Need of AI in Modern Education: in the Eyes of Explainable AI (xAI)
Project Overview
The document examines the role of generative AI, especially Explainable AI (xAI), in transforming education by personalizing learning experiences, enhancing transparency, and optimizing performance metrics. It underscores the potential of AI to tailor educational content to individual student needs, thus improving engagement and outcomes. However, it also raises critical concerns about biases embedded in AI systems, particularly those influenced by socioeconomic factors like parental income, which can exacerbate existing inequities in educational access and opportunities. The findings stress the importance of developing more reliable, fair, and ethical AI solutions that can effectively support educational policies and practices, ensuring that the advantages of AI are equitably distributed among all students. Ultimately, while generative AI presents significant opportunities for innovation in education, careful consideration of its limitations and biases is essential to foster a more inclusive and effective learning environment.
Key Applications
AI-powered Analytics and Recommendation Systems
Context: Used in online learning platforms and educational settings to provide personalized course recommendations, evaluate student performance comprehensively, enhance grading transparency, and deliver personalized feedback based on student data.
Implementation: Utilizes collaborative filtering algorithms, clustering analysis, predictive analytics, and natural language processing techniques to analyze individual student profiles and various performance factors, generating insights and recommendations tailored to student needs.
Outcomes: Optimizes learning outcomes and engagement by tailoring learning pathways, enabling the development of tailored learning strategies and interventions, enhancing student understanding of grading metrics, and facilitating targeted interventions.
Challenges: Potential biases in recommendations, complexity in data interpretation, reliance on the accuracy of NLP tools, and the need for accurate data collection and analysis to avoid misinterpretations.
Implementation Barriers
Financial
The high costs associated with implementing AI technologies can limit accessibility, particularly for economically disadvantaged students.
Proposed Solutions: Developing cost-effective AI solutions and funding initiatives to support equitable access.
Technical
Complexity and opacity of AI models can undermine trust among educators and students.
Proposed Solutions: Utilizing Explainable AI techniques to enhance transparency and interpretability of AI decisions.
Bias
Biases in AI algorithms can perpetuate educational inequities, particularly influenced by parental income.
Proposed Solutions: Employing fairness auditing tools like FairML to identify and mitigate biases in AI systems.
Cultural/Social
Societal biases can affect the design and implementation of AI in education, leading to unfair outcomes.
Proposed Solutions: Engaging diverse stakeholders in the development process to ensure inclusivity and fairness in AI applications.
Project Team
Supriya Manna
Researcher
Niladri Sett
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Supriya Manna, Niladri Sett
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18