Auto-Evaluation: A Critical Measure in Driving Improvements in Quality and Safety of AI-Generated Lesson Resources
Project Overview
The document explores the innovative use of generative AI in education through the development of Aila, an AI-powered lesson planning tool created by Oak National Academy. Aila aims to enhance lesson quality, alleviate teacher workload, and ensure alignment with educational standards by utilizing an auto-evaluation agent that assesses the quality of lesson outputs based on a comprehensive corpus of 13,000 Open Educational Resources (OER). Key applications of Aila focus on generating accurate and pedagogically sound educational materials. However, the implementation of such AI tools is not without challenges, as it raises concerns regarding the evidence of effectiveness and the potential for biases in the AI-generated content. Overall, the document underscores the promise of generative AI in transforming educational practices while acknowledging the critical need for rigorous evaluation and ethical considerations in its application.
Key Applications
Aila - AI Lesson Assistant
Context: Designed for UK teachers to assist with lesson planning and resource generation.
Implementation: Developed using a corpus of 13,000 OERs, incorporating an auto-evaluation agent for quality assessment.
Outcomes: Improved accuracy of lesson content, reduced teacher workload, and enhanced engagement in lesson planning.
Challenges: Limited robust evidence on the effectiveness of generative AI in education and potential biases in AI-generated content.
Implementation Barriers
Implementation Barrier
Lack of robust evidence on the effectiveness and impact of generative AI on teaching and learning.
Proposed Solutions: Conducting trials and research studies to establish the effectiveness and best practices for AI tools in education.
Content Accuracy Barrier
Potential inaccuracies and biases in AI-generated content that could lead to misconceptions in the classroom.
Proposed Solutions: Developing auto-evaluation tools to assess the quality and accuracy of AI-generated lessons and refining these tools through iterative evaluations.
Project Team
Hannah-Beth Clark
Researcher
Margaux Dowland
Researcher
Laura Benton
Researcher
Reka Budai
Researcher
Ibrahim Kaan Keskin
Researcher
Emma Searle
Researcher
Matthew Gregory
Researcher
Mark Hodierne
Researcher
William Gayne
Researcher
John Roberts
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Hannah-Beth Clark, Margaux Dowland, Laura Benton, Reka Budai, Ibrahim Kaan Keskin, Emma Searle, Matthew Gregory, Mark Hodierne, William Gayne, John Roberts
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