2026-10-02T22:20:02Z https://dcpapers.dublincore.org/oai

oai:dcpapers.dublincore.org:952626597 2026-09-10 dcmi-2026 openaire

Observability over Trialability: AI Metadata Attributes and Adoption Categories in Canva Usage Among Undergraduate Students in Surabaya Atmi, Ragil Tri Rahayu, Marsanda Lintang This study findings on the relationship between AI metadata attributes and adopter categories among undergraduate Canva users in Surabaya, Indonesia. Referring to Rogers’s Diffusion of Innovation theory (2003), five innovation attributes are used as a human-centered structured metadata scheme to evaluate AI innovation. A quantitative survey of 280 students measured using a Likert scale. Students were classified into three adopter categories: early adopters, majority, and late adopters. The findings reveal a striking asymmetry: Trialability attribute does not have a significant relationship with the adopter categories, whereas Observability shows a strong and significant relationship. Although Canva provides easily testable AI features, students adopt them more after seeing the success of their peers. This underscores the importance of social systems as mechanisms for AI diffusion. This study contributes to the DCMI 2026 theme “Meaning-Driven AI” thru the concept of “social adoption metadata” which are observational signals from peers that shape AI adoption decisions. AI systems aligned with human values need model social context as a structured metadata, not just technical capabilities. Implication for human-centered metadata design in AI-integrated learning are discussed. Dublin Core Metadata Initiative 2026-09-10 info:eu-repo/semantics/conferenceObject Text application/pdf text/html https://doi.org/10.23106/dcmi.952626597 https://dcpapers.dublincore.org/article/952626597 Dublin Core Metadata Initiative Conference Proceedings eng info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/