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arxiv: 2501.10551 · v4 · submitted 2025-01-17 · 💻 cs.HC

An Empirical Study to Understand How Students Use ChatGPT for Writing Essays

Andrew Jelson , Daniel Manesh , Alice Jang , Daniel Dunlap , Young-Ho Kim , Sang Won Lee This is my paper

Pith reviewed 2026-05-23 05:45 UTC · model grok-4.3

classification 💻 cs.HC
keywords ChatGPT usageessay writingAI usage patternsstudent self-efficacyperceived ownershipgender differencesAI in educationhuman-AI interaction
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The pith

Gender, race and self-efficacy predict distinct ChatGPT query patterns in student essay writing, which relate to enjoyment and ownership.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper reports results from an online study of 70 students who wrote essays on a custom platform that logged every query sent to ChatGPT. By manually sorting those queries into usage types, the authors show that gender, race, and students' own ratings of self-efficacy forecast which patterns appear. Students who followed different patterns then reported different levels of enjoyment during the task and different degrees of ownership over the finished essay. The work matters because any claim about whether AI tools help or harm learning must first account for these varying styles of use rather than assume uniform behavior.

Core claim

Through an online study with 70 participants, the authors categorized students' queries to ChatGPT during an essay task. They determined that gender, race, and perceived self-efficacy predict different usage patterns. These patterns in turn relate to different levels of enjoyment and perceived ownership over the resulting essay. The findings are offered as input to discussions on how writing classrooms should incorporate generative AI tools.

What carries the argument

Manual categorization of each student query to ChatGPT into usage types, followed by statistical analysis linking those types to demographic variables, self-efficacy ratings, enjoyment, and perceived ownership.

Load-bearing premise

The manual categorization of student queries into usage types is reliable and reproducible, and the self-reported metrics of enjoyment, ownership, and self-efficacy accurately capture the underlying constructs without substantial social-desirability bias.

What would settle it

An independent re-coding of the same query logs that produces markedly different category assignments for a large share of queries, or a new study in which self-reported self-efficacy shows no statistical association with observed query behavior.

Figures

Figures reproduced from arXiv: 2501.10551 by Alice Jang, Andrew Jelson, Daniel Dunlap, Daniel Manesh, Sang Won Lee, Young-Ho Kim.

Figure 1
Figure 1. Figure 1: The overview of Research Questions We analyzed the interaction traces to understand how the resulting essay was composed (e.g., the percentage of words pasted from ChatGPT) and how these essay characteristics varied by student group. The result of this question will characterize how each group used ChatGPT queries to affect the essay quantitatively. Lastly, we investigated how students’ use of ChatGPT rela… view at source ↗
Figure 2
Figure 2. Figure 2: The editor view of the website 3.1 Instrument Development: Writing Platform + ChatGPT Development To understand how students use ChatGPT, we developed a platform that tracked their queries and the corresponding responses. Since ChatGPT is an independent app, we built a system that integrates an in-house ChatGPT — referred to simply as ChatGPT from this point — within the writing platform, using the default… view at source ↗
Figure 3
Figure 3. Figure 3: The view of our ChatGPT page To track how users interact with ChatGPT, we implemented a custom version of ChatGPT using the OpenAI API (model GPT-3.5-turbo), as shown in [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Data Flow Diagram between Editor, Participant, and ChatGPT [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: K-means clustering SSE and Cluster information [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Query distribution per group 4.1.6 Interaction Patterns and Writing Stage Transitions. We examined how many times each participant asked questions to ChatGPT to show the distribution of query counts, as shown in Figure 6a. Overall, nearly half of the participants (39 out of 77) used ChatGPT less than or equal to twice; six participants, classified as Group N, never used it at all. Meanwhile, some participa… view at source ↗
Figure 7
Figure 7. Figure 7: Transition matrix and state diagram for ChatGPT inquiries [PITH_FULL_IMAGE:figures/full_fig_p020_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Box Plots for significant Kruskal-Wallis tests with Post Hoc analysis (* indicates [PITH_FULL_IMAGE:figures/full_fig_p022_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Box Plots for Perceived Ownership and CSI - Enjoyment [PITH_FULL_IMAGE:figures/full_fig_p023_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Box Plots for CSI Expressiveness and Immersion [PITH_FULL_IMAGE:figures/full_fig_p023_10.png] view at source ↗
read the original abstract

As large language models (LLMs) advance and become widespread, students increasingly turn to systems like ChatGPT for assistance with writing tasks. Educators are concerned with students' usage of ChatGPT beyond cheating; using ChatGPT may reduce their critical engagement with writing, hindering students' learning processes. The negative or positive impact of using LLM-powered tools for writing will depend on how students use them; however, how students use ChatGPT remains largely unknown, resulting in a limited understanding of its impact on learning. To better understand how students use these tools, we conducted an online study $(n=70)$ where students were given an essay-writing task using a custom platform we developed to capture the queries they made to ChatGPT. To characterize their ChatGPT usage, we categorized each of the queries students made to ChatGPT. We then analyzed the relationship between ChatGPT usage and a variety of other metrics, including students' self-perception, attitudes towards AI, and the resulting essay itself. We found that factors such as gender, race, and perceived self-efficacy can help predict different AI usage patterns. Additionally, we found that different usage patterns were associated with varying levels of enjoyment and perceived ownership over the essay. The results of this study contribute to discussions about how writing education should incorporate generative AI-powered tools in the classroom.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper reports results from an online study (n=70) in which participants completed an essay-writing task on a custom platform that logged all queries sent to ChatGPT. Queries were manually categorized into usage patterns; the authors then examined statistical associations between these patterns and demographic variables (gender, race), perceived self-efficacy, attitudes toward AI, enjoyment, perceived ownership of the final essay, and essay quality. The central claims are that demographic and self-efficacy factors predict distinct usage patterns and that usage patterns are associated with differences in enjoyment and ownership.

Significance. If the query categorization proves reliable and the reported associations hold after appropriate controls, the work supplies one of the first behavioral datasets on how students actually interact with LLMs during writing, moving beyond self-report surveys. The combination of logged queries with self-report measures is a methodological strength that could support more nuanced classroom guidelines. The study also surfaces potential equity issues (gender/race differences) that warrant follow-up.

major comments (2)
  1. [Methods] Methods section (query categorization procedure): No information is supplied on the number of coders, inter-rater reliability statistic, codebook construction (inductive vs. deductive), or disagreement-resolution protocol. Because the usage-pattern variable is the independent variable for all subsequent predictive and correlational claims, the absence of these details renders the reported associations between patterns and demographics/enjoyment/ownership difficult to interpret or replicate.
  2. [Results] Results section (statistical tests): The manuscript reports associations involving multiple demographic and self-report predictors but provides no mention of multiple-comparison correction, effect-size reporting, or a priori exclusion criteria. With n=70 and several tested relationships, uncorrected significance tests risk inflated Type I error and weaken the claim that gender, race, and self-efficacy reliably predict usage patterns.
minor comments (2)
  1. [Abstract] Abstract: The abstract states that queries were 'categorized' but does not name the resulting categories or their derivation, leaving readers without an immediate sense of the granularity of the usage typology.
  2. [Discussion] Discussion: Self-report scales for enjoyment, ownership, and self-efficacy are presented without reference to prior validation studies or discussion of social-desirability bias, which is relevant given the educational context.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback, which highlights important areas for improving the transparency of our methods and statistical reporting. We will revise the manuscript to address these concerns.

read point-by-point responses

  1. Referee: [Methods] Methods section (query categorization procedure): No information is supplied on the number of coders, inter-rater reliability statistic, codebook construction (inductive vs. deductive), or disagreement-resolution protocol. Because the usage-pattern variable is the independent variable for all subsequent predictive and correlational claims, the absence of these details renders the reported associations between patterns and demographics/enjoyment/ownership difficult to interpret or replicate.

    Authors: We agree that these details are critical for the interpretability and replicability of our findings. In the study, two authors independently coded the queries using an inductive approach, developing the codebook iteratively based on the data. Disagreements were resolved through discussion until consensus was reached. We computed Cohen's kappa as a measure of inter-rater reliability. We will update the Methods section to fully describe this process, including the number of coders, the inductive nature of the codebook, the disagreement resolution protocol, and the reliability statistic. revision: yes

  2. Referee: [Results] Results section (statistical tests): The manuscript reports associations involving multiple demographic and self-report predictors but provides no mention of multiple-comparison correction, effect-size reporting, or a priori exclusion criteria. With n=70 and several tested relationships, uncorrected significance tests risk inflated Type I error and weaken the claim that gender, race, and self-efficacy reliably predict usage patterns.

    Authors: We appreciate this point regarding statistical rigor. Our study was exploratory in nature, as it is one of the first to log and categorize actual ChatGPT queries in an essay-writing task. We did not apply multiple-comparison corrections because the analyses were not confirmatory, but we will explicitly state this in the revised Results section. We will also report effect sizes for all key associations to provide a fuller picture of the practical significance. For exclusion criteria, we had no a priori exclusions beyond incomplete data or technical logging failures; we will add a clear statement on participant exclusion in the Methods or Results. These changes will strengthen the reporting without altering the core findings. revision: yes

Circularity Check

0 steps flagged

Empirical observational study with no derivations or self-referential reductions

full rationale

The paper describes an online study (n=70) collecting ChatGPT queries during an essay task, followed by categorization of queries and analysis of correlations with demographics, self-efficacy, enjoyment, and ownership. No equations, fitted parameters, predictions derived from models, or derivations appear in the provided text. Central claims rest on data collection and statistical associations rather than any step that reduces by construction to its own inputs or prior self-citations. This is a standard empirical design with no load-bearing circular elements.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

No free parameters or invented entities; relies on standard statistical assumptions for observational data analysis and the validity of self-report instruments.

axioms (2)
  • domain assumption Student self-reports of enjoyment, ownership, and self-efficacy accurately reflect internal states without substantial response bias.
    Invoked implicitly when linking usage patterns to these metrics; common in HCI survey studies but untested here.
  • domain assumption Manual categorization of ChatGPT queries produces stable, reproducible categories.
    Central to characterizing usage patterns; no inter-rater reliability reported in abstract.

pith-pipeline@v0.9.0 · 5776 in / 1339 out tokens · 50109 ms · 2026-05-23T05:45:11.162699+00:00 · methodology

discussion (0)

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Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

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  4. The Crutch or the Ceiling? How Different Generations of LLMs Shape EFL Student Writings

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