AN INTERPRETABLE AI MODEL FOR INTELLIGENT IT SUPPORT TICKET PRIORITIZATION IN DIGITAL ORGANIZATIONS
Keywords:
artificial intelligence, ICT in education, programming courses, adaptive feedback, code analysis, learning analytics, explainable AI, digital learning.Abstract
Programming courses require continuous practice, timely feedback and the ability to understand not only whether a program works, but why it fails. Traditional assessment usually checks final output or syntax errors, while many student difficulties are connected with hidden misconceptions: boundary cases, variable scope, loop logic, data structure choice and algorithmic thinking. This paper proposes an explainable artificial intelligence-assisted framework for adaptive feedback in programming courses. The framework combines code analysis, test execution, learning analytics and natural-language explanation into a transparent feedback cycle. Instead of replacing the teacher, the model supports the teacher by detecting frequent error patterns, estimating the learner misconception level, and recommending the next task according to the student profile. A scenario-based analytical comparison shows that the proposed approach can improve feedback clarity, reduce delayed correction and support individualized learning in programming education.
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