The Influence of Age, Gender, and Gaming Experience on Robot Control Interface: An Exploratory Study

Raul Benites Paradeda; Artemísia Kimberlly Silva; Mateus da Costa Furtado; Anderson Abner Souza

doi:10.5753/jbcs.2025.4991

Authors

Raul Benites Paradeda State University of Rio Grande do Norte https://orcid.org/0000-0002-4031-6275
Artemísia Kimberlly Silva State University of Rio Grande do Norte https://orcid.org/0009-0000-9724-4930
Mateus da Costa Furtado State University of Rio Grande do Norte https://orcid.org/0009-0004-6187-0744
Anderson Abner Souza State University of Rio Grande do Norte https://orcid.org/0000-0001-6353-8674

DOI:

https://doi.org/10.5753/jbcs.2025.4991

Keywords:

Control Interface, Human-Robot Interaction, Robotics, Generational Differences, User Experience

Abstract

This study investigated how three distinct control modes—dual-joystick, smartphone gyroscope, and PlayStation 5 (PS5) controller—affect user experience variables (challenge, competence, flow, tension) during interaction with a robotic car, considering demographic factors such as age, gender, and gaming experience. Using an experimental design with 30 participants, non-parametric analyses revealed significant differences in performance and perception across control modes. The PS5 controller demonstrated superior efficiency, with fewer path deviations (p = 0.032) and faster task completion (p = 0.012) compared to the smartphone gyroscope. Marginal tension differences (p = 0.054) favored the PS5 controller over the smartphone interface. Gender differences emerged in task completion time (Condition 1: p = 0.038) and perceived competence/positive affect (Condition 3: p < 0.05), with males reporting higher competence and satisfaction. Generational disparities were context-specific: Generation Z exhibited lower negative affect than Generation Y (p = 0.009) in the PS5 condition, likely due to greater console familiarity. Prior gaming experience enhanced adaptation, with advanced users showing higher competence (p = 0.033) and flow (p = 0.023) in smartphone control. These findings underscore the need for adaptive interface designs that account for gender-specific preferences, generational familiarity (e.g., gaming consoles for younger users), and prior gaming expertise. The study advocates for personalized Human-Robot Interaction solutions to improve inclusivity and efficiency across applications, from entertainment to industrial robotics.

Downloads

Download data is not yet available.

References

Ben-Ari, M. and Mondada, F. (2017). Elements of Robotics. Springer Publishing Company. DOI: 10.1007/978-3-319-62533-1.

Buchner, R., Wurhofer, D., Weiss, A., and Tscheligi, M. (2012). User experience of industrial robots over time. In Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction, pages 115-116. DOI: 10.1145/2157689.2157712.

Cruea, M. and Park, S.-Y. (2012). Gender disparity in video game usage: A third-person perception-based explanation. Media Psychology, 15(1):44-67. DOI: 10.1080/15213269.2011.648861.

Csikszentmihalyi, M., Larson, R., et al. (2014). Flow and the foundations of positive psychology, volume 10. Springer. DOI: 10.1007/978-94-017-9088-8.

de Araújo Kohler, L. P., Fronza, L., Sartori, A., Burger, K., and Araújo, J. (2021). A representatividade feminina nos jogos digitais. In Women in Information Technology (WIT), pages 265-269. SBC. DOI: 10.5753/wit.2021.15863.

Dimock, M. (2019). Defining generations: Where millennials end and generation z begins. Pew Research Center, 17(1):1-7. Avialable at:[link].

Dupont, P. E., Nelson, B. J., Goldfarb, M., Hannaford, B., Menciassi, A., O’Malley, M. K., Simaan, N., Valdastri, P., and Yang, G.-Z. (2021). A decade retrospective of medical robotics research from 2010 to 2020. Science robotics, 6(60):eabi8017. DOI: 10.1126/scirobotics.abi8017.

Farley, K. A., Williford, K. H., Stack, K. M., Bhartia, R., Chen, A., de la Torre, M., Hand, K., Goreva, Y., Herd, C. D., Hueso, R., et al. (2020). Mars 2020 mission overview. Space Science Reviews, 216:1-41. DOI: 10.1007/s11214-020-00762-y.

Fortim, I., de França Monteiro, L., Sancassani, V., and Bengel, M. J. (2016). A tipologia das jogadoras: Um estudo do público feminino gamer brasileiro. In Proceedings of SBGames, pages 1312-1319. SBC. Available online [link].

Hancock, P. A., Billings, D. R., Schaefer, K. E., Chen, J. Y., De Visser, E. J., and Parasuraman, R. (2011). A meta-analysis of factors affecting trust in human-robot interaction. Human factors, 53(5):517-527. DOI: 10.1177/0018720811417254.

IJsselsteijn, W., de Kort, Y., and Poels, K. (2013).The game experience questionnaire. Eindhoven : Technische Universiteit Eindhoven, page 9, Available online [link].

Kaye, L. K., Pennington, C. R., and McCann, J. J. (2018). Do casual gaming environments evoke stereotype threat? examining the effects of explicit priming and avatar gender. Computers in Human Behavior, 78:142-150. DOI: 10.1016/j.chb.2017.09.031.

Lukosch, H., Kurapati, S., Groen, D., and Verbraeck, A. (2017). Gender and cultural differences in game-based learning experiences. Electronic Journal of e-Learning, 15(4):pp310-319, Available online [link].

Müller, M. and Koltun, V. (2021). Openbot: Turning smartphones into robots. In 2021 IEEE International Conference on Robotics and Automation (ICRA), pages 9305-9311, Xi'an, China. DOI: 10.1109/ICRA48506.2021.9561788.

Niemeyer, G., Preusche, C., Stramigioli, S., and Lee, D. (2016). Telerobotics. In Springer Handbook of Robotics, pages 1085-1108. Springer. DOI: 10.1007/978-3-319-32552-1_43.

Norman, D. (2013). The design of everyday things: Revised and expanded edition. Basic books. Book.

of Sciences, N. A., of Behavioral, D., Sciences, S., on Science Education, B., on Behavioral, B., Sciences, S., on How People Learn II, C., Science, T., and of Learning, P. (2018). How people learn II: Learners, contexts, and cultures. National Academies Press. DOI: 10.17226/24783.

Porte, M. d. S. and Trindade, J. D. R. (2021). Barreiras tecnológicas: um fator limitador na acessibilidade das pessoas com deficiência. Texto Livre, 14(3):e32563. DOI: 10.35699/1983-3652.2021.32563.

Riek, L. D. (2017). Healthcare robotics. Communications of the ACM, 60(11):68-78. DOI: 10.1145/3127874.

Saren, S., Mukhopadhyay, A., Ghose, D., and Biswas, P. (2024). Comparing alternative modalities in the context of multimodal human-robot interaction. Journal on Multimodal User Interfaces, 18(1):69-85. DOI: 10.1007/s12193-023-00421-w.

Shen, C., Ratan, R., Cai, Y. D., and Leavitt, A. (2016). Do men advance faster than women? debunking the gender performance gap in two massively multiplayer online games. Journal of Computer-Mediated Communication, 21(4):312-329. DOI: 10.1111/jcc4.12159.

Sheridan, T. B. (2016). Human-robot interaction: status and challenges. Human factors, 58(4):525-532. DOI: 10.1177/0018720816644364.

Sheridan, T. B. (2020). A review of recent research in social robotics. Current opinion in psychology, 36:7-12. DOI: 10.1016/j.copsyc.2020.01.003.

Shneiderman, B., Plaisant, C., Cohen, M., and Jacobs, S. (2017). Designing the User Interface: Strategies for Effective Human-Computer Interaction. Pearson Education. DOI: https://doi.org/10.1016/0003-6870(93)90471-k.

Sukaridhoto, S., Haz, A. L., Fajrianti, E. D., and Budiarti, R. P. N. (2023). Comparative study of 3d assets optimization of virtual reality application on vr standalone device. International Journal on Advanced Science, Engineering & Information Technology, 13(3). DOI: 10.18517/ijaseit.13.3.18375.

Tsagaris, A. and Chatzikyrkou, M. (2023). Usability evaluation of educational robotics. In Proceedings of the 7th International Conference on Education and Multimedia Technology, pages 62-68. DOI: 10.1145/3625704.3625719.

Türkay, S., Letheren, K., Crawford, R., Roberts, J., and Jaiprakash, A. T. (2021). The effects of gender, age, and videogame experience on performance and experiences with a surgical robotic arm: an exploratory study with general public. Journal of Robotic Surgery, pages 1-9. DOI: 10.1007/s11701-021-01287-4.

Turner, A. (2015). Generation z: Technology and social interest. The journal of individual Psychology, 71(2):103-113. DOI: 10.1353/jip.2015.0021.

Udekwe, D. and Seyyedhasani, H. (2025). Human robot interaction for agricultural tele-operation, using virtual reality: A feasibility study. Computers and Electronics in Agriculture, 228:109702. DOI: 10.1016/j.compag.2024.109702.

Vogels, E. A. (2019). Millennials stand out for their technology use, but older generations also embrace digital life. Pew Research Center. Available online [link].

Wang, X., Guo, S., Xu, Z., Zhang, Z., Sun, Z., and Xu, Y. (2024). A robotic teleoperation system enhanced by augmented reality for natural human-robot interaction. Cyborg and Bionic Systems, 5:0098. DOI: 10.34133/cbsystems.0098.

Wickens, C. D., Helton, W. S., Hollands, J. G., and Banbury, S. (2021). Engineering psychology and human performance. Routledge. DOI: 10.4324/9781003177616.

Yang, J. C. and Chen, S. Y. (2020). An investigation of game behavior in the context of digital game-based learning: An individual difference perspective. Computers in Human Behavior, 112:106432. DOI: 10.1016/j.chb.2020.106432.