The Future of Learning in the Age of Generative AI: Automated Question Generation and Assessment with Large Language Models
Project Overview
Generative AI, particularly through Large Language Models (LLMs), is poised to transform education by enabling automated question generation and answer assessment, thereby fostering personalized learning experiences. These models can create a wide array of diverse and contextually relevant questions, as well as offer automated feedback on student responses, which can enhance engagement and comprehension. However, the implementation of LLMs in educational settings is not without challenges; concerns regarding the accuracy of the generated content, ethical implications surrounding data usage, and the necessity for human oversight in evaluation processes must be addressed. As educators and institutions seek to integrate AI tools, careful consideration of these factors is essential to ensuring effective and responsible use of technology in enhancing learning outcomes. Ultimately, the strategic application of generative AI has the potential to significantly enrich the educational landscape, provided that it is approached with caution and a commitment to maintaining educational integrity.
Key Applications
Automated Question Generation and Answer Assessment using LLMs
Context: Education, targeting students and educators in various subjects
Implementation: LLMs are fine-tuned and prompt-tuned to generate questions and assess answers based on student inputs.
Outcomes: Enhanced learning through tailored questions, immediate feedback, identification of misconceptions, and reduced human workload.
Challenges: Variability in question quality, the need for accurate assessments, bias in AI outputs.
Implementation Barriers
Technical Barrier
Accuracy and consistency of assessments generated by LLMs can vary, leading to potential biases or incorrect evaluations.
Proposed Solutions: Ongoing monitoring and evaluation of outputs; fine-tuning models with high-quality datasets.
Ethical Barrier
Concerns about fairness, transparency, and data privacy in the use of AI in education.
Proposed Solutions: Ensuring transparency in AI processes and protecting sensitive student data.
Project Team
Subhankar Maity
Researcher
Aniket Deroy
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Subhankar Maity, Aniket Deroy
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