Towards Responsible AI in Education: Hybrid Recommendation System for K-12 Students Case Study
Project Overview
The document explores the implementation of generative AI in education through a case study focused on a hybrid recommendation system designed for K-12 students. This system utilizes a combination of graph-based modeling and matrix factorization to deliver personalized learning experiences by providing customized suggestions for activities, resources, and opportunities tailored to individual student needs. A significant aspect of the study is its emphasis on addressing fairness and bias, underscoring the importance of continuous monitoring and ethical considerations in AI applications. Additionally, it advocates for the development of frameworks that ensure equitable access to educational resources, thereby enhancing the overall learning experience while mitigating the risk of disparities among students. The findings indicate that generative AI has the potential to significantly improve personalized education, provided that ethical implications and access equity are prioritized in its deployment.
Key Applications
Hybrid recommendation system for K-12 students
Context: Public school districts targeting K-12 students
Implementation: Developed by SoftServe Inc. for Mesquite Independent School District, utilizing a hybrid approach that combines graph-based methods and matrix factorization.
Outcomes: Personalized recommendations leading to enhanced student engagement, curiosity, and skill development while addressing fairness and bias in AI recommendations.
Challenges: Potential biases in AI recommendations impacting access to resources for marginalized groups.
Implementation Barriers
Technical
Unintentional biases in AI-based systems which may limit fair access to educational resources.
Proposed Solutions: Integration of a fairness analysis framework to systematically evaluate and mitigate biases.
Ethical
Concerns about data privacy and ethical use of student data.
Proposed Solutions: Ensuring informed consent, data anonymization, and compliance with legal standards.
Project Team
Nazarii Drushchak
Researcher
Vladyslava Tyshchenko
Researcher
Nataliya Polyakovska
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Nazarii Drushchak, Vladyslava Tyshchenko, Nataliya Polyakovska
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai