MathAssist: An Interactive Mobile Application for Mathematics Learners Specially for Developing Countries Using AI, LLM And RAG Based Approach

Mathematics forms the foundation of science, engineering,
and technology education . However, many students struggle with mathematical concepts due to limited personalised guidance, insufficient learning resources, and difficulties in understanding problem-solving procedures . These challenges
are more severe in developing countries such as Bangladesh,
where approximately 16.4 per cent of students face difficulties
in mathematics and 53.1 per cent fail to achieve satisfactory
performance.
Recent advances in Artificial Intelligence (AI) have enabled
intelligent tutoring systems capable of providing adaptive
learning support and automated problem-solving assistance. Nevertheless, most existing solutions, including Photomath and Khan Academy, rely heavily on cloud computing and continuous internet connectivity. Such requirements
limit their applicability in rural and resource-constrained regions of Bangladesh, where internet access and digital infrastructure remain inadequate.
To address these challenges, this paper proposes Math
Assist, an offline AI-assisted mobile learning framework
that combines Retrieval-Augmented Generation (RAG) and
lightweight Large Language Models (LLMs) to provide
context-aware and step-by-step mathematical assistance using
verified educational content retrieved from a local knowledge
base. The proposed memory-efficient Serial RAG architecture
enables deployment on low-resource mobile devices while
improving the reliability of mathematical reasoning.
The major contributions of this work are summarised as
follows:
• Development of an offline AI-assisted mathematics learning framework with multimodal interaction capabilities
and lightweight execution suitable for devices with less
than 2 GB RAM.
• Design of a novel Serial RAG strategy that integrates
semantic retrieval and local knowledge indexing with
LLM-based reasoning, where experimental benchmarking
identifies WizardMath as the most effective mathematical reasoning model among the evaluated LLMs