Human Centered Learning and Teaching using AI in Python Projects

Human Centered Learning and Teaching using AI in Python Projects

Abstract

Human-centered learning and teaching aim to optimize educational experiences by tailoring instruction to individual learners’ needs, preferences, and abilities. The project Human-Centered Learning and Teaching using AI in Python Projects focuses on developing an intelligent system that leverages artificial intelligence to enhance personalized learning, assess student performance, and support adaptive teaching strategies. Python is used as the development platform due to its comprehensive libraries for machine learning, natural language processing, and data analytics, including TensorFlow, Keras, Scikit-learn, Pandas, NumPy, and NLTK. The system analyzes student data such as learning style, performance metrics, interaction patterns, and assessment results to provide customized learning pathways, real-time feedback, and recommendations for teachers to improve engagement and outcomes. By integrating AI with pedagogy, the system promotes effective, personalized, and human-centered education.

Existing System

Existing educational systems typically follow standardized teaching methods with limited adaptation to individual learners. Many platforms offer online courses or e-learning modules, but they often lack personalization and rely on generic content delivery. Traditional classroom assessment methods, such as tests and quizzes, do not provide real-time insight into learning gaps or behavioral patterns. While some adaptive learning systems exist, they frequently depend on rule-based algorithms or simple analytics, which fail to capture complex learner behaviors or dynamically adjust instruction. Consequently, educators are unable to provide truly personalized guidance, and students may not achieve optimal learning outcomes.

Proposed System

The proposed system introduces a Python-based AI framework for human-centered learning and teaching. Data from student interactions, assessment results, learning preferences, and engagement metrics are collected and preprocessed to ensure consistency and reliability. Machine learning models, including clustering, decision trees, neural networks, and reinforcement learning, are applied to identify learning gaps, predict performance, and recommend personalized learning pathways. Natural language processing (NLP) techniques are used to analyze student responses, questions, or essays to provide automated feedback. The system generates visual dashboards for teachers and students, showing progress, recommended actions, and adaptive content delivery. By integrating AI-driven insights with pedagogy, the system enhances individualized learning, improves teaching effectiveness, and fosters a human-centered educational environment where students receive tailored guidance and support.