from data to the workforce
- Cultural differences in an artificial representation of the human emotional brain system: A deep learning study
- Data science and digital art history
