Understanding Student Use of Large Language Models Across Computer Science Subfields

2026-10-01

AI Summary

This study investigates whether undergraduate students across computer science subdisciplines exhibit divergent usage behaviors with large language models (LLMs) and how such differences can inform instructional design optimization. Through an empirical cross-subdisciplinary study involving 211 students in a multi-module course, the authors integrate structured reflective pedagogy with questionnaire surveys to systematically analyze LLM usage frequency, role perception, and verification strategies. The findings reveal that even under uniform instructions, task characteristics significantly shape human-AI interaction patterns. Specifically, algorithm-oriented courses demonstrate high LLM adoption rates, whereas software engineering courses show comparatively lower usage. Students generally perceive LLMs as auxiliary tools, and their verification strategies dynamically adapt according to task types. This work provides critical empirical evidence for differentiated computing education design in the AI era.

Abstract

This research full paper examines how undergraduate students use large language models (LLMs) across computer science subfields. As LLMs become increasingly integrated into computing education, understanding how their use varies across technical and pedagogical contexts is essential for designing effective, subfield-aware instruction. This paper presents a cross-subfield analysis of LLM usage among 211 undergraduate students in a problem-solving course intentionally designed to support responsible and effective LLM use through structured instruction and reflection. Using post-assignment reflection data collected across seven instructional modules spanning multiple computer science subfields, we examine prompt counts, LLM role conceptualization, and verification behavior. Results show that LLM adoption varies substantially by assignment, with higher usage in algorithms and web development and lower usage in software engineering. Students predominantly treat LLMs as assistive tools rather than authoritative sources, and verification is common across all subfields, with most students using multiple strategies. Verification behavior also varies by assignment context, with testing more common in structured tasks and web search more common in open-ended tasks. These findings suggest that assignment characteristics play a central role in shaping how students interact with and evaluate LLM outputs, even under a single, consistently applied instructional design. This work contributes empirical evidence on how LLM adoption, role conceptualization, and verification behavior vary across computer science subfields, extending our prior work on structured, reflective LLM instruction to show how its effects differ by task rather than only in aggregate.

Problem

• Large Language Models
• Computer Science Education
• Student Usage
• Verification Behavior
• Cross-subfield Analysis

Innovation

• Large Language Models
• Computing Education
• Cross-subfield Analysis
• Verification Behavior
• Reflective Instruction