A Systematic Review of Undergraduate Programming Difficulties: Highlighting AI Tools to Address Learning Challenges

Undergraduate students find programming quite challenging due to various factors, such as limited problem-solving skills, and difficulties with debugging. If these obstacles are not addressed effectively, they would hinder learners' programming learning and even cause a higher dropout rate. Although various aspects of programming can be found in prior studies, existing literature still lacks up-to-date reviews and technological solutions regarding undergraduates' programming challenges. Thus, this systematic review addressed this gap by reviewing articles between 2015 and 2024 to find the programming difficulties faced by university students by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [1]. The result showed that their obstacles included programming fundamentals, debugging and error types, code quality and organization, problem-solving, learning and cognitive aspects and external factors. Additionally, this paper highlights the integration of AI tools to address these difficulties, including readability, runtime errors and writing codes. This study makes contributions to the literature by comprehensively reviewing undergraduates' programming difficulties and linking them to innovative AI tools, providing some practical recommendations for researchers to enhance programming education.