LearnCurve: Enhancing Learning Outcomes Through AI-Driven Generation and Mastery-Based Progression

Abstract

Self-directed digital learning fails at the intersection of pacing and personalization: platforms deliver uniform content regardless of what individual learners already know or where their understanding breaks down. This paper presents LearnCurve, an AI-driven adaptive learning platform that addresses this through three integrated mechanisms: First an AI-based pipeline that generates structured, hierarchical learning roadmaps tailored to a learner\'s goal, skill level, and timeline. Second a Mastery Index (MI) algorithm that synthesizes six per-topic performance dimensions into a continuous score driving real-time content adaptation.Third structured focus sessions with AI-generated post-session summaries that reinforce knowledge consolidation immediately after study. Unlike platforms that apply AI narrowly to recommendation or chatbot assistance, LearnCurve embeds it within a closed feedback loop spanning generation, assessment, adaptation, and reinforcement ensuring every learner interaction reshapeswhat that learner encounters next.

Introduction

This paper introduces LearnCurve, an AI-powered adaptive learning platform designed to address one of the biggest challenges in online education: the lack of personalized learning pace. Traditional online courses follow fixed learning paths that fail to accommodate differences in learners' prior knowledge, leading to low course completion rates and poor engagement.

LearnCurve uses artificial intelligence to generate personalized learning roadmaps, assessments, and study schedules that continuously adapt based on each learner’s progress. Unlike existing AI-assisted learning platforms that mainly provide content recommendations or chatbot support, LearnCurve integrates AI throughout the entire learning process—from roadmap generation to assessment, scheduling, and revision.

The major contributions of the system include:

• Adaptive Roadmap Engine: Automatically creates structured learning plans with phases, milestones, topics, estimated study durations, and learning resources tailored to the learner's goals.
• Mastery Index (MI): A continuous score (0–100) that measures learner understanding using quiz accuracy, response time, completion rate, confidence, attempts, and revisions, classifying topics as Weak, Developing, or Mastered.
• Focus Session Pipeline: Supports structured, timed study sessions followed by AI-generated summaries to reinforce learning.
• Closed Adaptive Feedback Loop: Continuously updates mastery scores based on learner interactions and dynamically adjusts future learning content and priorities without manual intervention.

The proposed architecture consists of five integrated layers: Frontend, Backend, Data, AI, and Real-Time Synchronization. AI-generated learning roadmaps are validated through a strict JSON schema to ensure reliable and structured outputs. The system also separates computationally intensive roadmap generation from lightweight quiz and summary generation to improve efficiency.

To personalize learning, LearnCurve tracks six performance metrics for each topic: accuracy, average response time, completion ratio, attempt count, revision count, and confidence score. These metrics are combined into the Mastery Index, where quiz accuracy receives the highest weight (50%), followed by time efficiency and completion ratio (20% each), and confidence (10%). The Mastery Index guides adaptive scheduling, ensuring learners spend more time on weak concepts while progressing quickly through mastered topics.

Conclusion

This research has presented LearnCurve, a platform that embeds AI into the complete learning loop rather than applying it at isolated points. The fine-tuned AI model generates a structured, hierarchical roadmap on demand, eliminating the authoring bottleneck that has historically constrained adaptive learning systems to well-resourced subject domains. The Mastery Index provides a continuous, multi-dimensional measure of per-topic proficiency that drives a closed adaptive feedback loop: every quiz, session, and Kanban interaction updates mastery scores, which immediately reshape what the learner encounters next. Focus sessions with AI-generated consolidation summaries apply cognitive science findings on post-study memory consolidation at negligible cost per session. The pilot evaluation provides early evidence that this integrated approach produces meaningful gains in topic completion, quiz participation, and session adherence compared to static roadmap delivery. Crucially, the gains appear attributable to the adaptive scheduling mechanism itself particularly the re-prioritization of low-MI topics rather than simply to content availability. As AI models continue to improve in factual reliability and fine-tuning efficiency, and as retrieval-augmented generation techniques mature, the case for deeply integrating purpose-trained AI models into learning platforms strengthens considerably. LearnCurve establishes a concrete architectural blueprint for that integration: one in which AI does not augment the learning experience at the periphery but drives it from within.

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Copyright

Copyright © 2026 Dr. Vinay Kumar Nassa, Snehit Pandey. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.