# Enhancing Ability Estimation with Time-Sensitive IRT Models in Computerized Adaptive Testing
## 2 Literature Review  
The integration of response time into Item Response Theory (IRT) frameworks has addressed critical limitations of traditional accuracy-only models, enhancing educational assessment precision and fairness [23; 24]. Classical IRT models, such as the 1 PL (One-Parameter Logistic) and Rasch models, focus on estimating student ability (\(\theta\)) and item difficulty (b) through response accuracy but overlook cognitive processes and engagement levels reflected in response times [25; 26]. This gap has stimulated the development of time-sensitive IRT models that include response time as an auxiliary parameter to improve capability prediction and adaptive testing strategies [27; 28]. The role of response time in IRT: response time provides insights into cognitive load, problem-solving behavior, and test-taker engagement [29]. [PERSON] [27] pioneered a hierarchical speed-accuracy trade-off model, showing how response times distinguish deliberate problem-solving from rapid guessing. Subsequent work, such as [PERSON] and [PERSON] [30], advanced Bayesian hierarchical models to improve parameter estimation in IRT-based response-time analyses. However, response times remain susceptible to external factors like test anxiety, cognitive fatigue, and item complexity, leading to parameter instability [31; 32]. To address this, researchers have proposed data-smoothing methods and Bayesian priors for response-time parameters [33]. In this context, [PERSON] et al. (2014) investigated how Multidimensional Computerized Adaptive Testing (MCAT) can simultaneously achieve accurate ability estimation and minimize test duration, finding that the use of MCAT significantly reduced test time compared to full-length selection tests without causing substantial losses in accuracy. The study also noted that incorporating response time constraints into item selection helped to further lower test time, although it introduced potential content imbalance, which could be mitigated by applying content constraints [34]. The use of Response Time (RT) in Computerized Adaptive Testing (CAT) has gained significant attention as researchers strive to optimize both the efficiency and security of adaptive test systems. [PERSON], [PERSON], and [PERSON] (2017) explored how response time could be incorporated into CAT frameworks to not only improve the efficiency of item selection but also to minimize test duration, an aspect traditionally overlooked in standard item-based efficiency metrics [35]. Building on previous research by [PERSON] et al. (2012), the study highlights the MI with Time (MIT) criterion, which maximizes the Fisher information-to-response time ratio for item selection. MIT effectively reduces the average test completion time in fixed-length tests. However, a critical drawback identified by the authors is that MIT excessively favors items with high discrimination (a) and low expected RT, resulting in imbalanced item exposure, a situation that poses risks to test security and fairness [36]. To address these issues and use response times in a more balanced way, the study investigated three alternative methods: (1) b-partitioned MIT (BMIT), (2) b-matching MI (MIB), and (3) Generalized MIT (GMIT). Among these, GMIT was found to be the most promising approach. GMIT optimizes the item selection criterion by centering the expected RT around a mean value (v) and applying a weighting exponent (w). This dual mechanism allows for flexible adjustment of the impact of expected RT on item selection, with the mean value (v) focusing on aligning the expected RT with a target value, improving item pool usage, and stabilizing test duration. The weighting (w) controls the influence of the centered RT term. Simulations and real-world data analyses demonstrated that GMIT significantly reduced both the average and variability of test times, as well as the average and variability of test overlap (a measure of item exposure), without requiring additional item exposure control mechanisms.