AI-Safety Respecting Architectures for Use in Education

The goal of using AI in education is to make learning more enjoyable and efficient by automating what can be automated, allowing teachers to spend more time individually with each student.

AIs can collect learning text and all interactions with each student into a private per-student vector embeddings document database. This allows both the teacher and student to “chat” with the AI in the context of the individual student. The AI can use this information to provide personalized recommendations and feedback to the student.

To ensure privacy, the student can keep private encrypted notes with the AI that only the student can “chat” about. This allows the student to have a private and secure way to communicate with the AI and receive personalized feedback.

AI can be used to determine which topics individual students are having problems with and suggest new material and a plan for improvement to both the student and teacher. For example, new AI systems are being developed to help teachers administer more effective testing that could uncover often-hidden conditions. Once these conditions can be properly identified, educators can tap into the resources available for a learning disability.

Large Language Models (LLMs) used by AI can be fine-tuned with appropriate data concerning fairness, social justice, respect for other people, etc. This involves training the LLMs on data that reflects these values and minimizing potential sources of bias in the training data. Techniques such as learning from human feedback can also be used to improve the model’s behavior and ensure that it aligns with these values.

By fine-tuning LLMs with appropriate data, the AI can provide recommendations and feedback that are fair, respectful, and socially just.

There are several best practices that can be followed to ensure AI safety respecting architectures for use in educational AI systems. One approach is to use open-source foundation models for building custom domain-specific LLMs. OpenAI has developed a set of best practices applicable to any organization developing or deploying large language models (LLMs). These include publishing usage guidelines and terms of use of LLMs that prohibit material harm to individuals, communities, and society. They also recommend building systems and infrastructure to enforce usage guidelines.

There are several AI tools that can be used in education to automate tasks such as walking students through training material, automating testing and evaluation, and providing teachers with review material specific to each individual student for 1-on-1 teacher/student sessions. These types of tools rely on a combination of machine learning (ML) and AI to make it easier to grade, which saves time and energy.

It is important to ensure that AI systems are built, deployed, and used safely. OpenAI has developed a set of best practices for ensuring AI safety. These include conducting rigorous testing prior to releasing any new system, engaging external experts for feedback, working to improve the model’s behavior with techniques like reinforcement learning with human feedback, and building broad safety and monitoring systems.