Dynamic college path projector system and method for ai-powered, multi-year personalized college admissions roadmap generation

An AI-powered platform for generating a personalized multi-year college admissions roadmap for a student, the platform comprising:

• a cloud-hosted LLM and data computation system configured to perform operations comprising:
• ingesting a structured student data profile comprising academic data including grade point average (GPA), course enrollment data, and standardized test scores;
• extracurricular data including activity type, duration, leadership level, and time commitment;
• and honors and awards data, wherein the AI-powered platform is configured for:
• retrieving, for each of one or more target colleges, a target college data profile comprising admission statistics and college-specific weighting parameters for a plurality of admissions criteria;
• computing, for each target college, an Admissions Strength Index (ASI) by applying a weighted composite scoring function-implemented via a LLM and machine learning inference layer—to the student data profile and the target college data profile across at least six predefined modules comprising (i) academics, (ii) extracurricular activities, (iii) major selection, (iv) honors and awards, (v) application materials, and (vi) financial aid and scholarships;
• identifying, via an AI gap analysis engine, developmental gaps between the student data profile and the target college data profile for each of the six predefined modules;
• generating, via a LLM recommendation engine, a plurality of action-item pathways at a plurality of difficulty levels, each pathway comprising a set of recommended tasks and a projected admissions probability computed by a machine learning model;
• constructing a multi-year dynamic roadmap comprising the recommended tasks, associated timelines, milestones, and priority rankings;
• and
• updating the multi-year dynamic roadmap in response to ingestion of updated student data profile inputs.