Year: 2025
Type: article
Study designs: Randomized Controlled Trial, Clinical Trial
Abstract: With the intensification of population aging, elderly individuals face significant barriers in learning digital skills, leading to a widening digital divide. Based on Cognitive Aging Theory, this study proposes and validates an original paradigm called Structured Gamified Intergenerational Digital Reverse Mentorship to enhance digital literacy among the elderly. Cognitive Aging Theory suggests that due to declines in memory, attention, and executive function, older adults encounter challenges when learning new technologies, while gamified learning combined with intergenerational interaction can help reduce cognitive load and increase learning motivation. This study designed a collaborative gamified digital reverse mentorship application, “Digital Bridge”, and employed a randomized controlled trial method, assigning 90 participants aged 60 and above into three groups: the traditional digital mentorship group (Group A), the independent gamified learning group (Group B), and the collaborative gamified digital mentorship group (Group C). Each intervention session lasted 30 min and was conducted in a controlled environment. Experimental results showed that Group C significantly outperformed Groups A and B in digital skill acquisition, user experience, and learning motivation (p < 0.001), indicating that the combination of gamified learning and intergenerational interaction effectively enhances learning interest, reduces learning anxiety, and improves skill transferability. This study provides a new approach to elderly digital literacy education and offers theoretical and practical support for the design of future age-friendly digital learning tools.
DOI: 10.3390/mti9060064
Author: Sijie Sun
Institution: Universitat Autònoma de Barcelona
Language: English
Subfield: Demography
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,930
Funder: China Scholarship Council
Award: G875357961
OpenAlex ID: W4411456331
