AI-Based Learning Assistant

Abstract

In the modern era of digital transformation, the field of education is undergoing a significant shift toward intelligent, technology-driven platforms that can support large-scale, personalized learning. The traditional approach to e-learning—dependent on manually constructed question banks and rigid, keyword-based answer evaluation mechanisms—struggles to meet the growing demands for contextual relevance, adaptability, and scalability. These limitations hinder the ability of educational systems to deliver meaningful feedback, especially in the case of descriptive or open-ended responses, where understanding semantics and context is crucial. To address these challenges, this project introduces a next-generation AI-based Learning Assistant that integrates Natural Language Processing (NLP), Generative AI (GenAI), and Machine Learning (ML) methodologies to automate two critical tasks: dynamic question generation and intelligent evaluation of learner responses. The system is capable of analyzing instructional content to generate context-aware questions across various difficulty levels and types (descriptive, short-answer, multiple-choice, etc.). On the assessment side, it leverages transformer-based language models to understand and evaluate student answers semantically, rather than relying on surface-level keyword matches. Our architecture employs pre-trained large language models (LLMs) such as BERT, T5, and GPT-based transformers for tasks like question paraphrasing, distractor generation, and rubric-based scoring. Additionally, fine-tuned classifiers and similarity models are used to assess the relevance, completeness, and clarity of descriptive answers. The system supports both formative and summative assessment modes, enabling real-time feedback, progress tracking, and adaptive question sequencing based on individual learner profiles. By bridging the gap between automated content generation and intelligent assessment, the proposed solution not only enhances the pedagogical value of e-learning platforms but also reduces the manual burden on educators. The assistant is scalable, language-agnostic, and can be deployed across diverse educational settings—from K-12 to higher education and corporate training—marking a step forward in the democratization and personalization of education.