AI-Driven Tutorial Code Learning System: Personalized Programming Education Through Adaptive Instruction and Gamification
adaptive learning; artificial intelligence in education; gamification; intelligent tutoring systems; personalized learning; programming education
Existing programming education faces critical challenges, such as lack of personalization, restricted feedback tools, and scalability limitations that hinder efficient learning outcomes. This paper presents an AI-powered tutorial code learning system to transform programming education through personalized and adaptive instruction. The system integrates advanced components, including learner modeling, intelligent content recommendation, error analysis, adaptive evaluation, gamification, learning analytics, integration frameworks, quality assurance, security, and scalability layers. To evaluate the system, the study employs a mixed-methods research approach, incorporating embedded case studies and a randomized controlled trial (RCT). Rigorous data collection methods, system measure validation, undergraduate program participant selection, quality assurance protocols, statistical analysis, and ethical considerations are utilized in this work. The architecture demonstrates potential for scalable and globally accessible programming education, addressing traditional challenges through personalized learning protocols. Unlike traditional platforms offering static content and limited feedback, this AI-powered system acts as a personalized tutor, providing active problem-solving and continuous learner engagement. The adaptive system delivers optimal learning paths based on individual student needs and has the potential to transform programming education delivery and outcomes. 2025 IEEE.
Sharafudheen, Nashwa; Rohini, V.; Malviya, Meenakshi
Proceedings of 2025 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2025;
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/IConSCEPT66142.2025.11437327 https://www.scopus.com/pages/publications/105036597327?origin=resultslist
Sharafudheen N., CHRIST (Deemed to be University), Department of Computer Applications, Bengaluru, India; Rohini V., CHRIST (Deemed to be University), Department of Computer Applications, Bengaluru, India; Malviya M., CHRIST (Deemed to be University), Department of Computer Applications, Bengaluru, India