# Neural-Symbolic Knowledge Tracing: Injecting Educational Knowledge into Deep Learning for Responsible Learner Modelling

arXiv: https://arxiv.org/abs/2604.08263 · HTML: https://mlsanity.com/paper/2604.08263/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 43 of its 110 references that are in the map.
- First posted on arXiv 9 Apr 2026.

## Builds on (most cited first, 30 of 43)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Neural Turing Machines](https://mlsanity.com/paper/1410.5401/) (2014)
- [Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods](https://mlsanity.com/paper/1911.02508/) (2019)
- [Understanding the Behaviour of Contrastive Loss](https://mlsanity.com/paper/2012.09740/) (2020)
- [The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision](https://mlsanity.com/paper/1904.12584/) (2019)
- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [Deep Learning for Symbolic Mathematics](https://mlsanity.com/paper/1912.01412/) (2019)
- [Neurosymbolic AI: The 3rd Wave](https://mlsanity.com/paper/2012.05876/) (2020)
- [Neural-Symbolic Learning and Reasoning: A Survey and Interpretation](https://mlsanity.com/paper/1711.03902/) (2017)
- [Context-Aware Attentive Knowledge Tracing](https://mlsanity.com/paper/2007.12324/) (2020)
- [A Self-Attentive model for Knowledge Tracing](https://mlsanity.com/paper/1907.06837/) (2019)
- [Dynamic Key-Value Memory Networks for Knowledge Tracing](https://mlsanity.com/paper/1611.08108/) (2016)
- [EKT: Exercise-aware Knowledge Tracing for Student Performance Prediction](https://mlsanity.com/paper/1906.05658/) (2019)
- [Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing](https://mlsanity.com/paper/2002.07033/) (2020)
- [Knowledge Tracing: A Survey](https://mlsanity.com/paper/2201.06953/) (2022)
- [Unifying AI Tutor Evaluation: An Evaluation Taxonomy for Pedagogical Ability Assessment of LLM-Powered AI Tutors](https://mlsanity.com/paper/2412.09416/) (2024)
- [Knowledge Tracing with Sequential Key-Value Memory Networks](https://mlsanity.com/paper/1910.13197/) (2019)
- [Addressing Two Problems in Deep Knowledge Tracing via Prediction-Consistent Regularization](https://mlsanity.com/paper/1806.02180/) (2018)
- [GIKT: A Graph-based Interaction Model for Knowledge Tracing](https://mlsanity.com/paper/2009.05991/) (2020)
- [How Different Groups Prioritize Ethical Values for Responsible AI](https://mlsanity.com/paper/2205.07722/) (2022)
- [Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens](https://mlsanity.com/paper/2508.01191/) (2025)
- [Evaluating the Effectiveness of LLMs in Introductory Computer Science Education: A Semester-Long Field Study](https://mlsanity.com/paper/2404.13414/) (2024)
- [Large Language Models are Biased Because They Are Large Language Models](https://mlsanity.com/paper/2406.13138/) (2024)
- [Deep Knowledge Tracing and Dynamic Student Classification for Knowledge Tracing](https://mlsanity.com/paper/1809.08713/) (2018)
- [LLM-powered Multi-agent Framework for Goal-oriented Learning in Intelligent Tutoring System](https://mlsanity.com/paper/2501.15749/) (2025)
- [QuesNet: A Unified Representation for Heterogeneous Test Questions](https://mlsanity.com/paper/1905.10949/) (2019)
- [Can Large Language Models Match Tutoring System Adaptivity? A Benchmarking Study](https://mlsanity.com/paper/2504.05570/) (2025)
- [Deep Graph Memory Networks for Forgetting-Robust Knowledge Tracing](https://mlsanity.com/paper/2108.08105/) (2021)

## Built on by (most cited first, 1 of 1)

- [Agentic Education: Using Claude Code to Teach Claude Code](https://mlsanity.com/paper/2604.17460/) (2026)

## Abstract (arXiv)

The growing use of artificial intelligence (AI) in education, particularly large language models (LLMs), has increased interest in intelligent tutoring systems. However, LLMs often show limited adaptivity and struggle to model learners' evolving knowledge over time, highlighting the need for dedicated learner modelling approaches. Although deep knowledge tracing methods achieve strong predictive performance, their opacity and susceptibility to bias can limit alignment with pedagogical principles. To address this, we propose Responsible-DKT, a neural-symbolic deep knowledge tracing approach that integrates symbolic educational knowledge (e.g., mastery and non-mastery rules) into sequential neural models for responsible learner modelling. Experiments on a real-world dataset of students' math interactions show that Responsible-DKT outperforms both a neural-symbolic baseline and a fully data-driven PyTorch DKT model across training settings. The model achieves over 0.80 AUC with only 10% of training data and up to 0.90 AUC, improving performance by up to 13%. It also demonstrates improved temporal reliability, producing lower early- and mid-sequence prediction errors and the lowest prediction inconsistency rates across sequence lengths, indicating that prediction updates remain directionally aligned with observed student responses over time. Furthermore, the neural-symbolic approach offers intrinsic interpretability via a grounded computation graph that exposes the logic behind each prediction, enabling both local and global explanations. It also allows empirical evaluation of pedagogical assumptions, revealing that repeated incorrect responses (non-mastery) strongly influence prediction updates. These results indicate that neural-symbolic approaches enhance both performance and interpretability, mitigate data limitations, and support more responsible, human-centered AI in education.