Designing and Governing Trustworthy AI Marketing Systems in Educational Technology: A Managerial and Implementation Framework - Chen, Chuntie; Zakaria, Nor Hidayati Binti; Deng, Wei; Xu, Xiaoli; Xu, Youyu (Digital Technologies Research and Applications, 2026)
This study proposes a dual-dimensional framework to design such technologies in the EdTech industry using the conceptual framework methodology, which is based on thematic content analysis and theory-driven and evidence-based validation using scholarly literature, industry reports, and policies from the top EdTech organizations worldwide. The framework identifies trustworthiness as the key construct that integrates two interrelated dimensions. The governing dimension is operational at the managerial level and includes governance principles based on an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework and personalization governance principles based on three dimensions, such as intensity, tempo, and boundaries. The designing dimension is operational at the implementation level and identifies the technical requirements related to virtual sales personnel systems and AI promotion systems. The results of the validation process against known platform practices demonstrate a mixed pattern of alignment, with stronger support in regulatory-related areas and weaker support in governance-intensive domains where the framework extends current industry practice. The extension of the UTAUT model from personal acceptance to organizational governance represents a theoretical contribution with a link to existing research in personal acceptance and its expanded applicability. The three-dimensional personalization governance model has more detailed mechanisms than the current one-dimensional approaches. For educational technology organizations, the framework offers systematic guidance in the development of AI-based marketing systems that are trustworthy to users while being effective for organizational goals. Validation results indicate tempo governance and recommendation explainability as areas that need development in the industry for effective engagement with users in a sustainable manner.