Theoretical Framework
The project is grounded in two complementary frameworks that explain both why students adopt AI and how that adoption can be shaped toward genuine learning.
Four Data-Backed Strategies for AI Use in AP
This project documents a year-long, data-driven initiative at Delta Global School in Hanoi to move beyond reactive AI policing toward proactive AI literacy. Four structured interventions were designed, implemented, and measured with AP-level students — each generating student data, AI narratives, and reflections that informed the next phase. The results were presented to school leadership and form the basis of the AP Annual Conference session.
The project is grounded in two complementary frameworks that explain both why students adopt AI and how that adoption can be shaped toward genuine learning.
The project began with a comprehensive school-wide survey of 220 middle and high school students to establish a baseline understanding of AI attitudes and behaviors. A key finding was a consistent gap between what students said about AI and what they actually did — particularly pronounced between middle and high school cohorts.
The second intervention used Toddle AI as a low-stakes, in-class tool for concept reinforcement and review. Students engaged in 15-minute structured AI conversations — used as either a starter or a closer — and then wrote reflective AI narratives documenting what worked, what didn't, and how they adapted their questioning strategies. Mixed feedback revealed both the promise and the limits of AI-driven review.
The third intervention directly addressed the finding that students used AI as a search engine rather than a thinking tool. AP Capstone students completed two parallel research tasks — one unstructured (free use of AI) and one following a specific step-by-step research protocol. The structured approach produced measurably better outcomes and student reflections revealed a shift in how students experienced the research process itself.
The fourth intervention moved from protocol documents to a purpose-built tool. The Socratic AI Tutor is a Streamlit web app that scaffolds students through a structured, self-regulated learning (SRL) workflow for AI-assisted problem solving. Rather than letting students jump straight to asking AI for answers, the app walks them through concept review, problem framing, and strategic prompting — then guides them to reflect on outcomes.
The app generates a ready-to-paste priming message that instructs the AI to act as a Socratic tutor — scaffolding rather than solving. Students return to the app after working with the AI to log their outcome (Success, Retry, or Mastered) and download a full session transcript for submission.
Note: The app requires activation before use. If you'd like to try it, please email me and I'll get it running for you.