Date of Award

5-10-2026

Date Published

June 2026

Degree Type

Dissertation

Degree Name

Doctor of Professional Studies

Department

Information Management

Advisor(s)

Bei Yu

Keywords

Computational Literacy;Help-Seeking Behavior;In-Context Learning;Large Language Models;Programming Education;Suppression Analysis

Abstract

Large language models (LLMs) represent a disruptive innovation in programming education, acting as both a supportive tutor and a mechanism for cognitive offloading. This multi-method study investigated how specific student engagement patterns with a custom, course-provided LLM chatbot influenced learning performance and computational literacy among novice programmers (N = 87). Through categorical content analysis of 1,024 chatbot interaction sessions, student behaviors were classified into adaptive (learning-oriented) and maladaptive (task-completion) help-seeking strategies. Quantitative analysis revealed a mutual suppression effect, demonstrating that while aggregate usage volume was an unreliable predictor of success, the type of engagement was highly significant. Findings indicated that task-completion behaviors negatively predicted midterm exam scores and computational literacy gains, whereas learning?oriented sessions positively correlated with exam performance. Additionally, a randomized controlled trial (n treatment = 39, n control = 48) demonstrated a conditional effect: a context-aware LLM significantly improved learning performance when controlling for these usage patterns, acting as a performance multiplier. Consequently, this study advocates for a pedagogical shift from managing AI access to cultivating AI literacy, ensuring students utilize generative technology as a scaffold for cognitive development rather than a substitute for it.

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Open Access

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