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1. Can Large Language Models Generate Middle School Mathematics Explanations Better than Human Teachers?

   Wang, A ; Prihar, E ; Heffernan, N ; Lee, M ; Hopman, M ; Kalai, A.T ; Vempala, S ; Wickline, G ( January 2024 , LAK 2024 (submitted, in review))

   The development and measurable improvements in performance of large language models on natural language tasks opens the opportunity to utilize large language models in an educational setting to replicate human tutoring, which is often costly and inaccessible. We are particularly interested in large language models from the GPT series, created by OpenAI. In the original study we found that the quality of explanations generated with GPT-3.5 was poor, where two different approaches to generating explanations resulted in a 43% and 10% successrate. In a replication study, we were interested in whether the measurable improvements in GPT-4 performance led to a higher rate of success for generating valid explanations compared to GPT-3.5. A replication of the original study was conducted by using GPT-4 to generate explanations for the same problems given to GPT-3.5. Using GPT-4, explanation correctness dramatically improved to a success rate of 94%. We were further interested in evaluating if GPT-4 explanations were positively perceived compared to human-written explanations. A preregistered, follow-up study was implemented where 10 evaluators were asked to rate the quality of randomized GPT-4 and teacher-created explanations. Even with 4% of problems containing some amount of incorrect content, GPT-4 explanations were preferred over human explanations. 

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2. Leveraging Large Language Models for Evaluating Explanations in Math Education. Learning Analytics and Knowledge

   Worden, E ; Croteau, E ; Cheng, L ; McReynolds, A ; Heffernan, N ( January 2024 , LAK 2024 (submitted, in review))

   Human-conducted rating tasks are resource-intensive and demand significant time and financial commitments. As Large Language Models (LLMs) like GPT emerge and exhibit prowess across various domains, their potential in automating such evaluation tasks becomes evident. In this research, we leveraged four prominent LLMs: GPT-4, GPT-3.5, Vicuna, and PaLM 2, to scrutinize their aptitude in evaluating teacher-authored mathematical explanations. We utilized a detailed rubric that encompassed accuracy, explanation clarity, the correctness of mathematical notation, and the efficacy of problem-solving strategies. During our investigation, we unexpectedly discerned the influence of HTML formatting on these evaluations. Notably, GPT-4 consistently favored explanations formatted with HTML, whereas the other models displayed mixed inclinations. When gauging Inter-Rater Reliability (IRR) among these models, only Vicuna and PaLM 2 demonstrated high IRR using the conventional Cohen’s Kappa metric for explanations formatted with HTML. Intriguingly, when a more relaxed version of the metric was applied, all model pairings showcased robust agreement. These revelations not only underscore the potential of LLMs in providing feedback on student-generated content but also illuminate new avenues, such as reinforcement learning, which can harness the consistent feedback from these models. 

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3. Assessing Student Explanations with Large Language Models Using Fine-Tuning and Few-Shot Learning

   Carpenter, D ; Min, W ; Lee, S ; Ozogul, G ; Zheng, X ; Lester, J ( June 2024 , Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications, Association for Computational Linguistics)

   Kochmar, E ; Bexte, M ; Burstein, J ; Horbach, A ; Laarmann-Quante, R ; Tack, A ; Yaneva, V ; Yuan, Z (Ed.)

   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. 

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4. PROC2PDDL: Open-Domain Planning Representations from Texts

   https://doi.org/10.18653/v1/2024.nlrse-1.2  

   Zhang, Tianyi ; Zhang, Li ; Hou, Zhaoyi ; Wang, Ziyu ; Gu, Yuling ; Clark, Peter ; Callison-Burch, Chris ; Tandon, Niket ( January 2024 , Association for Computational Linguistics)

   Planning in a text-based environment continues to be a significant challenge for AI systems. Recent approaches have utilized language models to predict planning domain definitions (e.g., PDDL) but have only been evaluated in closed-domain simulated environments. To address this, we present Proc2PDDL, the first dataset containing open-domain procedural texts paired with expert-annotated PDDL representations. Using this dataset, we evaluate the task of predicting domain actions (parameters, preconditions, and effects). We experiment with various large language models (LLMs) and prompting mechanisms, including a novel instruction inspired by the zone of proximal development (ZPD), which reconstructs the task as incremental basic skills. Our results demonstrate that Proc2PDDL is highly challenging for end-to-end LLMs, with GPT-3.5’s success rate close to 0% and GPT-4o’s 38%. With ZPD instructions, GPT-4o’s success rate increases to 45%, outperforming regular chain-of-thought prompting’s 34%. Our analysis systematically examines both syntactic and semantic errors, providing insights into the strengths and weaknesses of language models in generating domain-specific programs. 

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5. Augmenting Training Data for a Virtual Character Using GPT-3.5

   https://doi.org/10.32473/flairs.37.1.135552  

   Chen, Elizabeth ; Artstein, Ron ( May 2024 , The International FLAIRS Conference Proceedings)

   This paper compares different methods of using a large language model (GPT-3.5) for creating synthetic training data for a retrieval-based conversational character. The training data are in the form of linked questions and answers, which allow a classifier to retrieve a pre-recorded answer to an unseen question; the intuition is that a large language model could predict what human users might ask, thus saving the effort of collecting real user questions as training data. Results show small improvements in test performance for all synthetic datasets. However, a classifier trained on only small amounts of collected user data resulted in a higher F-score than the classifiers trained on much larger amounts of synthetic data generated using GPT-3.5. Based on these results, we see a potential in using large language models for generating training data, but at this point it is not as valuable as collecting actual user data for training.

    

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