Optimizing crowdsourced human computation with adaptive intelligent user interfaces for scalability and explainability
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Abstract
Intelligent User Interfaces (IUIs) represent a transformative paradigm for advancing crowdsourced and human computation by optimizing task distribution, strengthening human–AI collaboration, and ensuring data integrity. This study presents a case study–driven analysis of an adaptive IUI framework designed to enhance scalability, engagement, and accuracy in large-scale, crowd-based problem-solving. By examining three representative platforms—Amazon Mechanical Turk (MTurk), Zooniverse (a citizen science platform), and AI-assisted medical image analysis in public health—the research investigates the influence of dynamic task allocation, Explainable AI (XAI), and gamification on user participation and task performance. The findings demonstrate that adaptive IUIs improve task accuracy relative to user expertise, reduce completion time as experience increases, and strengthen volunteer retention through gamified elements. Moreover, integrating XAI into AI-assisted medical diagnostics substantially elevates both trust and diagnostic precision. Collectively, these outcomes underscore the scalability, adaptability, and efficacy of IUIs in human computation, offering a comprehensive framework for future advancements in task optimization and explainability.
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References
[1] L. von Ahn and L. Dabbish, “Designing games with a purpose,” Communications of the ACM, vol. 51, no. 8, pp. 58–67, Aug. 2008. [Online]. Available: https://doi.org/10.1145/1378704.1378719
[2] E. Horvitz, “Principles of mixed-initiative user interfaces,” in Proceedings of the SIGCHI conference on Human factors in computing systems the CHI is the limit - CHI ’99, ser. CHI ’99. ACM Press, 1999, pp. 159–166. [Online]. Available: https://doi.org/10.1145/302979.303030
[3] A. J. Quinn and B. B. Bederson, “Human computation: a survey and taxonomy of a growing field,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ser. CHI ’11. ACM, May 2011, pp. 1403–1412. [Online]. Available: https://doi.org/10.1145/1978942.1979148
[4] C. Schmidbauer, S. Zafari, B. Hader, and S. Schlund, “An empirical study on workers’ preference in human–robot task assignment in industrial assembly systems,” IEEE Transactions on Human-Machine Systems, vol. 53, no. 2, pp. 293–302, 2023. [Online]. Available: https://doi.org/10.1109/thms.2022.3230667
[5] J. Wen, J. Yang, T. Wang, Y. Li, and Z. Lv, “Energy-efficient task allocation for reliable parallel computation of cluster-based wireless sensor network in edge computing,” Digital Communications and Networks, vol. 9, no. 2, pp. 473–482, Apr. 2023. [Online]. Available: https://doi.org/10.1016/j.dcan.2022.06.014
[6] M. Faccio, I. Granata, and R. Minto, “Task allocation model for human-robot collaboration with variable cobot speed,” Journal of Intelligent Manufacturing, vol. 35, no. 2, pp. 793–806, Jan. 2023. [Online]. Available: https://doi.org/10.1007/s10845-023-02073-9
[7] Z. Yuan, R. Wang, T. Kim, D. Zhao, I. Obi, and B.-C. Min, “Adaptive task allocation in multi-human multi-robot teams under team heterogeneity and dynamic information uncertainty,” ICRA 2025, 2024. [Online]. Available: https://doi.org/10.48550/arXiv.2409.13824
[8] A. Tamali, N. Amardjia, and M. Tamali, “Distributed and autonomous multi-robot for task allocation and collaboration using a greedy algorithm and robot operating system platform,” IAES International Journal of Robotics and Automation (IJRA), vol. 13, no. 2, p. 205, Jun. 2024. [Online]. Available: https://doi.org/10.11591/ijra.v13i2.pp205-219
[9] M. Jain, Crowd-Sourced Evaluation of Explainable AI Techniques with Games. Carnegie Mellon University, 2021. [Online]. Available: https://upsalesiana.ec/ing35ar4r9
[10] Z. Kou, Y. Zhang, D. Zhang, and D. Wang, “Crowdgraph: A crowdsourcing multi-modal knowledge graph approach to explainable fauxtography detection,” Proceedings of the ACM on Human-Computer Interaction, vol. 6, no. CSCW2, pp. 1–28, Nov. 2022. [Online]. Available: https://doi.org/10.1145/3555178
[11] Z. Kou, L. Shang, Y. Zhang, and D. Wang, “HC-COVID: A hierarchical crowdsource knowledge graph approach to explainable COVID-19 misinformation detection,” Proceedings of the ACM on Human-Computer Interaction, vol. 6, no. GROUP, pp. 1–25, Jan. 2022. [Online]. Available: http://doi.org/10.1145/3492855
[12] M. Sawant, A. Younus, S. Caton, and M. A. Qureshi, “Using explainable AI (XAI) for identification of subjectivity in hate speech annotations for low-resource languages,” in 4th International Workshop on OPEN CHALLENGES IN ONLINE SOCIAL NETWORKS, ser. HT ’24. ACM, Sep. 2024, pp. 10–17. [Online]. Available: http://doi.org/10.1145/3677117.3685006
[13] V. Lai, Y. Zhang, C. Chen, Q. V. Liao, and C. Tan, “Selective explanations: Leveraging human input to align explainable AI,” Proceedings of the ACM on Human-Computer Interaction, vol. 7, no. CSCW2, pp. 1–35, Sep. 2023. [Online]. Available: http://doi.org/10.1145/3610206
[14] C. Zhang, P. van Gorp, M. Derksen, R. Nuijten, W. A. IJsselsteijn, A. Zanutto, F. Melillo, and R. Pratola, “Promoting occupational health through gamification and e-coaching: A 5-month user engagement study,” International Journal of Environmental Research and Public Health, vol. 18, no. 6, p. 2823, Mar. 2021. [Online]. Available: http://doi.org/10.3390/ijerph18062823
[15] C. J. Hellín, F. Calles-Esteban, A. Valledor, J. Gómez, S. Otón-Tortosa, and A. Tayebi, “Enhancing student motivation and engagement through a gamified learning environment,” Sustainability, vol. 15, no. 19, p. 14119, Sep. 2023. [Online]. Available: http://doi.org/10.3390/su151914119
[16] H.-P. Lu and H.-C. Ho, “Exploring the impact of gamification on users’ engagement for sustainable development: A case study in brand applications,” Sustainability, vol. 12, no. 10, p. 4169, May 2020. [Online]. Available: http://doi.org/10.3390/su12104169
[17] P. Bitrián, I. Buil, and S. Catalán, “Enhancing user engagement: The role of gamification in mobile apps,” Journal of Business Research, vol. 132, pp. 170–185, Aug. 2021. [Online]. Available: http://doi.org/10.1016/j.jbusres.2021.04.028
[18] A. S. Alfaqiri, S. F. M. Noor, and N. Sahari, “Framework for gamification of online training platforms for employee engagement enhancement,” International Journal of Interactive Mobile Technologies (iJIM), vol. 16, no. 06, pp. 159–175, Mar. 2022. [Online]. Available: http://doi.org/10.3991/ijim.v16i06.28485
[19] N. Pius Owoh and M. Mahinderjit Singh, “Sensecrypt: A security framework for mobile crowd sensing applications,” Sensors, vol. 20, no. 11, p. 3280, Jun. 2020. [Online]. Available: http://doi.org/10.3390/s20113280
[20] Z. Li, J. Liu, J. Hao, H. Wang, and M. Xian, “Crowdsfl: A secure crowd computing framework based on blockchain and federated learning,” Electronics, vol. 9, no. 5, p. 773, May 2020. [Online]. Available: http://doi.org/10.3390/electronics9050773
[21] P. Siangliulue, J. Chan, S. P. Dow, and K. Z. Gajos, “Ideahound: Improving large-scale collaborative ideation with crowd-powered realtime semantic modeling,” in Proceedings of the 29th Annual Symposium on User Interface Software and Technology, ser. UIST ’16. ACM, Oct. 2016, pp. 609–624. [Online]. Available: http://doi.org/10.1145/2984511.2984578
[22] T. Abbas, Affective Real-Time Crowd-Powered Conversational Systems. Eindhoven University of Technology, Sep. 2022. [Online]. Available: https://upsalesiana.ec/ing35ar4r22
[23] M. Ponti and A. Seredko, “Human-machinelearning integration and task allocation in citizen science,” Humanities and Social Sciences Communications, vol. 9, no. 1, Feb. 2022. [Online]. Available: http://doi.org/10.1057/s41599-022-01049-z
[24] S. Stein and V. Yazdanpanah, “Citizen-centric multiagent systems,” in Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, ser. AAMAS ’23. Richland, SC: International Foundation for Autonomous Agents and Multiagent Systems, 2023, p. 1802–1807. [Online]. Available: https://upsalesiana.ec/ing35ar4r24
[25] P. Gupta, T. N. Nguyen, C. González, and A. W. Woolley, “Fostering collective intelligence in human–ai collaboration: Laying the groundwork for cohumain,” Topics in Cognitive Science, vol. 17, no. 2, pp. 189–216, Jun. 2023. [Online]. Available: http://doi.org/10.1111/tops.12679
[26] A. Carrera-Rivera, F. Larrinaga, G. Lasa, G. Martínez-Arellano, and G. Unamuno, “Adaptui: A framework for the development of adaptive user interfaces in smart productservice systems,” User Modeling and User-Adapted Interaction, vol. 34, no. 5, pp. 1929–1980, Aug. 2024. [Online]. Available: http://doi.org/10.1007/s11257-024-09414-0
[27] S. E. Shaw, S. Paparini, J. Murdoch, J. Green, T. Greenhalgh, B. Hanckel, H. M. James, M. Petticrew, G. W. Wood, and C. Papoutsi, “TRIPLE C reporting principles for case study evaluations of the role of context in complex interventions,” BMC Medical Research Methodology, vol. 23, no. 1, May 2023. [Online]. Available: http://doi.org/10.1186/s12874-023-01888-7
[28] L. Uden and N. Willis, “Designing user interfaces using activity theory,” in Proceedings of the 34th Annual Hawaii International Conference on System Sciences, ser. HICSS-01. IEEE Comput. Soc, 2005, p. 11. [Online]. Available: http://doi.org/10.1109/hicss.2001.926547
[29] M. Calzavara, M. Faccio, I. Granata, and A. Trevisani, “Achieving productivity and operator well-being: A dynamic task allocation strategy for collaborative assembly systems in industry 5.0,” The International Journal of Advanced Manufacturing Technology, Aug. 2024. [Online]. Available: http://doi.org/10.1007/s00170-024-14302-3
[30] M. H. Faisal, A. W. AlAmeeri, and A. A. Alsumait, “An adaptive e-learning framework: crowdsourcing approach,” in Proceedings of the 17th International Conference on Information Integration and Web-based Applications & Services, ser. iiWAS ’15. ACM, Dec. 2015, pp. 1–5. [Online]. Available: http://doi.org/10.1145/2837185.2837249
[31] J. C. Cheung and S. S. Ho, “The effectiveness of explainable AI on human factors in trust models,” Scientific Reports, vol. 15, no. 1, Jul. 2025. [Online]. Available: http://doi.org/10.1038/s41598-025-04189-9
[32] M. T. Ribeiro, S. Singh, and C. Guestrin, “Why should I trust you?: explaining the predictions of any classifier,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ser. KDD ’16. ACM, Aug. 2016, pp. 1135–1144. [Online]. Available: http://doi.org/10.1145/2939672.2939778
[33] R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-CAM: visual explanations from deep networks via gradient-based localization,” in 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, Oct. 2017, pp. 618–626. [Online]. Available: http://doi.org/10.1109/iccv.2017.74
[34] K. Borys, Y. A. Schmitt, M. Nauta, C. Seifert, N. Krämer, C. M. Friedrich, and F. Nensa, “Explainable AI in medical imaging: An overview for clinical practitioners – saliency-based XAI approaches,” European Journal of Radiology, vol. 162, p. 110787, May 2023. [Online]. Available: http://doi.org/10.1016/j.ejrad.2023.110787
[35] Q.Wang, Y.Wan, F. Feng, and X.Wang, “Threshold optimization of task allocation models in human–machine collaborative scoring of subjective assignments,” Computers & Industrial Engineering, vol. 188, p. 109923, Feb. 2024. [Online]. Available: http://doi.org/10.1016/j.cie.2024.109923
[36] L. Sun, X. Yu, J. Guo, Y. Yan, and X. Yu, “Deep reinforcement learning for task assignment in spatial crowdsourcing and sensing,” IEEE Sensors Journal, vol. 21, no. 22, pp. 25 323–25 330, Nov. 2021. [Online]. Available: http://doi.org/10.1109/jsen.2021.3057376
[37] S. N. Ahmadabadi, M. Haghifam, V. Shah-Mansouri, and S. Ershadmanesh, “Design and evaluation of crowdsourcing platforms based on users’ confidence judgments,” Scientific Reports, vol. 14, no. 1, Aug. 2024. [Online]. Available: http://doi.org/10.1038/s41598-024-65892-7
[38] J. Cox, E. Y. Oh, B. Simmons, C. Lintott, K. Masters, A. Greenhill, G. Graham, and K. Holmes, “Defining and measuring success in online citizen science: A case study of zooniverse projects,” Computing in Science ∓ Engineering, vol. 17, no. 4, pp. 28–41, Jul. 2015. [Online]. Available: http://doi.org/10.1109/mcse.2015.65
[39] S. A. Triantafyllou, T. Sapounidis, and Y. Farhaoui, “Gamification and computational thinking in education: A systematic literature review,” Salud, Ciencia y Tecnología - Serie de Conferencias, vol. 3, p. 659, Mar. 2024. [Online]. Available: http://doi.org/10.56294/sctconf2024659
[40] H. Cigdem, M. Ozturk, Y. Karabacak, N. Atik, S. Gürkan, and M. H. Aldemir, “Unlocking student engagement and achievement: The impact of leaderboard gamification in online formative assessment for engineering education,” Education and Information Technologies, vol. 29, no. 18, pp. 24 835–24 860, Jun. 2024. [Online]. Available: http://doi.org/10.1007/s10639-024-12845-2
[41] A. Tomar and S. Tripathi, “Bcsom: Blockchain-based certificateless aggregate signcryption scheme for internet of medical things,” Computer Communications, vol. 212, pp. 48–62, Dec. 2023. [Online]. Available: http://doi.org/10.1016/j.comcom.2023.09.027
[42] P. Runeson and M. Höst, “Guidelines for conducting and reporting case study research in software engineering,” Empirical Software Engineering, vol. 14, no. 2, pp. 131–164, Dec. 2008. [Online]. Available: http://doi.org/10.1007/s10664-008-9102-8