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arxiv: 2605.23123 · v1 · pith:MA5Y6S4Hnew · submitted 2026-05-22 · 💻 cs.CY · cs.AI· cs.HC

Defining AI Fatigue in Academic Contexts: Dimensions, Indicators, and a Stage-Based Model Using Grounded Theory

John Paul P. Miranda , Emmanuel B. Parre\~no , Jovita G. Rivera This is my paper

Pith reviewed 2026-05-25 03:39 UTC · model grok-4.3

classification 💻 cs.CY cs.AIcs.HC
keywords AI fatigueacademic contextsgrounded theorydimensions of fatiguestage-based modelcognitive overloadstudent AI use
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The pith

Sustained academic use of AI tools produces five dimensions of fatigue and a stage-based model of how they accumulate.

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

This study applies grounded theory to open-ended responses from 1,054 university students in the Philippines to define AI fatigue as a strain from AI tools in academic tasks. It identifies five dimensions—Cognitive Overload, Motivational Disengagement, Moral Unease, Physical Strain, and Attentional Drift—each with two indicators from the data. The work also introduces the AI Fatigue Model, which outlines stages where these pressures build and reinforce one another. Readers might care because AI tools are becoming central to learning, yet their long-term effects on students remain unclear under current frameworks.

Core claim

Analysis produced five dimensions of AI fatigue, namely Cognitive Overload, Motivational Disengagement, Moral Unease, Physical Strain, and Attentional Drift, each consisting of two indicators grounded in participant accounts. The findings also yielded the AI Fatigue Model, a stage-based framework that explains how these pressures accumulate and reinforce one another across repeated AI interaction in academic tasks. These contributions establish a conceptual and exploratory foundation for AI fatigue as a distinct construct and provide a basis for future instrument validation, scale development, and cross-contextual inquiry in academic settings where AI now mediates student learning.

What carries the argument

The AI Fatigue Model, a stage-based framework derived from grounded theory that shows accumulation and reinforcement of five fatigue dimensions.

Load-bearing premise

The coding of responses from this sample of students in three Philippine universities is enough to show that AI fatigue is a distinct construct not covered by existing technostress or digital fatigue ideas.

What would settle it

Surveying a similar group of students using established technostress questionnaires and finding that all reported AI-related strains are already captured by those measures would challenge the need for a new construct.

Figures

Figures reproduced from arXiv: 2605.23123 by Emmanuel B. Parre\~no, John Paul P. Miranda, Jovita G. Rivera.

Figure 1
Figure 1. Figure 1: Conceptual Model of AI Fatigue 4. Discussion This study identified five dimensions of AI fatigue that emerge from sustained academic use of AI tools among university students: Cognitive Overload, Motivational Disengagement, Moral Unease, Physical Strain, and Attentional Drift. Each dimension was grounded in participant accounts and supported by two indicators that captured specific and recurring forms of p… view at source ↗
read the original abstract

The integration of AI tools in academic settings has introduced a distinct form of strain that existing frameworks like technostress and digital fatigue have not yet fully addressed. This study develops a conceptual model and identifies the dimensions that define AI fatigue as a form of strain arising from sustained academic use of AI tools. Using grounded theory analysis of open-ended responses from 1,054 university students across three universities in the Philippines, the study examined the cognitive, motivational, emotional, physical, and attentional pressures students experienced during AI-supported academic work. Analysis produced five dimensions of AI fatigue, namely Cognitive Overload, Motivational Disengagement, Moral Unease, Physical Strain, and Attentional Drift, each consisting of two indicators grounded in participant accounts. The findings also yielded the AI Fatigue Model, a stage-based framework that explains how these pressures accumulate and reinforce one another across repeated AI interaction in academic tasks. These contributions establish a conceptual and exploratory foundation for AI fatigue as a distinct construct and provide a basis for future instrument validation, scale development, and cross-contextual inquiry in academic settings where AI now mediates student learning.

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 / 1 minor

Summary. The paper claims to define AI fatigue as a distinct form of strain in academic AI use through a grounded theory analysis of open-ended responses from 1,054 students across three Philippine universities. It identifies five dimensions—Cognitive Overload, Motivational Disengagement, Moral Unease, Physical Strain, and Attentional Drift—each with two indicators, and proposes the AI Fatigue Model, a stage-based framework explaining the accumulation and mutual reinforcement of these pressures over repeated interactions.

Significance. Should the dimensions prove irreducible to existing constructs and the model accurately capture the process, this study would provide a useful conceptual foundation for understanding AI-specific fatigue in educational contexts, facilitating the development of measurement instruments and further empirical research. The use of a relatively large sample for grounded theory and the participant-driven identification of dimensions are positive aspects of the approach.

major comments (2)
  1. [Abstract] Abstract: The claim that 'existing frameworks like technostress and digital fatigue have not yet fully addressed' the described strain is central to positioning the contribution but is not accompanied by any indicator-by-indicator mapping or systematic comparison to established scales (e.g., Tarafdar technostress items), leaving the distinctness untested.
  2. [Methods] Methods: The manuscript does not report coding procedures, inter-rater reliability, saturation criteria, or the analytic steps used to derive the stage-based model from the data, which are necessary to evaluate the grounded theory process underlying the five dimensions and the model.
minor comments (1)
  1. [Abstract] Abstract: The abstract refers to 'three universities in the Philippines' without specifying whether they are public or private or their locations, which could aid in assessing generalizability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which help clarify the positioning of our contribution and improve methodological transparency. We respond to each major comment below.

read point-by-point responses

  1. Referee: [Abstract] The claim that 'existing frameworks like technostress and digital fatigue have not yet fully addressed' the described strain is central to positioning the contribution but is not accompanied by any indicator-by-indicator mapping or systematic comparison to established scales (e.g., Tarafdar technostress items), leaving the distinctness untested.

    Authors: We agree that the absence of an explicit mapping leaves the distinctness claim less substantiated than it could be. Although the study is inductive and the dimensions emerged from participant data, a direct comparison would strengthen the positioning. In the revised manuscript we will add a table or subsection that maps each of the five dimensions and their indicators against core technostress items (Tarafdar et al.) and digital-fatigue constructs, noting overlaps and areas where participant accounts suggest AI-specific elements not fully captured by prior scales. revision: yes

  2. Referee: [Methods] The manuscript does not report coding procedures, inter-rater reliability, saturation criteria, or the analytic steps used to derive the stage-based model from the data, which are necessary to evaluate the grounded theory process underlying the five dimensions and the model.

    Authors: We acknowledge that these procedural details are missing from the current Methods section and are required for proper evaluation of the grounded-theory analysis. In the revision we will expand the Methods section to specify: the coding sequence (open, axial, and selective coding), the number of coders and inter-rater reliability statistic (e.g., Cohen’s kappa on a 20 % subsample), the saturation criterion applied, and the iterative analytic steps that integrated the dimensions into the stage-based model. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation is bottom-up from data

full rationale

The paper applies grounded theory coding to open-ended responses from 1,054 students to identify five dimensions and a stage-based model, with all elements explicitly grounded in participant accounts. No equations, fitted parameters, predictions, or self-citation chains appear in the provided text. The central claim that AI fatigue is distinct rests on the coding process itself rather than reducing any output to an input by construction, satisfying the default expectation of a self-contained qualitative derivation.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that grounded theory applied to this dataset can validly delineate a new construct; no free parameters or invented entities are introduced.

axioms (1)
  • domain assumption Grounded theory methodology applied to open-ended student responses can reliably identify dimensions of a new psychological construct distinct from existing frameworks.
    Invoked throughout the abstract as the basis for deriving the five dimensions and stage model.

pith-pipeline@v0.9.0 · 5741 in / 1310 out tokens · 34143 ms · 2026-05-25T03:39:40.028403+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

11 extracted references · 11 canonical work pages

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    Introduction Students now use Artificial Intelligence (AI) tools in many academic tasks, and this shift exposes them to pressures that existing research has not yet defined or explained. Studies on technostress show that digital tasks can create overload and mental strain, with overuse of technology consistently associated with elevated stress among unive...

    work page 2024

  2. [2]

    Can you describe your experiences using AI chatbots such as ChatGPT or similar tools for your studies?

    Methodology 2.1 Research Design This study utilized a qualitative design guided by grounded theory. The design allowed the researchers to analyze student accounts of AI -related strain, determine whether the pressures formed a coherent structure, and develop a conceptual model grounded directly in participant accounts. 2.2 Research Locale, Population, and...

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  3. [3]

    My mind is now having a hard time thinking or functioning

    Results The analysis of 1,054 participant responses produced five dimensions of AI fatigue through the three-stage grounded theory coding process described in the Methodology. This process produced ten indicators organized under five dimensions: Cognitive Overload , Motivational Disengagement, Moral Unease, Physical Strain, and Attentional Drift. The resp...

    work page

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    Each dimension was grou nded in participant accounts and supported by two indicators that captured specific and recurring forms of pressure during AI - supported academic work

    Discussion This study identified five dimensions of AI fatigue that emerge from sustained academic use of AI tools among university students: Cognitive Overload, Motivational Disengagement, Moral Unease, Physical Strain, and Attentional Drift. Each dimension was grou nded in participant accounts and supported by two indicators that captured specific and r...

    work page 2025

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    Conclusion This study addressed a gap in existing technology -related strain frameworks by identifying five dimensions of AI fatigue, namely Cognitive Overload, Motivational Disengagement, Moral Unease, Physical Strain, and Attentional Drift, through grounded theory a nalysis of responses from 1,054 students across three universities in the Philippines. U...

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    A., & Khan, T

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