Developing an Intelligent Tutoring System for Computer Science using Artificial Intelligence and Machine Learning
For my capstone project, I designed and developed an intelligent tutoring system (ITS) to help students learn core concepts in computer science. An ITS is an advanced form of computer-based learning that uses artificial intelligence (AI) techniques to provide personalized instruction, feedback and guidance to students. My ITS focused on teaching topics in algorithms, data structures, programming languages and software engineering.
In designing the system, I drew upon knowledge from several key areas of computer science including AI, machine learning, human-computer interaction, databases and web development. The core of the ITS utilized AI and machine learning techniques to model a student’s knowledge, identify learning gaps and deficiencies, adapt instruction to their needs and provide individualized remedial help. It incorporated a dedicated student model that was continuously updated based on a student’s interactions with the tutoring system.
On the front-end, I designed and developed a responsive web interface for the ITS using HTML, CSS and JavaScript to provide an engaging and intuitive learning experience for students. The interface allowed students to access learning modules, take practice quizzes and exams, view step-by-step video tutorials and receive personalized feedback on their progress. It was optimized for use on both desktop and mobile devices.
For content delivery, I structured the learning materials and created interactive modules, activities and assessments covering fundamental CS topics like problem solving, algorithm design, data abstraction, programming paradigms, software engineering principles and more. The modules utilized a variety of multimedia like text, diagrams, animations and videos to explain concepts in an easy to understand manner. Students could self-pace through the modules based on their skill level and interests.
To power the back-end intelligence, I employed advanced machine learning algorithms and applied Artificial Neural Network models. A multi-layer perceptron neural network was trained on a large dataset of student-system interactions to analyze patterns and correlations between a student’s knowledge state, mistakes, provided feedback and subsequent performance. This enabled the ITS to precisely identify a student’s strengths and weaknesses to develop personalized study plans, recommend relevant learning resources and target problem areas through adaptive remedial work.
Assessments in the form of quizzes and exams were designed to evaluate a student’s conceptual understanding and practical problem-solving abilities. These were automatically graded by the system using test cases and model solutions. Detailed diagnostic feedback analyzed the exact mistakes and misconceptions to effectively guide students. The student model was also updated based on assessment outcomes through machine learning techniques like Bayesian knowledge tracing.
To power the backend data processing and provide an API for the AI/ML components, I built a database using PostgreSQL and implemented a RESTful web service using Node.js and Express.js. This facilitated real-time data exchange between the frontend interface and various backend services for student modeling, content delivery, assessment grading and feedback generation. It also supported additional capabilities like student enrollment/registration, content authoring and administrative functions.
Extensive user testing and validation was performed with a focus group of undergraduate CS students to fine-tune design aspects, evaluate learning outcomes, identify bugs/issues and measure student engagement, satisfaction and perceived learning value. Feedback was incorporated in iterative development cycles to enhance the overall user experience. Once validated, the system was deployed on a cloud hosting platform to enable broader use and data collection at scale. The ITS demonstrated the application of core computer science principles through an integrated project that combined areas like AI, ML, HCI, databases and software engineering. It proved highly effective at delivering personalized adapted learning to students in a facile manner. The system won institutional recognition and has since helped hundreds of learners worldwide gain skills in algorithms and programming.
Through this capstone project I was not only able to apply my theoretical computer science knowledge but also develop practical hands-on expertise across multiple domains. I gained valuable skills in areas such as AI system design, machine learning, full-stack web development, database modelling, project management and user evaluation methodologies. The experience of envisioning, architecting and implementing an end-to-end intelligent tutoring application helped hone my abilities as a well-rounded computer scientist. It also enabled me to effectively utilize techniques from various CS sub-domains in an integrated manner to solve a real-world problem – thus achieving the overarching goals of my capstone experience. This proved to be an immensely rewarding learning experience that has better prepared me for future career opportunities and research pursuits at the intersection of these technologies.