Year: 2025
Type: conference-paper
Abstract: Introductory programming courses present significant challenges for novice learners, often leading to frustration and difficulty in identifying learning gaps.This research aims to develop an AI-driven tool that provides personalized guidance, moving beyond traditional "one-size-fits-all" approaches.Recognizing the limitations of relying solely on digital interaction logs in the era of generative AI, we explore the integration of student personal characteristics and fine-grained programming interactions to predict learning behavior and performance.We will investigate how to accurately predict student outcomes early in the semester, analyze the dynamics of learning behaviors, and design an AI-assisted tool to recommend tailored learning materials and feedback.Our goal is to foster effective learning and mitigate the risks associated with over-reliance on general-purpose AI, ultimately enhancing knowledge retention and problem-solving skills.
Author: Mubina Kamberovic
Institution: University of Sarajevo
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: bronze
OpenAlex ID: W4411279910