# Enhancing Ability Estimation with Time-Sensitive IRT Models in Computerized Adaptive Testing
## 7 Conclusions  
The findings of this study have important implications for educational assessment, test development, and adaptive learning systems. One of the key takeaways is that subtractive time adjustments, particularly DTA-IRT and TP-IRT, can enhance ability estimation without adding excessive complexity to the model. This result suggests that educational testing systems, particularly computerized adaptive testing (CAT) platforms, can integrate subtractive time adjustments to improve student assessment accuracy. By penalizing excessive response times proportionally rather than disproportionately affecting students with moderate response speeds, subtractive models provide a fairer and more stable alternative to traditional IRT models. Another important implication relates to fairness in timed assessments. Many standardized tests impose strict time limits, potentially disadvantageing students who process information more carefully or those with slower reading speeds. This study shows that multiplicative time models, such as TWD-IRT, NRT-IRT, and ART-IRT, tend to disproportionately penalize slower respondents, introducing potential bias. In contrast, subtractive models (DTA-IRT, TP-IRT) offer a more balanced way of incorporating time effects while maintaining predictive accuracy. Test designers and policymakers should consider integrating subtractive time adjustments to ensure fairer assessments, particularly for tests that aim to evaluate knowledge and reasoning ability rather than speed alone. Furthermore, the use of response-time-enhanced IRT models could improve automated test analysis and cheating detection. For example, very short response times combined with high accuracy may indicate rapid guessing or pre-exposure to test questions, whereas excessively long response times with low accuracy may indicate disengagement or test fatigue. By implementing best-performing models (such as DTA-IRT) in automated scoring systems, test administrators can detect irregular response patterns and flag potential test-taking anomalies for further investigation. Finally, the results of this study have implications for personalized learning environments. Adaptive learning platforms often adjust question difficulty based on previous responses, but they typically do not consider response time as an additional factor. The findings suggest that incorporating response time into adaptive question selection algorithms could improve the efficiency and accuracy of personalized learning systems. By considering both correctness and response time, these systems could dynamically adjust content delivery to match a student's cognitive processing speed, ultimately leading to more effective and engaging learning experiences. These findings contribute to the broader understanding of time-based IRT models by demonstrating that subtractive time adjustments (DTA-IRT, TP-IRT) provide a more robust and unbiased alternative to traditional approaches (1 PL-IRT). While these findings offer promising directions, further research with larger and more diverse student populations is needed to validate their applicability across different testing environments. Additionally, future studies should explore nonlinear transformations of response time, such as logarithmic or exponential models, to better capture the relationship among time, ability estimation, and test performance. Overall, this study demonstrates that integrating response time into IRT models enhances assessment accuracy while promoting fairness and adaptability in educational measurement. Future research should continue refining these approaches to optimize learning outcomes and improve test validity in diverse educational settings. Conceptualization, A.H.I. ; Methodology, A.H.I. ; Software, A.H.I. ; Validation, A.H.I. and S.O. ; Resources, A.H.I. ; Writing--original draft, A.H.I. ; Writing--review & editing, S.O. ; Visualization, A.H.I. and S.O. ; Supervision, S.O. ; Project administration, S.O. All authors have read and agreed to the published version of the manuscript.