Elaris Computing Nexus
Designing Context Aware Interfaces for Better Human Agent Collaboration in Autonomous Systems
Elaris Computing Nexus
Received On : 16 July 2025
Revised On : 18 October 2025
Accepted On : 16 November 2025
Published On : 25 November 2025
Volume 01, 2025
Pages : 230-241
Abstract
The practice of human-agent cooperation within autonomous systems is a particularly important area of study, particularly as autonomous systems increase in their involvement in the daily setting. The main problem is in creating interfaces that are used by different users in a dynamic environment, where the level of task complexity and their user state play a role in interaction. In this paper, the researcher concerns the issue of developing context-aware user interfaces to improve the human-autonomous agent collaboration. Current interfaces do not take into consideration the dynamic conditions of the user, like cognitive load, emotional indicators, and environmental influences, resulting in ineffective and disastrous experiences. An innovative method is suggested, which is based on multimodal interaction methods and context-aware algorithms. The process makes use of the real-time sensor information to evaluate the conditions of the environment and user-specific conditions and modify the interface in a manner that maximizes communication. Using the combination of voice, gesture, and haptic response, the system tailors the interface to the needs of each specific user to enhance task performance and decision-making performance. In order to test the proposed system, the state-of-the-art methods are compared based on the main parameters, i.e., the time spent to complete a task, accuracy, and user satisfaction. Findings indicate a high level of collaboration efficiency and user experience, and the level of engagement and satisfaction is high. The research work is relevant to the body of knowledge because it provides an elaborate framework on how adaptive interfaces can be designed to meet the changing needs of users and autonomous systems.
Keywords
Human-Agent Collaboration, Context-Aware Interfaces, Multimodal Interaction, Autonomous Systems, User Experience, Adaptive Systems.
- Hauptman, R. Mallick, C. Flathmann, and N. J. McNeese, “Human factors considerations for the context-aware design of adaptive autonomous teammates,” Ergonomics, vol. 68, no. 4, pp. 571–587, Jul. 2024, doi: 10.1080/00140139.2024.2380341.
- J. Huang, W. Wu, Z. Zhang, G. Tian, S. Zheng, and Y. Chen, “Human Decision-Making Modeling and Cooperative Controller Design for Human–Agent Interaction Systems,” IEEE Transactions on Human-Machine Systems, vol. 52, no. 6, pp. 1122–1134, Dec. 2022, doi: 10.1109/thms.2022.3185333.
- N. Jiang, X. Liu, H. Liu, E. T. K. Lim, C.-W. Tan, and J. Gu, “Beyond AI-powered context-aware services: the role of human–AI collaboration,” Industrial Management & Data Systems, vol. 123, no. 11, pp. 2771–2802, Dec. 2022, doi: 10.1108/imds-03-2022-0152.
- W. Li, H. Liu, K. Huang, and A. Hussain, “Reinforcement Learning for Human-AI Collaboration: Challenges, Mechanisms, and Methods,” Cognitive Computation, vol. 17, no. 5, Sep. 2025, doi: 10.1007/s12559-025-10500-7.
- M. S. Islam et al., “Human-AI collaboration in real-world complex environment with reinforcement learning,” Neural Computing and Applications, vol. 37, no. 23, pp. 18957–18987, May 2025, doi: 10.1007/s00521-025-11288-1.
- G. Lee et al., “Sensible Agent: A Framework for Unobtrusive Interaction with Proactive AR Agents,” Proceedings of the 38th Annual ACM Symposium on User Interface Software and Technology, pp. 1–22, Sep. 2025, doi: 10.1145/3746059.3747748.
- Tolzin and A. Janson, “Uncovering the mechanisms of common ground in human–agent interaction: review and future directions for conversational agent research,” Internet Research, Jan. 2025, doi: 10.1108/intr-06-2023-0514.
- S. Mondal, I. Bhattacharya, and S. Gupta, “Human–Computer Interactions Through Multi-agent Systems: Design and Implementations,” Multi Agent Systems, pp. 5–32, 2022, doi: 10.1007/978-981-19-0493-6_2.
- L. Karn et al., “ICACIA: An Intelligent Context-Aware framework for COBOT in defense industry using ontological and deep learning models,” Robotics and Autonomous Systems, vol. 157, p. 104234, Nov. 2022, doi: 10.1016/j.robot.2022.104234.
- U. M. Borghoff, P. Bottoni, and R. Pareschi, “Human-artificial interaction in the age of agentic AI: a system-theoretical approach,” Frontiers in Human Dynamics, vol. 7, May 2025, doi: 10.3389/fhumd.2025.1579166.
- W. Yan, W. Wei, and T. Baitao, “Research on intelligent auxiliary assembly technology based on deep learning,” Cobot, vol. 3, p. 2, Jun. 2024, doi: 10.12688/cobot.17668.2.
- Omolaja, A. Otebolaku, and A. Alfoudi, “Context-Aware Complex Human Activity Recognition Using Hybrid Deep Learning Models,” Applied Sciences, vol. 12, no. 18, p. 9305, Sep. 2022, doi: 10.3390/app12189305.
- J. Schwalb, V. Menon, N. Tenhundfeld, K. Weger, B. Mesmer, and S. Gholston, “A Study of Drone-based AI for Enhanced Human-AI Trust and Informed Decision Making in Human-AI Interactive Virtual Environments,” 2022 IEEE 3rd International Conference on Human-Machine Systems (ICHMS), pp. 1–6, Nov. 2022, doi: 10.1109/ichms56717.2022.9980625.
- Y. Ding, B. Xin, J. Chen, Q. Yang, and H. Fang, “A Unifying Framework for Human–Agent Collaborative Systems—Part II: Design Procedure and Application,” IEEE Transactions on Cybernetics, vol. 52, no. 11, pp. 11990–12002, Nov. 2022, doi: 10.1109/tcyb.2021.3086073.
- M. Aggravi, G. Sirignano, P. R. Giordano, and C. Pacchierotti, “Decentralized Control of a Heterogeneous Human–Robot Team for Exploration and Patrolling,” IEEE Transactions on Automation Science and Engineering, vol. 19, no. 4, pp. 3109–3125, Oct. 2022, doi: 10.1109/tase.2021.3106386.
- J. Y. C. Chen, “Transparent Human–Agent Communications,” International Journal of Human–Computer Interaction, vol. 38, no. 18–20, pp. 1737–1738, Sep. 2022, doi: 10.1080/10447318.2022.2120173.
CRediT Author Statement
The author reviewed the results and approved the final version of the manuscript.
Acknowledgements
Authors thanks to American University for this research support.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics Declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of Data and Materials
Data sharing is not applicable to this article as no new data were created or analysed in this study.
Author Information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding Author
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit: https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this Article
Logan Barnes, “Designing Context Aware Interfaces for Better Human Agent Collaboration in Autonomous Systems”, Elaris Computing Nexus, pp. 230-241, 2025, doi: 10.65148/ECN/2025021.
Copyright
© 2025 Logan Barnes. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.