Information Package On Scholar Quant Yasser Noori, Frank Kocun November 23, 2025
About ScholarQuant ScholarQuant is an adaptive scholarship matching and AI drafting system created to address a clear weakness in existing scholarship tools. Most platforms stop at listing opportunities or applying static scoring, which leaves students to reuse one generic essay across many awards without understanding what each scholarship truly values. ScholarQuant treats every scholarship as a distinct personality with its own priorities, then helps students understand those priorities, measure fit, and draft targeted applications that remain faithful to their real experiences. Under the hood, ScholarQuant runs a pipeline that imports scholarship descriptions, winner story examples, and structured student profiles, then performs quantitative pattern finding algorithmically. The system learns adaptive property weights per scholarship, builds cross type heatmaps, extracts high signal n gram themes from winner essays, and measures correlation structure between scholarship dimensions. Instead of hiding these results behind a single opaque score, ScholarQuant surfaces them as interpretable visualizations. Claude sits on top as the language analyst, translating patterns into clear guidance and producing scholarship specific drafts grounded in the student’s base story. This meets the Agentiiv Challenge requirements for pattern recognition, adaptive scoring, content generation, explainable AI, and demonstrable improvement over generic drafting, while reflecting the hackathon focus on safe, human centered systems that extend human reasoning. The Dashboard gives an immediate read on progress and fit. Live performance indicators show how many scholarships are in the library and how many drafts have been generated. A demand versus you visualization compares what the library typically rewards across seven qualities, academics, leadership, community impact, adversity and resilience, research, innovation, and financial need, against what the user’s profile demonstrates. The largest gaps rise to the top, and improvement blurbs explain what is already strong and what concrete proof point or reframing would close the next gap. A vertical scholarship feed anchors navigation into deeper analysis. On the Scholarships page, users add awards through a focused input module, and each addition triggers analysis. A scrollable library lists awards with type tags and priority chips. Selecting one opens a clean scholarship summary with a suggested framing strategy and a personality profile visualization that displays numeric weight intensities across dimensions. Student Profiles keep evidence structured through a story bank tagged by dimensions, compact academic context, and a reusable base story editor that autosaves. An alignment constellation overlays the student’s seven dimension shape against scholarship type shapes so fit shifts and missing evidence are visible at a glance. Pattern Lab exposes the analytics driving recommendations. Heatmaps show type level priorities, Theme Explorer surfaces winner essay n grams with lift and frequency, and an interactive correlation map reveals which dimensions tend to co occur with values and explanations. Draft Studio completes the loop. Users select a profile and scholarship, add positive and negative guidance, tune focus and length, and generate a tailored essay. Ethical guardrails are always enforced: Claude cannot invent experiences, distort timelines, or overwrite locked text. Across all pages, ScholarQuant keeps both matching and drafting transparent, user governed, and grounded in real data.
Built With
- claude
- react-native
- zustand
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