In our last blog, we explored the transformative role of AI in personalized learning and how TimelyGrader adapts to individual student needs. While the advantages of AI-driven feedback are evident, we also recognized some significant challenges — such as feedback overload, limited contextual understanding, and difficulty with open-ended questions — that accompany Gen AI. If you haven’t yet read that blog, we recommend doing so [here] before continuing. In this blog, we’re excited to share how we are addressing these challenges.
The solution we’re focusing on today is simple: RAG.
Ok… it’s not quite that simple. To start with, what is RAG?
Retrieval-augmented generation (RAG) is an advanced AI technique that enhances the quality of generated responses by combining real-time information retrieval with natural language processing. In essence, RAG allows an AI model to search for and pull in relevant external information from vast datasets or knowledge bases before generating its final response. This approach helps the AI provide more accurate, contextually rich, and nuanced feedback, especially in complex or open-ended scenarios where additional context is crucial.
As you can see from the diagram above, instead of going directly from Prompt → Generator → Response, RAG pulls in relevant external information before delivering a response. Check out this great introductory video for a more detailed explanation.
Here’s the game-changer: We’re developing a system that allows instructors to build their own content bank, which the AI will use to pull relevant information. By incorporating resources like feedback templates and course syllabi, this customizable content bank gives instructors the ability to tailor the additional context that the AI pulls from.
So how can this tackle the concerns mentioned earlier?
One of the primary concerns with AI-generated feedback is managing feedback overload. Without RAG, current models can continually identify new areas for improvement but often lack the ability to discern when to stop providing feedback. Excessive feedback can overwhelm students, making it challenging for them to concentrate on the most critical areas for improvement.
RAG addresses this challenge by enhancing feedback prioritization and summarization. It retrieves relevant information and context from an instructor’s curated content bank, allowing the AI to focus on the most critical aspects of a student's work. By integrating this external context, RAG can help the AI filter and rank feedback based on relevance and impact, ensuring that students receive targeted and actionable comments.
AI’s limitation in understanding broader context has been a significant challenge, particularly in assignments requiring deep contextual knowledge or multidisciplinary perspectives.
By creating a content bank, instructors can provide materials such as relevant reference documents that give the AI insight into the broader context. The AI can retrieve and incorporate this information to generate more nuanced responses. This approach bridges the gap between AI efficiency and human insight, ensuring the feedback is accurate and contextually rich.
Complex or open-ended questions often present a challenge for AI, as they require deep understanding and the ability to synthesize information from multiple sources.
With RAG, instructors can enhance the AI’s ability to handle complex questions by providing detailed guidelines and supplementary materials as part of their content bank. By incorporating specific guidelines, feedback templates, and additional context, instructors equip the AI with the necessary tools to approach complex queries more effectively.
As TimelyGrader continues to advance the capabilities of AI in education, we are focused on integrating solutions like RAG to address critical challenges like the ones discussed in this blog. Stay tuned as we continue to refine and expand our solutions, driving forward the future of personalized learning!
Enhancing education with AI-powered grading and feedback.
