# PEOAT: Personalization-Guided Evolutionary Question Assembly for One-Shot Adaptive Testing

arXiv: https://arxiv.org/abs/2512.00439 · HTML: https://mlsanity.com/paper/2512.00439/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 47 references that are in the map.
- First posted on arXiv 29 Nov 2025.

## Builds on (most cited first, 12 of 12)

- [Trivializations for Gradient-Based Optimization on Manifolds](https://mlsanity.com/paper/1909.09501/) (2019)
- [Neural Cognitive Diagnosis for Intelligent Education Systems](https://mlsanity.com/paper/1908.08733/) (2019)
- [Agent4Edu: Generating Learner Response Data by Generative Agents for Intelligent Education Systems](https://mlsanity.com/paper/2501.10332/) (2025)
- [Can Offline Testing of Deep Neural Networks Replace Their Online Testing?](https://mlsanity.com/paper/2101.11118/) (2021)
- [BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing](https://mlsanity.com/paper/2108.07386/) (2021)
- [Zero-1-to-3: Domain-level Zero-shot Cognitive Diagnosis via One Batch of Early-bird Students towards Three Diagnostic Objectives](https://mlsanity.com/paper/2312.13434/) (2023)
- [Knowledge is Power: Harnessing Large Language Models for Enhanced Cognitive Diagnosis](https://mlsanity.com/paper/2502.05556/) (2025)
- [Connections Between Nuclear Norm and Frobenius Norm Based Representations](https://mlsanity.com/paper/1502.07423/) (2015)
- [RIGL: A Unified Reciprocal Approach for Tracing the Independent and Group Learning Processes](https://mlsanity.com/paper/2406.12465/) (2024)
- [DisenGCD: A Meta Multigraph-assisted Disentangled Graph Learning Framework for Cognitive Diagnosis](https://mlsanity.com/paper/2410.17564/) (2024)
- [Collaborative Cognitive Diagnosis with Disentangled Representation Learning for Learner Modeling](https://mlsanity.com/paper/2411.02066/) (2024)
- [FedCD: A Fairness-aware Federated Cognitive Diagnosis Framework](https://mlsanity.com/paper/2508.01296/) (2025)

## Abstract (arXiv)

With the rapid advancement of intelligent education, Computerized Adaptive Testing (CAT) has attracted increasing attention by integrating educational psychology with deep learning technologies. Unlike traditional paper-and-pencil testing, CAT aims to efficiently and accurately assess examinee abilities by adaptively selecting the most suitable items during the assessment process. However, its real-time and sequential nature presents limitations in practical scenarios, particularly in large-scale assessments where interaction costs are high, or in sensitive domains such as psychological evaluations where minimizing noise and interference is essential. These challenges constrain the applicability of conventional CAT methods in time-sensitive or resourceconstrained environments. To this end, we first introduce a novel task called one-shot adaptive testing (OAT), which aims to select a fixed set of optimal items for each test-taker in a one-time selection. Meanwhile, we propose PEOAT, a Personalization-guided Evolutionary question assembly framework for One-shot Adaptive Testing from the perspective of combinatorial optimization. Specifically, we began by designing a personalization-aware initialization strategy that integrates differences between examinee ability and exercise difficulty, using multi-strategy sampling to construct a diverse and informative initial population. Building on this, we proposed a cognitive-enhanced evolutionary framework incorporating schema-preserving crossover and cognitively guided mutation to enable efficient exploration through informative signals. To maintain diversity without compromising fitness, we further introduced a diversity-aware environmental selection mechanism. The effectiveness of PEOAT is validated through extensive experiments on two datasets, complemented by case studies that uncovered valuable insights.