Research on Interactive Interface Adaptive Design Model Based on Dynamic Cognitive Load Evaluation

Mingxiang Yang

doi:10.71451/ISTAER2611

Authors

Mingxiang Yang College of Arts, Shandong Jianzhu University, Jinan, Shandong, China Author https://orcid.org/0009-0002-3965-4047

DOI:

https://doi.org/10.71451/ISTAER2611

Keywords:

Dynamic cognitive load assessment; Adaptive interface; Multimodal fusion; Reinforcement learning; Human-computer interaction

Abstract

With the increasing complexity of human-computer interaction systems, the cognitive load caused by interface information overload has become a key bottleneck affecting user experience and operational efficiency. Therefore, this paper proposes an interactive interface adaptive design model based on dynamic cognitive load evaluation and constructs a closed-loop optimization framework of “perception-evaluation-decision-execution”. Therefore, this paper proposes an interactive interface adaptive design model based on the dynamic evaluation of cognitive load, and constructs a closed-loop optimization framework of “perception evaluation decision execution”. First, a dynamic multimodal cognitive load assessment model is designed, which integrates behavioral, eye-movement, and physiological signals via a cross-modal attention mechanism, combined with time series modeling and uncertainty estimation, to achieve continuous and accurate perception of cognitive load. The root mean square error of prediction is 0.592. Second, a cognitive-driven interface adaptive decision-making framework is constructed, which uses the results of cognitive load assessment and task context as the basis for decision-making. On this basis, a cognitive-constrained reinforcement learning optimization algorithm is proposed. By introducing an upper-limit constraint on cognitive load and a strategy clipping mechanism, interaction efficiency is guaranteed and decision stability is improved. Experimental results show that the proposed method reduces task completion time by 22.7%, increases user satisfaction by 41.9%, decreases cognitive load by 25.0%, and keeps total response delay within 81 milliseconds. This study provides a systematic solution for the construction of intelligent adaptive interface with both evaluation accuracy and decision stability.

References

[1] Vemuri, V. (2024). The evolution of human-computer interaction: From command lines to conversational interfaces powered by large language models. Journal of Artificial Intelligence, Machine Learning and Data Science, 2(1), 1-10. DOI: https://doi.org/10.51219/JAIMLD/venkata-padma-kumar-vemuri/494

[2] Jin, H., Zhu, L., Li, M., & Duffy, V. G. (2024). Recognition and evaluation of mental workload in different stages of perceptual and cognitive information processing using a multimodal approach. Ergonomics, 67(3), 377-397. DOI: https://doi.org/10.1080/00140139.2023.2223785

[3] Nderitu, J. H. (2023). Mental State Adaptive Interfaces as a Remedy to the Issue of Long-term Continuous Human Machine Interaction. Journal of Robotics Spectrum, 1, 078-089. DOI: https://doi.org/10.53759/9852/JRS202301008

[4] Hinss, M. F., Brock, A. M., & Roy, R. N. (2022). Cognitive effects of prolonged continuous human-machine interaction: The case for mental state-based adaptive interfaces. Frontiers in Neuroergonomics, 3, 935092. DOI: https://doi.org/10.3389/fnrgo.2022.935092

[5] Stefanidi, Z., Margetis, G., Ntoa, S., & Papagiannakis, G. (2022). Real-time adaptation of context-aware intelligent user interfaces, for enhanced situational awareness. IEEE Access, 10, 23367-23393. DOI: https://doi.org/10.1109/ACCESS.2022.3152743

[6] Tao, X., Wang, W., Liu, Y., Chen, H., Lu, Q., & Huang, J. (2026). Evaluation of Cognitive Load of Industrial Control Interface by Integrating Dynamic Weights and Improved GRA-TOPSIS. International Journal of Human–Computer Interaction, 1-28. DOI: https://doi.org/10.1080/10447318.2026.2620648

[7] Abrahão, S., Insfran, E., Sluÿters, A., & Vanderdonckt, J. (2021). Model-based intelligent user interface adaptation: challenges and future directions. Software and Systems Modeling, 20(5), 1335-1349. DOI: https://doi.org/10.1007/s10270-021-00909-7

[8] Zhu, S., Yu, T., Xu, T., Chen, H., Dustdar, S., Gigan, S., ... & Pan, Y. (2023). Intelligent computing: the latest advances, challenges, and future. Intelligent Computing, 2, 0006. DOI: https://doi.org/10.34133/icomputing.0006

[9] Xin, D. (2025). Multi-source heterogeneous data fusion and intelligent prediction modeling for chemical engineering construction projects based on improved transformer architecture. Scientific Reports, 15(1), 38806. DOI: https://doi.org/10.1038/s41598-025-22752-2

[10] Jiang, M., Wu, Q., & Li, X. (2022). Multisource heterogeneous data fusion analysis of regional digital construction based on machine learning. Journal of Sensors, 2022(1), 8205929. DOI: https://doi.org/10.1155/2022/8205929

[11] Fox, S., & Rey, V. F. (2024). A cognitive load theory (CLT) analysis of machine learning explainability, transparency, interpretability, and shared interpretability. Machine Learning and Knowledge Extraction, 6(3), 1494-1509. DOI: https://doi.org/10.3390/make6030071

[12] Gkintoni, E., Antonopoulou, H., Sortwell, A., & Halkiopoulos, C. (2025). Challenging cognitive load theory: The role of educational neuroscience and artificial intelligence in redefining learning efficacy. Brain sciences, 15(2), 203. DOI: https://doi.org/10.3390/brainsci15020203

[13] Zak, Y., Parmet, Y., & Oron-Gilad, T. (2020, October). Subjective Workload assessment technique (SWAT) in real time: Affordable methodology to continuously assess human operators’ workload. In 2020 IEEE international conference on Systems, Man, and Cybernetics (SMC) (pp. 2687-2694). IEEE. DOI: https://doi.org/10.1109/SMC42975.2020.9283168

[14] Eggemeier, F. T., & Wilson, G. F. (2020). Performance-based and subjective assessment of workload in multi-task environments. Multiple task performance, 217-278. DOI: https://doi.org/10.1201/9781003069447-13

[15] Li, K. W., Lu, Y., & Li, N. (2022). Subjective and objective assessments of mental workload for UAV operations. Work, 72(1), 291-301. DOI: https://doi.org/10.3233/wor-205318

[16] Gasz, R., Bougriche, Z., Osuchowski, J., & Tomaszewski, M. (2026). A review of current capabilities and future directions in machine-based emotion recognition. Cognitive, Affective, & Behavioral Neuroscience, 1-27. DOI: https://doi.org/10.3758/s13415-025-01398-7

[17] Akhuseyinoglu, K., & Brusilovsky, P. (2022). Exploring behavioral patterns for data-driven modeling of learners' individual differences. Frontiers in Artificial Intelligence, 5, 807320. DOI: https://doi.org/10.3389/frai.2022.807320

[18] Go, R. Y. (2025). User behavior and interaction patterns. In Unveiling Social Dynamics and Community Interaction in the Metaverse (pp. 65-92). IGI Global Scientific Publishing. DOI: https://doi.org/10.4018/979-8-3693-8628-6.ch004

[19] Lu, G., Yu, J., Zhou, J., Cheng, T., Zhang, T., & Zhang, S. (2023). Interface layout optimization for electrical devices using heuristic algorithms and eye movement. IEEE Access, 11, 106083-106094. DOI: https://doi.org/10.1109/ACCESS.2023.3319473

[20] Zheng, W., Liu, C., Deng, P., Chen, X., & Wu, X. (2025). Enhancing concurrency vulnerability detection through AST-based static fuzz mutation. Journal of Systems and Software, 222, 112352. DOI: https://doi.org/10.1016/j.jss.2025.112352

[21] Planas, E., Daniel, G., Brambilla, M., & Cabot, J. (2021). Towards a model-driven approach for multiexperience AI-based user interfaces. Software and Systems Modeling, 20(4), 997-1009. DOI: https://doi.org/10.1007/s10270-021-00904-y

[22] Abrahão, S., Insfran, E., Sluÿters, A., & Vanderdonckt, J. (2021). Model-based intelligent user interface adaptation: challenges and future directions. Software and Systems Modeling, 20(5), 1335-1349. DOI: https://doi.org/10.1007/s10270-021-00909-7

[23] Wang, W., Grundy, J., Khalajzadeh, H., Madugalla, A., & Obie, H. O. (2026). Designing adaptive user interfaces for mHealth applications targeting chronic disease: a user-centered approach. ACM Transactions on Software Engineering and Methodology, 35(2), 1-57. DOI: https://doi.org/10.1145/3731750

[24] Khamaj, A., & Ali, A. M. (2024). Adapting user experience with reinforcement learning: Personalizing interfaces based on user behavior analysis in real-time. Alexandria Engineering Journal, 95, 164-173. DOI: https://doi.org/10.1016/j.aej.2024.03.045

[25] Zhang, C., Yang, Z., He, X., & Deng, L. (2020). Multimodal intelligence: Representation learning, information fusion, and applications. IEEE Journal of Selected Topics in Signal Processing, 14(3), 478-493. DOI: https://doi.org/10.1109/JSTSP.2020.2987728

[26] Luo, Q., He, S., Han, X., Wang, Y., & Li, H. (2024). LSTTN: A long-short term transformer-based spatiotemporal neural network for traffic flow forecasting. Knowledge-Based Systems, 293, 111637. DOI: https://doi.org/10.1016/j.knosys.2024.111637

[27] Zhao, L., & Ji, S. (2022). CNN, RNN, or ViT? An evaluation of different deep learning architectures for spatio-temporal representation of sentinel time series. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 44-56. DOI: https://doi.org/10.1109/JSTARS.2022.3219816

[28] Huang, L., Mao, F., Zhang, K., & Li, Z. (2022). Spatial-temporal convolutional transformer network for multivariate time series forecasting. Sensors, 22(3), 841. DOI: https://doi.org/10.3390/s22030841

[29] Zhu, L., & Lv, J. (2023). Review of studies on user research based on EEG and eye tracking. Applied Sciences, 13(11), 6502. DOI: https://doi.org/10.3390/app13116502

[30] Zhao, T., Chen, G., Pang, C., & Busababodhin, P. (2025). Application and Performance Optimization of SLHS-TCN-XGBoost Model in Power Demand Forecasting. CMES-Computer Modeling in Engineering and Sciences, 143(3), 2883-2917. DOI: https://doi.org/10.32604/cmes.2025.066442

[31] Zhao, T., Chen, G., Suraphee, S., Phoophiwfa, T., & Busababodhin, P. (2025). A hybrid TCN-XGBoost model for agricultural product market price forecasting. PLoS One, 20(5), e0322496. DOI: https://doi.org/10.1371/journal.pone.0322496