# TrueReason: An Exemplar Personalised Learning System Integrating Reasoning with Foundational Models

arXiv: https://arxiv.org/abs/2502.10411 · HTML: https://mlsanity.com/paper/2502.10411/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 27 of its 72 references that are in the map.
- First posted on arXiv 23 Jan 2025.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 27 of 27)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Plug and Play Language Models: A Simple Approach to Controlled Text Generation](https://mlsanity.com/paper/1912.02164/) (2019)
- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [Learning to Ask: Neural Question Generation for Reading Comprehension](https://mlsanity.com/paper/1705.00106/) (2017)
- [TLDR: Extreme Summarization of Scientific Documents](https://mlsanity.com/paper/2004.15011/) (2020)
- [A Distributional Approach to Controlled Text Generation](https://mlsanity.com/paper/2012.11635/) (2020)
- [Knowledge Tracing: A Survey](https://mlsanity.com/paper/2201.06953/) (2022)
- [SAINT+: Integrating Temporal Features for EdNet Correctness Prediction](https://mlsanity.com/paper/2010.12042/) (2020)
- [Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agents](https://mlsanity.com/paper/2405.16533/) (2024)
- [Learning to Use Tools via Cooperative and Interactive Agents](https://mlsanity.com/paper/2403.03031/) (2024)
- [Empowering Personalized Learning through a Conversation-based Tutoring System with Student Modeling](https://mlsanity.com/paper/2403.14071/) (2024)
- [Scalable Educational Question Generation with Pre-trained Language Models](https://mlsanity.com/paper/2305.07871/) (2023)
- [TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources](https://mlsanity.com/paper/1911.09471/) (2019)
- [How Teachers Can Use Large Language Models and Bloom's Taxonomy to Create Educational Quizzes](https://mlsanity.com/paper/2401.05914/) (2024)
- [Generative AI for Education (GAIED): Advances, Opportunities, and Challenges](https://mlsanity.com/paper/2402.01580/) (2024)
- [PEEK: A Large Dataset of Learner Engagement with Educational Videos](https://mlsanity.com/paper/2109.03154/) (2021)
- [ARN: Analogical Reasoning on Narratives](https://mlsanity.com/paper/2310.00996/) (2023)
- [EduCOR: An Educational and Career-Oriented Recommendation Ontology](https://mlsanity.com/paper/2107.05522/) (2021)
- [Semantic TrueLearn: Using Semantic Knowledge Graphs in Recommendation Systems](https://mlsanity.com/paper/2112.04368/) (2021)
- [Watch Less and Uncover More: Could Navigation Tools Help Users Search and Explore Videos?](https://mlsanity.com/paper/2201.03408/) (2022)
- [A Toolbox for Modelling Engagement with Educational Videos](https://mlsanity.com/paper/2401.05424/) (2023)
- [Learning Cognitive Models using Neural Networks](https://mlsanity.com/paper/1806.08065/) (2018)
- [Can Population-based Engagement Improve Personalisation? A Novel Dataset and Experiments](https://mlsanity.com/paper/2207.01504/) (2022)

## Built on by (most cited first, 2 of 2)

- [Catching The Correct Answer Trap: Characterising AI Tutor Blind Spots When Analysing Student Reasoning](https://mlsanity.com/paper/2605.23925/) (2026)
- [The Correct Answer Trap: Pedagogically-Grounded Detection and Feedback for Hidden Misconceptions](https://mlsanity.com/paper/2606.23205/) (2026)

## Abstract (arXiv)

Personalised education is one of the domains that can greatly benefit from the most recent advances in Artificial Intelligence (AI) and Large Language Models (LLM). However, it is also one of the most challenging applications due to the cognitive complexity of teaching effectively while personalising the learning experience to suit independent learners. We hypothesise that one promising approach to excelling in such demanding use cases is using a society of minds. In this chapter, we present TrueReason, an exemplar personalised learning system that integrates a multitude of specialised AI models that can mimic micro skills that are composed together by a LLM to operationalise planning and reasoning. The architecture of the initial prototype is presented while describing two micro skills that have been incorporated in the prototype. The proposed system demonstrates the first step in building sophisticated AI systems that can take up very complex cognitive tasks that are demanded by domains such as education.