High Performance Adaptive Learning & Testing System with Safe Dynamic Plugin System
Haozhe Ruan, Sihui Li, Jingwen Guo, Yiyi Wang, Suyu Jiang, Aquil Mirza Mohammed
2026 12th International Conference on Communication and Signal Processing (ICCSP) · Smart Education Systems
Abstract
Modern software systems, particularly in domains like Computerized Adaptive Testing (CAT), demand a balance of high flexibility and robust reliability. This paper elaborates on the implementation of a new CAT system using the Rust programming language. Rust’s innovative ownership model guarantees memory safety without a garbage collector, while its powerful generic programming and meta programming capabilities enable a highly flexible and maintainable architecture. We demonstrate how these features allow us to encapsulate complex adaptive logic into reusable components, reducing both the system’s overall complexity and potential for bugs. The resulting platform aims to provide a stable foundation that is more adaptable than current systems, offering distinct advantages for personalized learning and assessment.
Affiliations
The Hong Kong Polytechnic University (PolyU), Hong Kong · Hubei University of Arts and Science, China
Status: Best Paper Award – Smart Education Systems track, ICCSP 2026 (April 20–22, 2026)
Presenter: Aquil Mirza Mohammed
IEEE copyright completed on February 3, 2026
Keywords
BibTeX
@inproceedings{ruan2026adaptive,
author = {Ruan, Haozhe and Li, Sihui and Guo, Jingwen and Wang, Yiyi and Jiang, Suyu and Mohammed, Aquil Mirza},
title = {High Performance Adaptive Learning \& Testing System with Safe Dynamic Plugin System},
booktitle = {2026 12th International Conference on Communication and Signal Processing (ICCSP)},
year = {2026},
note = {Smart Education Systems track},
keywords = {generic programming, testing, learning, system, plugin}
}