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The system combines an AI Tutor powered by large language models, Voice Recall for active retrieval using the Web Speech API, and an SM-2 algorithm-based spaced repetition flashcard engine. An adaptive quiz engine, AI-assisted rich-text workspace with PDF imports, and a 3D knowledge graph support visual concept mapping, while a dashboard tracks learning metrics, streaks, mastery scores, and weak topics. 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Of Computer Science & Engineering, Aiarkp, Panvel.  \nAbstract- MentorAI is a comprehensive, web-based intelligent learning assistant designed to transform how students study, retain knowledge, and engage with educational content. The platform integrates a personalized AI Tutor powered by large language models, a Voice Recall system for active retrieval practice using the Web Speech API, an SM-2 algorithm-driven Spaced Repetition Flashcard engine, an AI-generated adaptive Quiz Engine, an AI-assisted rich-text Workspace for notes and PDF imports, a 3D Knowledge Graph for visual concept mapping using D3.js force-directed visualization, and a command-palettestyle Nexus navigation system. A central Dashboard aggregates learning metrics, study streaks, mastery scores, and AI-detected weak topics in real time. This paper presents the complete system architecture, feature design rationale, technology stack, database design, security model, testing methodology, and results achieved during the development of MentorAI as a final year engineering capstone project.  \nKeywords – Personalized Learning, AI Tutor, Spaced Repetition, SM-2 Algorithm, Voice Recall, Web Speech API, Knowledge Graph, D3.js, Adaptive Quiz Engine, Flashcard System, Large Language Models, Retrieval Practice, Study Analytics, EdTech.  \nI. INTRODUCTION  \nThe modern educational landscape is undergoing a profound transformation, driven by advances in artificial intelligence, natural language processing, and interactive web technologies. Students today face not only an ever- expanding volume of content to master but also a persistent challenge in retaining and applying that knowledge effectively. Conventional study methods — passive re-reading, static flashcards, and linear note-taking — are increasingly inadequate in meeting the demands of personalized, evidence-based learning.  \nResearch in cognitive science has consistently demonstrated that active retrieval practice, spaced repetition, and selfexplanation are among the most effective strategies for longterm memory retention. Yet the majority of digital learning tools remain fragmented: one application for flashcards, another for quizzes, a third for note-taking, and yet another for AI-assisted tutoring. This fragmentation forces learners to manage multiple platforms, losing contextual continuity and the compounding benefits that arise from a unified, data-driven learning ecosystem.  \nMentorAI is conceived as a direct response to these challenges. It is an intelligent, web-based learning assistant that integrates seven tightly coupled study modules—AI Tutor, Voice Recall, Flashcards, Quiz Engine, Workspace, Knowledge Graph, and a Dashboard — into a single cohesive platform. Each module feeds data into the others: quiz errors become flashcards, voice recall gaps surface in the AI Tutor, and all study interactions  \nupdate a dynamic knowledge graph and mastery dashboard. The result is a platform that learns about the learner and continuously adapts to their evolving knowledge state.  \nThis paper is organized as follows: Section II defines the problem statement. Section III lists the project objectives. Section IV reviews relevant literature. Section V describes the system architecture. Section VI details each feature. Section VII covers the technology stack. Section VIII discusses the database design. Section IX outlines security measures. Section X describes the testing approach. Section XI presents results, and Section XII concludes the paper.  \nMentorAI was developed by a team of final-year Diploma in Computer Engineering students over approximately five months using an Agile methodology with two-week sprint cycles, peer code reviews, and iterative user feedback. The platform is fully browser-b","cbCaia0Rr176T7gu","https://ap.wps.com/l/cbCaia0Rr176T7gu","pdf",340306,"English","# I. Introduction\n## Background and motivation\n## Research basis and limitations of existing tools\n## Proposed solution and module integration\n# II. Problem Statement\n## Gap 1: limited personalization\n## Gap 2: weak retrieval practice support\n## Gap 3: lack of holistic knowledge visualization\n## Gap 4: disconnected AI tutoring","[{\"question\":\"What is MentorAI and what learning problem does it target?\",\"answer\":\"MentorAI is a web-based intelligent learning assistant designed to make study more personalized and effective. It targets fragmentation in learning tools and the lack of evidence-based retrieval, visualization, and adaptive guidance.\"},{\"question\":\"How does MentorAI support active retrieval practice?\",\"answer\":\"MentorAI includes a Voice Recall system that uses the Web Speech API so learners can articulate concepts aloud. It also uses an SM-2 algorithm-driven spaced repetition flashcard engine to strengthen long-term retention.\"},{\"question\":\"How does MentorAI adapt to a learner’s knowledge state?\",\"answer\":\"All study interactions feed a shared data flow into a dynamic 3D knowledge graph and a central dashboard. Quiz errors, voice recall gaps, and flashcard performance update mastery scores and highlight weak topics in real time.\"}]","MentorAI - A Smart Web-Based Learning Assistant with Personalized Guidance and Interactive Study Support | PDF",23]