In today's rapidly-evolving educational system environment, students generally find it difficult to prepare for both college entrance exams and placement testing. Most of the platforms that currently exist are fragmented with no personal feedback or actionable information. PadippAI aims to streamline this process as an AI-guided study aid that archives and analyzes previous question papers. Using Natural Language Processing (NLP), PadippAI identifies important and previously-asked questions and topics, and generates question sets as study aids to enhance Learning. It even features adaptive aptitude and technical tests that automatically calibrates to each student's level, allowing students to be directed towards their areas for improvement. By bridging an academic preparation student's study toward college exams and exam preparation for placements, PadippAI saves time, enhances structure, and boosts student confidence in their discipline. This research presents and evaluates an intelligent platform that focuses on question paper archiving, NLP analysis, and adaptive tests, to address effectiveness in preparation for exams and placements. The system employs transformer-based NLP model protocol, a topic extraction framework, and a method for changing difficulty to create the individual learner experience. The system comprises four major components aimed at improving assessment and learning with technology: (i) Digital Repository to create and securely manage questions, (ii) Intelligent Analysis Engine to organize questions based on difficulty and topics, (iii) Adaptive Assessment System with assessments which are assigned based upon the performance level of the student, and (iv) Personalized Learning Dashboard to measure each student's individual progress and illustrate trends of performance. The platform was evaluated in research with a sample of 500 students over a 12-week period, where engagement and test scores were the outcome variables. Results of the data analysis indicated that students involved with this system demonstrate large, observable increases in their engagement, and importantly were overwhelmingly positive in their performance levels on any test indicating the system supported adaptive learning and high-quality learning outcomes.