Generative AI Integration in Education: Theoretical Review and Future Directions Informed by the ADO Framework

The accelerated integration of Generative Artificial Intelligence (GenAI) tools such as ChatGPT is transforming learner engagement, instructional design, and institutional governance in education. This systematic literature review synthesizes theory-driven scholarship on GenAI adoption and pedagogical use through the Antecedents–Decisions–Outcomes (ADO) framework, examining how cognitive, motivational, technological, and institutional factors collectively shape implementation and learning outcomes. Drawing primarily on the Technology Acceptance Model (TAM), Self-Determination Theory (SDT), and Institutional Theory, the review integrates complementary insights from Constructivist Learning and Diffusion of Innovations perspectives to conceptualize how antecedents influence decision-making and outcomes across educational settings. The findings indicate that learner motivation, perceived usefulness, digital literacy, and institutional readiness constitute key antecedents affecting GenAI adoption. Decision processes—spanning instructional design, ethical regulation, and pedagogical adaptation—mediate how these antecedents translate into practice. Outcomes reveal a dual trajectory: GenAI enhances personalization, feedback, and self-regulated learning, yet introduces challenges related to ethical ambiguity and overreliance. The review offers a conceptually integrated synthesis that bridges motivational, technological, and organizational perspectives, advancing a theoretical roadmap for ethical and sustainable GenAI adoption. For educators and policymakers, the findings emphasize transparent governance, faculty capacity-building, and equitable access to ensure that innovation remains aligned with pedagogical integrity and human-centered values.