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README.md

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+# Knowledge Tracing with Math Solutions
+
+## Motivation
+Knowledge Tracing (KT) is a core research task that models the evolution of a learner’s knowledge state based on their problem-solving history.  
+This capability is essential for **Intelligent Tutoring Systems (ITS)** to provide adaptive feedback and personalized guidance.  
+
+Traditional KT research has primarily relied on student–item interactions in the form of binary correctness (1/0).  
+While deep learning-based models such as **DKT, SAINT, and AKT** have brought notable improvements,  
+they still face **limitations in transferability and generalization** across datasets.
+
+---
+
+## Challenges
+KT continues to face long-standing issues:
+- **Cold start problem**  
+- **Lack of interpretability**
+
+Recent approaches have introduced natural language as a new modality:  
+- **LKT**: models questions as natural language prompts to mitigate cold start  
+- **EFKT**: applies cognitive frameworks to enhance interpretability  
+- **LBMKT**: uses LLM encoders to summarize a learner’s knowledge state in natural language  
+
+These works suggest the potential of natural language to overcome KT limitations, but their performance gains remain modest.
+
+---
+
+## Related Progress in Programming Education
+Programming education has seen stronger improvements by leveraging **richer interaction data** such as:
+- Students’ code submissions  
+- Textual questions  
+
+Recent studies integrating these signals into KT architectures have shown significant improvements.  
+For example, an **ACL 2025 paper** demonstrated that student question texts yielded **state-of-the-art performance** in programming education KT.
+
+---
+
+## Advances in LLMs
+Recent LLMs have enabled more **systematic and consistent step-by-step reasoning** through reinforcement learning and alignment:
+- **Math-Shepherd**: leveraged verifiable reward signals → substantial gains on GSM8K and MATH  
+- **PRM-Guided GFlowNets**: improved reasoning trace quality and diversity → better generalization on unseen datasets  
+
+---
+
+## Our Approach
+Building on these developments, our project integrates **LLM-generated step-by-step math solutions** into KT inputs.  
+This provides **richer interaction signals** beyond simple correctness.
+
+**Hypothesis:**  
+Modeling student–item interactions with synthesized solutions can break through the current performance ceiling of KT models.
+
+---
+
+## Research Question
+> Can incorporating LLM-generated mathematical solutions into KT inputs  
+> push Knowledge Tracing beyond its existing limitations?