CIKT: A Collaborative Iterative Knowledge Tracing Framework Based on Large Language Models

CIKT (Collaborative and Iterative Knowledge Tracing) is an innovative knowledge tracing framework that integrates large language models (LLMs). It consists of two core mechanisms: collaboration and iteration, aiming to provide more accurate learner ability modeling for personalized education. This article will analyze it from aspects such as background, core innovations, technical architecture, and application scenarios.

Knowledge tracing is a core technology of intelligent education systems, whose goal is to model the mastery level of knowledge points based on learners' historical answer records and predict future performance. Its core problems are: 1. The current mastery level of each knowledge point by the learner; 2. The probability of answering the next question correctly. Traditional methods such as Bayesian-based BKT and deep learning models DKT/DKVMN have limitations in handling complex learning behavior patterns and knowledge associations.

The core innovations of CIKT include: 1. Collaborative learning: Using multi-learner data to discover common patterns, achieve data augmentation, alleviate cold start, and aggregate group wisdom; 2. Iterative optimization: Dynamically adapting to learning changes through multiple iterations, correcting wrong estimates, and supporting deep reasoning; 3. LLM empowerment: Realizing semantic understanding (extracting semantic features of questions/knowledge points), knowledge association reasoning (identifying implicit associations between knowledge points), and generative explanation (providing learning status feedback and suggestions).

The CIKT technical architecture includes: 1. Input layer: Fusing answer sequences, question texts, knowledge point descriptions, and learner portraits; 2. Encoding layer: Dual-path modeling (sequence modeling captures temporal dependencies, semantic encoding uses LLMs to extract deep semantics); 3. Collaboration layer: Aggregating knowledge states of similar learners through attention/graph neural networks; 4. Iteration layer: Multiple rounds of refinement of knowledge state estimation; 5. Output layer: Multi-task prediction (answer accuracy, knowledge point mastery evaluation, learning path recommendation, etc.).

CIKT has important value in multiple scenarios: 1. Intelligent tutoring systems: Accurately identifying weak points, dynamically adjusting question difficulty, and recommending optimal learning paths; 2. Adaptive testing: Efficiently evaluating ability levels, intelligently selecting questions, and updating ability estimates in real time; 3. Enterprise training: Building skill maps, personalized recommendation of training content, and quantifying training effects.

Challenges and countermeasures of introducing LLMs: 1. Computational efficiency: Using model distillation, quantization, or caching strategies to optimize performance; 2. Data privacy: Protecting sensitive information through federated learning and differential privacy; 3. Interpretability: Designing explanation mechanisms to improve the transparency of system decisions.

CIKT integrates LLM semantic understanding with traditional sequence modeling, improving prediction accuracy and endowing knowledge tracing with 'understanding' capabilities. Future prospects include: multi-modal fusion (video/audio materials), cross-disciplinary knowledge graph construction, expansion of real-time collaboration scenarios, and combination with generative AI to realize intelligent Q&A tutoring.