Muhamad Fadil Akbar@Deel

AI-DOP is an agentic AI system that analyzes student signals using 3C Triangulation (Core Competency, Career Compass, Contextual Capacity) to detect wellbeing risks, map talents, and trigger automated early-intervention workflows using IBM watsonx.

AI-DOP (Analytical Intervention & Observation Pipeline) is an agentic AI-powered workflow designed to help schools understand students more deeply and intervene earlier when risks appear. Built using IBM watsonx Orchestrate, AI-DOP connects multiple custom agents—NLP Analyzer, 3C Classifier, Risk Detection Agent, and SMID Impact Agent—into a single orchestrated intelligence pipeline. The system analyzes raw text generated by students across different channels (confession walls, chat rooms, journaling, and virtual homeroom interactions). Using a structured framework called 3C Triangulation, AI-DOP produces three types of insights: • C1 – Core Competency (academic behavior and skill patterns) • C2 – Career Compass (interests, passions, and early talent signals) • C3 – Contextual Capacity (mental wellbeing, stress indicators, peer dynamics) When emotional distress, bullying cues, or conflicting behavior patterns appear, the Risk Agent automatically generates an early-warning signal. These insights are then summarized inside the SMID – Student Measurement Impact Dashboard, giving counselors a clear, real-time understanding of each student's wellbeing and potential. AI-DOP demonstrates how agentic workflows can solve real problems in education by orchestrating reasoning, classification, and decision-making tools without requiring teachers to interpret large volumes of student data manually. This approach helps schools reduce unnoticed emotional issues, support talent development earlier, and provide safer learning environments. The project includes a functional backend, orchestrated multi-agent design, and a visual demo dashboard to showcase real use cases, such as bullying detection, talent identification, and wellbeing monitoring.

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