PrepWise AI: A Smart Preparation Assistant for Study and Career
Year: 2026
Type: article
Abstract: Modern undergraduate engineering students face a dual challenge: mastering semester syllabi within compressed academic timelines while simultaneously cultivating the technical and communicative competencies that industry recruitment processes demand. Existing digital tools tend to address only one of these dimensions in isolation, leaving students without a cohesive and adaptive support ecosystem. This paper introduces PrepWise AI, a full-stack, privacy-preserving intelligent platform that consolidates academic study planning and multimodal interview preparation into a single unified environment. The system is organized around four interdependent modules. The Smart Study Planner interprets free-form natural language exam prompts and constructs structured, day-wise preparation schedules through constrained prompt engineering over a locally hosted large language model (LLM). The Internal Assessment (IA) Planner bridges teacher-defined institutional syllabi with student-facing AI-generated resources, dynamically calibrating content depth and emphasis based on examination proximity. The Resources Module delivers curated theory notes enriched with diagrams alongside an embedded Data Structures and Algorithms (DSA) coding environment powered by the Monaco Editor, with real-time test-case evaluation. The AI Interview Assistant conducts comprehensive multimodal candidate evaluation through synchronized video analysis—comprising MediaPipe Face Mesh landmark detection, pixel-level frame processing, and YOLO-based object detection—alongside audio preprocessing using FFmpeg for extraction, resampling, and normalization of the audio signal, followed by pitch estimation and speech rate computation, culminating in semantic answer evaluation via a locally deployed LLM. The backend is implemented using Flask and FastAPI microservices. All LLM inference executes on-premise through the Ollama platform using Qwen 2.5 (7B) and Mistral models. Firebase Firestore supports institutional data persistence. Experimental evaluation with thirty undergraduate participants yielded a schedule relevance score of 86.4%, a user satisfaction rating of 4.2 out of 5.0, and a plan completion rate of 72.4%. Interview assessment quality was validated by a Pearson correlation of r = 0.81 with expert human raters, accompanied by a semantic evaluation accuracy of 84.7%, speech clarity consistency of 87.2%, and speech-to-text accuracy of 91.3%, at a mean LLM response latency of 4.2 seconds. These results collectively confirm that PrepWise AI constitutes a practical, scalable, and pedagogically sound contribution to AI-assisted academic and career preparation.
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7159670647