Applying cross-modal plasticity principles in auditory training applications

Huang, Qiqi; Stawarz, Katarzyna; Zhao, Linqi; Yang, Shuya; Xie, Wenyu; Song, Fanghao; Liu, Hantao

Abstract

Research indicates that a significant number of individuals are in a suboptimal auditory health state, yet their auditory function can potentially be improved through auditory training. To raise awareness of auditory health issues, auditory training apps should provide effective yet accessible training methods alongside engaging mechanisms that motivate users to adopt and sustain auditory training habits, ultimately facilitating self-directed auditory health management. Current auditory training apps overlook the importance of user needs and motivation, as well as the relationship between the two, leading to low engagement and retention rates. We document the specific needs of both normal-hearing and mildly hearing-impaired users and analyze physiological data collected during auditory training tasks. Through lab experiment study, we provide quantitative evidence supporting the effectiveness of the auditory training method used in this study. The findings indicate that audiovisual-based auditory training contributes to improved auditory performance. Building on these insights, we develop an auditory training app prototype that integrates gamification and narrative design into the auditory training app prototype, and examine their impact on user motivation and engagement. Furthermore, based on the results, we propose design recommendations for future auditory training app development, emphasizing the need to align training effectiveness with user motivation and engagement strategies.

Citation

Huang, Q., Stawarz, K., Zhao, L., Yang, S., Xie, W., Song, F., & Liu, H. (2025). Applying cross-modal plasticity principles in auditory training applications. International Journal of Human-Computer Studies, 203, Article 103570. https://doi.org/10.1016/j.ijhcs.2025.103570

• Journal Article Type: Article
• Acceptance Date: Jun 5, 2025
• Online Publication Date: Jun 27, 2025
• Publication Date: 2025-09
• Deposit Date: Jun 30, 2025
• Publicly Available Date: Jun 30, 2025
• Journal: International Journal of Human-Computer Studies
• Print ISSN: 1071-5819
• Electronic ISSN: 1095-9300
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 203
• Article Number: 103570
• DOI: https://doi.org/10.1016/j.ijhcs.2025.103570
• Public URL: https://nottingham-repository.worktribe.com/output/50978316
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S1071581925001272