When Automated Assessment Meets Automated Content Generation: Examining Text Quality in the Era of GPTs
Project Overview
The document explores the role of generative AI, specifically large language models (LLMs) like GPT, in education, with a particular focus on automated essay scoring (AES) and personalized learning. It notes that machine learning models often score GPT-generated texts higher than those written by humans, despite being trained solely on human content, indicating a potential bias in the evaluation process. The findings suggest the necessity for a framework that assesses the interaction between human and AI-generated texts and calls for further empirical research to understand these dynamics better. Additionally, the document discusses the broader applications of generative AI in educational settings, highlighting its potential to enhance learning outcomes and provide personalized educational experiences. However, it also raises concerns regarding the accuracy and biases inherent in AI systems, which pose challenges to effective implementation in academic assessments. Overall, while generative AI presents significant opportunities for innovation in education, it also necessitates careful consideration of its implications and the need for rigorous evaluation to ensure fairness and effectiveness.
Key Applications
Automated Essay Scoring and Personalized Learning Systems
Context: K-12 and higher education settings, including standardized tests (e.g., GMAT, GRE, TOEFL) and writing courses for students requiring tailored learning experiences.
Implementation: Utilizes transformer models (such as BERT and RoBERTa) and neural networks to evaluate and score essays as well as to provide personalized learning experiences through AI-driven recommendations based on individual student performance.
Outcomes: Achieves improved grading efficiency, consistent evaluation of writing quality, enhanced student engagement, and improved learning outcomes through customized pathways.
Challenges: Concerns regarding data privacy, potential biases in scoring, reliability of automated systems, and inaccuracies in assessing creative or non-standard writing styles.
Implementation Barriers
Technological
The effectiveness of ML models for AES can vary significantly based on the type of model used, with traditional models scoring human texts higher than GPT texts. Limitations in the accuracy and reliability of AI models used for educational assessments exist, which can impact grading and feedback.
Proposed Solutions: Adopting transformer-based models which have shown better performance in scoring, though they may still present challenges in accurately assessing GPT-generated content. Continuous improvement of algorithms, regular updates to training data, and incorporation of educator feedback are essential.
Ethical
Concerns about the implications of using generative AI in educational settings, particularly regarding the integrity of assessments and bias in AI systems. There are implications of automated grading on student evaluations, raising questions about fairness and accuracy.
Proposed Solutions: A need for transparent and well-researched methodologies to integrate AI in assessment processes, ensuring that the use of AI complements rather than replaces human judgment. Implementing fairness checks, creating diverse training datasets, and involving educators in the review of AI outputs are crucial.
Logistical Barrier
Challenges in integrating AI tools into existing educational frameworks and teacher training.
Proposed Solutions: Providing professional development for educators, creating clear guidelines for AI use in classrooms, and ensuring adequate resources are available.
Project Team
Marialena Bevilacqua
Researcher
Kezia Oketch
Researcher
Ruiyang Qin
Researcher
Will Stamey
Researcher
Xinyuan Zhang
Researcher
Yi Gan
Researcher
Kai Yang
Researcher
Ahmed Abbasi
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Marialena Bevilacqua, Kezia Oketch, Ruiyang Qin, Will Stamey, Xinyuan Zhang, Yi Gan, Kai Yang, Ahmed Abbasi
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