Ai-tutor framework for online programming courses

Abstract

This research report presents the design, development and evaluation of a proposed AI-Tutor framework designed to augment the capabilities of online tutors in supporting learners in introductory programming courses. The proposed framework addresses challenges such as limited personalised feedback, debugging difficulties, and reduced learner engagement. The study used a design science research methodology to systematically develop and evaluate the proposed AI-Tutor framework. A theoretical framework was developed to guide the design and development of the proposed AI-Tutor framework. A systematic literature review was conducted to identify the proposed framework's design criteria, components and requirements. The proposed AI-Tutor framework incorporates features, including personalised feedback, automated debugging tools, and interactive learning activities. These features are designed to provide a more engaging and effective learning experience for learners enrolled in online IPCs. A panel of experts evaluated the framework’s usability, practicality, and potential impact, providing qualitative feedback that informed refinements. Analysis revealed the potential to improve learner outcomes, and enhance understanding, problem-solving skills, and satisfaction. This research contributes to the online programming education by demonstrating the AI’s potential to aid the learning experience and offering a practical framework for educators, developers and researchers seeking to enhance the online learning experience.