# Enhancing Ability Estimation with Time-Sensitive IRT Models in Computerized Adaptive Testing
## 6 Discussion  
One important consideration pertains to the consistency between item selection and post-hoc scoring procedures. In this study, adaptive item selection was performed using a traditional IRT model (1 PL-Rasch) without incorporating response time during test administration. However, response-time-aware models (e.g., DTA-IRT, ART-IRT) were applied retrospectively during the ability estimation phase. This discrepancy introduces a potential source of model mismatch--where item exposure and sequencing were governed by a time-agnostic selection algorithm, while final scoring accounted for timing behaviors. Such a mismatch may affect estimation fairness, particularly for high-ability students who might spend more time solving complex items and yet be penalized under subtractive models that assign lower ability scores to longer response latencies. To mitigate this risk, the time-aware models used in this study employed regularization mechanisms to constrain the influence of \(\lambda\) (time sensitivity), resulting in stable and interpretable parameter estimates. Furthermore, the \"TestYourself\" platform was intentionally designed with a dual-scoring strategy that distinguishes between correctness-based and time-based performance feedback. This design empowers learners to evaluate not only their proficiency but also their response efficiency without conflating the two in the final score interpretation. When item response time is formally adopted in IRT modeling, several practical and theoretical outcomes can be expected. First, the inclusion of time data enhances the diagnostic power of assessments by distinguishing deliberate reasoning, impulsive guessing, and disengaged behavior. This additional dimension allows for deeper interpretation of ability estimates and may lead to more equitable feedback for test-takers. Second, it is anticipated that incorporating response time can improve the efficiency of adaptive testing by enabling dual-criteria item selection algorithms that consider both information value and expected completion time. Furthermore, response-time-aware models may support fairness in assessment design, particularly for students who differ in processing speed due to anxiety, cognitive load, or language proficiency. However, such benefits are contingent on careful model design that avoids over-penalizing thoughtful or slow respondents. Therefore, the adoption of response time must be accompanied by appropriate calibration, regularization, and empirical validation to ensure that it improves measurement without compromising validity. Empirical findings revealed that time-sensitive models consistently achieved better fit metrics (AIC, BIC) and improved generalizability across K-fold cross-validations, reinforcing the value of response time as a complementary source of information. Additionally, we acknowledge that the item difficulty parameters (b) in this study were calibrated based solely on response accuracy without incorporating response time during the item calibration phase. This was an intentional methodological decision to isolate the impact of response time on student ability estimation (\(\theta\)) rather than confounding it with item-level effects. By holding item difficulties fixed across all models, we were able to more clearly assess how response-time-aware scoring affects ability estimates. Despite the absence of time-informed item calibration, the empirical results demonstrated that time-sensitive models--especially subtractive approaches such as DTA-IRT--yielded improved model fit and generalizability. This suggests that student-level response time adjustment can meaningfully enhance scoring accuracy, even when item-level calibration does not explicitly account for time. Future studies may benefit from jointly modeling item difficulty and expected response time for more comprehensive calibration. Nevertheless, the inconsistency between selection and scoring logic underscores an opportunity for future development.