AI-Driven Content Quality Beyond Technological Convenience: A Dual-Track Model of Sustainable Architectural Heritage Engagement

Sustainable building research increasingly incorporates AI technologies to enhance efficiency and decision-making, yet little is known about how algorithmic mediation shapes the cultural identity processes essential for heritage sustainability. This study proposes and validates a Content-Driven Dual-Track (CDDT) Model examining the relationships among AI content quality (AIQ), technology acceptance, and Architectural Cultural Identity (ACI). Based on a survey of 631 architecture and design students, structural equation modeling identified three patterns. First, AIQ strongly predicts perceived usefulness, perceived ease of use (PEOU), and perceived enjoyment, supporting a content-driven formation of system evaluations. Second, an “ease-of-use paradox” is observed: PEOU negatively relates to ACI (β = −0.18, p = 0.005), suggesting that frictionless browsing may hinder the cognitive effort required for deeper heritage value internalization. Third, ACI independently predicts continuous engagement intention (β = 0.110, p < 0.05) and correlates strongly with perceived content quality (r = 0.719, p < 0.001). Together, these findings suggest that while operational convenience serves as an essential entry point, sustainable digital heritage engagement requires moving beyond interface usability to prioritize the cultural depth of content assets, a principle applicable to BIM-driven cultural heritage systems and AI-based educational platforms.