Student Data Paradox and Curious Case of Single Student-Tutor Model: Regressive Side Effects of Training LLM s for Personalized Learning (2024)

Abstract: The pursuit of personalized education has led to the integration of Large Language Models (LLMs) in developing intelligent tutoring systems. To better understand and adapt to individual student needs, including their misconceptions, LLMs need to be trained on extensive datasets of student-tutor dialogues. Our research uncovers a fundamental challenge in this approach: the “Student Data Paradox”. This paradox emerges when LLMs, trained on student data to understand learner behavior, inadvertently compromise their own factual knowledge and reasoning abilities. We investigate this paradox by training state-of-the-art language models on student-tutor dialogue datasets and evaluating their performance across multiple benchmarks. These benchmarks assess various aspects of language model capabilities, including reasoning, truthfulness, and common sense understanding. Our findings reveal significant declines in the models’ performance across these diverse benchmarks, indicating a broad impact on their capabilities when trained to model student behavior. Our research makes two primary contributions: (1) empirical demonstration of the Student Data Paradox through quantitative analysis of model performance, and (2) introduction of “hallucination tokens” as a mitigation strategy. These tokens, while improving performance, highlight the persistent challenge of balancing accurate student behavior modeling with maintaining the LLM’s integrity as an educational tool.This study emphasizes the need for innovative solutions to reconcile the conflicting goals of faithfully understanding diverse student cognition while preserving the model’s ability to provide accurate information and guidance.

## introduction personalized education is undergoing a significant transformation, driven by the rapid advancement of large language models (llms) @xcite . these ai systems are being developed to serve as adaptive tutors, capable of understanding and responding to individual student needs @xcite @xcite . the incorporation of student data into the training process is an important step towards creating truly personalized learning experiences. by training llms on diverse student data, including dialogues, errors, and problem-solving approaches, these models can learn to identify knowledge gaps, recognize common misconceptions, and provide personalized support tailored to each learner's unique needs. however, this approach is at odds with traditional llm training, where data quality is of utmost importance @xcite . student data, by nature, is prone to errors and misconceptions, which raises concerns about the impact on the llms' factual knowledge and reasoning abilities. this tension between the need to understand student misconceptions accurately and the imperative to maintain the model's factual integrity creates a paradoxical challenge, which we term the "student data paradox." in this paper, we study this timely topic: what happens when we train an llm on student-tutor interaction data? our research reveals that while training llms on student data can enable models to accurately simulate authentic student behaviors and misconception, it comes at a significant cost -llms' own factual integrity and reasoning capabilities are compromised. this paradox poses a serious concern, as the primary purpose of any educational model is to provide accurate and reliable information to learners. we investigated this paradox by fine-tuning llama @xcite and vicuna models @xcite , on the class dataset @xcite -a collection of student-tutor dialogues on college-level biology questions (example in figure 1 ). training llms to simulate student behavior significantly decreased the models' figure 1 : training llms on student-tutor dialogues to model student behavior: an example from the class dataset. this approach creates "student data paradox": while the model accurately simulates student responses and misconceptions, its own factual knowledge and reasoning abilities are compromised. we introduce hallucination tokens @xcite , [/hal]) during training to instruct the model when to replicate misconceptions versus provide accurate information, showing promise in mitigating the paradox. a seemingly intuitive solution to the paradox would be to create separate student and tutor models. however, we present a formal theorem demonstrating that this approach necessitates potentially vast number of student models, making it impractical. this finding emphasizes the need for a unified framework to capture both accurate knowledge and diverse student misconceptions. to this end to counteract the side effects, we propose to incorporate novel start and end hallucination tokens ([hal] and [/hal]) into the llm training process. these tokens, placed at the beginning and end of each student response, serve as cues to the model, instructing it when to differentiate between providing accurate responses and replicating student misconceptions. our results indicate a substantial improvement in the model's performance across all datasets after introducing this token. however, these tokens do not fully restore the model's baseline performance, underscoring the complexity of the issue. contributions: our research makes important contributions to the use of llms in modeling student behavior for adaptive tutoring systems. we identify and study the student data paradox in llms trained on student-tutor interactions, revealing a trade-off between accurately modeling student misconceptions and maintaining the llm's factual integrity and reasoning ability (section 3 and 4). through a formal theorem discussed in section 2, we show that the intuitive solution of separating student and tutor models is fundamentally flawed, necessitating a more sophisticated, unified approach. we propose hallucination tokens as a method for this unified approach. these tokens, integrated into the training process, enable llms to differentiate between modeling student misconceptions and providing accurate information, significantly improving model performance. our study exposes the critical trade-offs in training llms on student data and offers a concrete solution with hallucination tokens. this work lays the foundation for developing more robust and effective ai tutors that can accurately model student behavior without compromising factual integrity. ## methodology our methodology is divided into three main parts: data preparation, model training, and the incorporation of hallucination tokens. ## experiments and discussion in this section, we present our experimental methodology and discuss the findings in detail. the experiments were designed to explore the regressive side effects of training llms to model student behavior and to assess the effectiveness of our proposed hallucination tokens in mitigating these effects. ## related work the intersection of artificial intelligence and education has been an area of active research, with a focus on developing systems that can adapt to and support individual learners. our work touches upon several research domains, including student modeling, the design of intelligent tutoring systems, and the deployment of large language models (llms) in educational contexts. ## conclusion in this study, we have delved into the student data paradox, a critical challenge that arises when training llms on student data for personalized education. our findings reveal a complex trade-off: as llms become more adept at modeling student misconceptions, they tend to compromise their own factual integrity and reasoning abilities. we term this phenomenon the regressive side effects of the student data paradox. our experiments demonstrated a notable decrease in the model's performance across various key benchmark datasets like arc reasoning challenge and truthfulqa. to mitigate these regressive side effects, we introduced a novel technique involving the use of hallucination tokens during the training process. our results indicate that the introduction of these tokens leads to a substantial improvement in the model's performance across all datasets. however, it's important to note that despite the significant improvements achieved with the hallucination tokens, they do not fully restore the model's baseline performance. this outcome underscores the complexity of the problem and highlights the need for a more nuanced approach when training llms to mimic student behavior. while we have made some strides in addressing the regressive side effects, our work is just the beginning. we believe that our findings will pave the way for further research in this domain, ultimately contributing to the refinement of llms in personalized learning environments.