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Behavioral Analysis of Human-AI Interaction in AI-Assisted Programming and Data Science Learning

The rapid advancement of artificial intelligence (AI) has positioned data science and programming as essential competencies in higher education.

Author: Yu-Ting Shih 10 pages 3 figures Lag Sequential Analysis Generative AI
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Publication Info

Source file: 20250424905.pdf

Keywords

Lag Sequential Analysis, Generative AI, Programming Education

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Overview

The rapid advancement of artificial intelligence (AI) has positioned data science and programming as essential competencies in higher education.

Key Points

  • Background: The rapid advancement of artificial intelligence (AI) has positioned data science and programming as essential competencies in higher education.
  • Method: The study presents a clear research design that can support further work in educational technology and AI-supported learning.
  • Findings: The article highlights the value and applicability of AI-related approaches in educational and learning contexts.
  • Significance: Relevant themes for follow-up discussion include: Lag Sequential Analysis, Generative AI, Programming Education.

Summary

The rapid advancement of artificial intelligence (AI) has positioned data science and programming as essential competencies in higher education. However, students without a computer science background often struggle with the abstract reasoning required for programming. This study explores how 103 undergraduates interacted with an AI-based virtual learning partner during a general education course, analyzing 710 dialogue records through lag sequential analysis. Results identified seven behavior types and revealed that task submission (TS) was the most frequent, reflecting a highly task-oriented learning style. A key finding was the bidirectional transition between information seeking (IS) and cognitive/metacognitive interaction (CM), forming a "learning exploration loop" indicative of reflective and integrative learning strategies. Transitions from IS to task-oriented requests (TO) further showed students applying knowledge to concrete tasks. Social engagement (SE) patterns, including…

Figures

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