A Smart Educational Assistant for Conceptual Learning
Miss. Akshata Dunagi *1, Drakshayani H Joshi *2
*1Teaching Assistant, Department Of Computer Science, Rani Channamma University, Dr. P.G.Halakatti P.G Centre, Toravi, Vijayapur, India.
*2PG Scholar, Department Of Computer Science, Rani Channamma University, Dr. P.G. Halakatti P.G Centre, Toravi, Vijayapur, India.
Abstract –
In the era of artificial intelligence, educational methodologies are undergoing a significant transformation, moving towards more personalized, adaptive, and student-centered learning experiences. Traditional teaching methods often struggle to meet the diverse needs of students, especially in mixed-ability classrooms where learners have varying levels of prior knowledge, understanding, and learning styles. Despite advances in technology, a major challenge persists in translating long-established educational principles into scalable, effective solutions that genuinely address individual learning gaps and promote conceptual understanding. To bridge this gap, this project introduces an AI-powered educational assistant designed to enhance deeper learning by providing adaptive feedback, personalized guidance, and tailored learning pathways that go beyond rote memorization. The system leverages advanced AI technologies to dynamically generate quizzes, explanations, flashcards, and other study tools, reinforcing principle-based learning while mitigating cognitive overload. Its backend is built using Flask and integrates with the Groq LLM API to enable AI-driven content creation, while the frontend offers a responsive and interactive experience, featuring Supabase authentication for secure access and Chart.js analytics to visualize learning progress. By combining personalized assessment, continuous feedback, and data-driven insights, this educational assistant transforms traditional passive learning into an engaging, adaptive, and concept-focused experience. Ultimately, the system empowers students to achieve meaningful mastery of subjects, supports individualized learning at scale, enhances engagement, and reduces cognitive load, effectively creating a holistic and modern approach to education that aligns with the needs of today’s learners.
Key Words: Artificial Intelligence (AI) in Education, Personalized Learning, Adaptive Learning, Large Language Models (LLMs), Prompt Engineering, AI-Assisted Learning, Groq API, Principle-Based Learning
