# Enhancing Ability Estimation with Time-Sensitive IRT Models in Computerized Adaptive Testing
## 2 Literature Review
These improvements in time and overlap reduction contributed to increased test score validity by mitigating the likelihood of hasty or random responses due to time pressure and decreasing the chance of prior knowledge influencing item responses. In conclusion, [PERSON] et al. (2017) emphasized the central role of response time in enhancing time efficiency and test security within CAT systems. The study showed that GMIT, compared to other RT-based approaches (BMIT, MIB), provided a more balanced and effective solution for optimizing item selection algorithms, thereby improving the validity of test scores [35]. Time-sensitive IRT models fall into two categories: 1. Subtractive models (e.g., DTA-IRT), which penalize excessive response times by reducing ability estimates and discouraging inefficient time use [37]. 2. Multiplicative models (e.g., TWD-IRT, ART-IRT), which apply response time as a weighting factor, directly modulating ability estimates but introducing higher variability [38; 39]. Empirical comparisons show that subtractive models achieve superior stability in high-stakes testing, as evidenced by lower AIC/BIC values [40], while multiplicative models risk inflating ability estimates, reducing their utility in adaptive testing [41]. Optimization techniques parameter estimation in time-sensitive IRT models demands robust optimization. Traditional Maximum Likelihood Estimation (MLE) struggles with computational complexity for response-time parameters (\(\lambda\)) [42]. Advanced methods like full parameter optimization and K-fold Cross-Validation (CV) mitigate overfitting [43]. The L-BFGS-B algorithm has emerged as particularly effective, balancing numerical stability and efficiency in high-dimensional parameter spaces [44]. Recent studies confirm its superiority over alternative optimization methods [45]. Adaptive Testing and Empirical Insights Modern Computerized Adaptive Testing (CAT) frameworks leverage response-time metrics to dynamically adjust test difficulty, improving efficiency and fairness [46]. [PERSON] and [PERSON] [33] demonstrated that integrating response-time variables via the shadow-test approach reduces test length without sacrificing precision. Empirical studies in medical education highlight adaptive testing's motivational benefits, particularly when aligning item difficulty with individual proficiency [28]. However, system efficacy depends on item bank diversity; limited variability can introduce measurement bias [47]. Despite progress, key gaps persist; for example, large-scale empirical evaluations of subtractive models (e.g., DTA-IRT) remain scarce [48]. Regarding dynamic item selection, the interaction between response time and item difficulty in real-time adaptive testing requires deeper exploration [49]. Previous research has explored methods to incorporate response time into IRT-based assessments. One of the earliest approaches involved penalizing excessive response times by adjusting ability estimates [50]. Other studies examined time-efficiency trade-offs in test-taking, proposing methods to weight or normalize response time in ability estimation. However, these approaches were often limited in scope and lacked systematic comparisons across multiple response time-influenced models. The integration of response time in Item Response Theory (IRT) has significantly improved the accuracy and efficiency of adaptive testing methods. Traditional IRT models primarily rely on response accuracy to estimate student ability, but incorporating response time provides deeper insights into cognitive processing and test-taking behavior. A major challenge in adaptive testing is selecting the most informative items dynamically. [PERSON] and [PERSON] (2009) [PERSON] and [PERSON] (2009) introduced an advanced item selection framework that optimizes the trade-off between response time and test precision.