Self-Reported AI Usage for Learning in Computer Science Education: Relationships with Goal Orientation and Academic Help-Seeking

This research investigates the factors influencing university students' self-reported Artificial Intelligence (AI) usage for learning, specifically within computer science education. It explores the relationships between students' goal orientation, academic help-seeking behaviors, and AI use, while also considering individual, behavioral, and contextual characteristics. The study, based on survey data from 236 database course students, aims to provide a clearer understanding of how these elements shape AI adoption in higher education.

Why it matters

Understanding the drivers of AI adoption in educational contexts is crucial for effective integration of AI technologies into learning processes. This insight can inform the development of educational strategies and tools that better align with student behaviors and learning objectives, ultimately enhancing academic outcomes and preparing students for an AI-driven future.

Key insights

• AI is increasingly integrated into higher education settings.
• Factors influencing students' AI use for learning are not yet fully understood.
• The study specifically examines how students' goal orientation and academic help-seeking relate to AI use.
• Individual, behavioral, and contextual characteristics are also considered in the analysis.
• Data was collected via self-report surveys from 236 university students in a database course.
• AI use was measured by self-reported frequency and the number of AI-supported learning activities.
• Hierarchical regression analyses were used to determine relationships among variables.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.21373