Lecture-style instruction is one of the most prevalent forms of learning in postsecondary education in the United States. Despite the factors that make lectures a convenient format, they tend to present few opportunities for meaningful engagement between students and the course materials being presented due to factors such as the overhead associated with interacting with large numbers of students. By utilizing large language models, we have created a pipeline built upon the ExplainIt classroom response system for processing student self-explanations produced during lectures using automatically generated knowledge components. This pipeline can facilitate deeper engagement with course materials, offer traceability in assessment results, and allows instructors to respond to student errors or misconceptions in real-time during lecture. While previous work using a proprietary large language model has examined the basic functionality of this pipeline, this work more closely examines the consistency and quality of this pipeline using both a large closed-weight model and a smaller open-weight model, with or without retrieval-augmented generation (RAG). The use of open-source models could allow institutions deploying ExplainIt to maintain control of their student data without substantially sacrificing performance. We find that while there are small statistically significant differences in performance between the RAG conditions of each LLM, they are nearly comparable at this task. Additionally, the LLM-generated knowledge components are of higher quality when relevant course material is provided for RAG, although consistency is not improved. These results indicate that both large closed-weight and smaller open-weight models show promise in this task, but fine-tuning may be necessary to improve performance further.

Large Language Models (LLMs) often generate hallucinations, producing outputs that are contextually inaccurate or factually incorrect. We introduce HICD, a novel method designed to induce hallucinations for contrastive decoding to mitigate hallucinations. Unlike existing contrastive decoding methods, HICD selects attention heads crucial to the model’s prediction as inducing heads, then induces hallucinations by dispersing attention of these inducing heads and compares the hallucinated outputs with the original outputs to obtain the final result. Our approach significantly improves performance on tasks requiring contextual faithfulness, such as context completion, reading comprehension, and question answering. It also improves factuality in tasks requiring accurate knowledge recall. We demonstrate that our inducing heads selection and attention dispersion method leads to more “contrast-effective” hallucinations for contrastive decoding, outperforming other hallucination-inducing methods. Our findings provide a promising strategy for reducing hallucinations by inducing hallucinations in a controlled manner, enhancing the performance of LLMs in a wide range of tasks.

The practice of soliciting self-explanations from students is widely recognized for its pedagogical benefits. However, the labor-intensive effort required to manually assess students’ explanations makes it impractical for classroom settings. As a result, many current solutions to gauge students’ understanding during class are often limited to multiple choice or fill-in-the-blank questions, which are less effective at exposing misconceptions or helping students to understand and integrate new concepts. Recent advances in large language models (LLMs) present an opportunity to assess student explanations in real-time, making explanation-based classroom response systems feasible for implementation. In this work, we investigate LLM-based approaches for assessing the correctness of students’ explanations in response to undergraduate computer science questions. We investigate alternative prompting approaches for multiple LLMs (i.e., Llama 2, GPT-3.5, and GPT-4) and compare their performance to FLAN-T5 models trained in a fine-tuning manner. The results suggest that the highest accuracy and weighted F1 score were achieved by fine-tuning FLAN-T5, while an in-context learning approach with GPT-4 attains the highest macro F1 score.