Transforming Student Evaluation with Adaptive Intelligence and Performance Analytics
Project Overview
The document explores the transformative role of generative AI in education, focusing on its potential to redefine student assessment methodologies through the implementation of an innovative system utilizing the Gemini API. This system generates customized questions, automates grading, and offers real-time performance analytics, all aimed at enhancing the efficiency, accuracy, and integrity of assessments. By leveraging these capabilities, the system not only streamlines the evaluation process but also fosters a more adaptive learning environment that benefits both students and educators. Key applications include personalized learning experiences and timely feedback mechanisms that help identify individual student needs, ultimately leading to improved educational outcomes. The findings indicate that generative AI can significantly enhance the assessment experience, creating a more responsive and effective educational framework.
Key Applications
AI-powered student assessment system using Gemini API
Context: Higher education institutions focusing on student evaluation
Implementation: Integration of Gemini API for automated question generation, grading, and performance analytics
Outcomes: Improved grading accuracy, instant feedback for students, enhanced assessment integrity, and data-driven learning processes
Challenges: Implementation of anti-cheating mechanisms, ensuring reliability of AI-generated assessments, and addressing concerns about data privacy
Implementation Barriers
Technical Barrier
Challenges in ensuring reliability and consistency of AI-generated assessments and grading
Proposed Solutions: Implementing robust AI models and continuous monitoring of the assessment process
Ethical Barrier
Concerns over data privacy and the potential for bias in AI algorithms
Proposed Solutions: Adopting strict data governance policies and regular audits of AI systems
Project Team
Pushpalatha K S
Researcher
Abhishek Mangalur
Researcher
Ketan Hegde
Researcher
Chetan Badachi
Researcher
Mohammad Aamir
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Pushpalatha K S, Abhishek Mangalur, Ketan Hegde, Chetan Badachi, Mohammad Aamir
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