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arxiv: 2604.27812 · v1 · submitted 2026-04-30 · 💻 cs.HC

"It depends on where AI is used": Players' attitude patterns and evaluative logics toward different AI applications in digital games

Ting-Chen Hsu , Jiangxu Lin , Wenran Chen , Fei Qin , Zheyuan Zhang This is my paper

Pith reviewed 2026-05-07 05:57 UTC · model grok-4.3

classification 💻 cs.HC
keywords AI in gamesplayer attitudesevaluative logicsthematic analysisdigital gamesAI acceptanceimmersionautonomy
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The pith

Players accept AI in digital games only when it enhances immersion or efficiency without threatening creativity or autonomy

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

The paper examines how players evaluate eight specific AI uses in games, including intelligent NPCs, emergent narratives, dynamic balancing, recommendation systems, review processes, art generation, co-creation, and gameplay evolution. Based on nearly 1,900 open-ended responses from 310 players, it finds systematic patterns: AI gains support for boosting immersion, personalization, novelty, efficiency, and convenience, but faces resistance when it risks creativity, emotional authenticity, autonomy, fairness, stability, authorship, or accountability. The responses cluster into six evaluative logics that organize player reasoning. A sympathetic reader cares because broad pro-AI or anti-AI positions overlook these context-specific reactions that shape actual adoption. If the patterns hold, developers gain a practical map for choosing which AI features to implement and how to frame them to players.

Core claim

Players welcomed AI when it enhanced immersion, personalization, novelty, efficiency, or convenience, but resisted it when it threatened creativity, emotional authenticity, autonomy, fairness, system stability, authorship, or accountability. Responses organized around six evaluative logics: experiential enrichment, instrumental efficiency, system reliability, agency and control, authorship and compliance, and human oversight.

What carries the argument

Thematic analysis of open-ended responses to eight distinct AI application contexts, which surfaces six evaluative logics that players apply when judging acceptance or rejection

Load-bearing premise

That thematic analysis of self-reported open-ended responses from a sample of 310 players accurately captures generalizable evaluative logics without significant researcher bias or limitations from convenience sampling

What would settle it

A larger, more diverse survey or in-game experiment in which player attitudes toward the same eight AI contexts do not cluster into the six logics and instead show uniform acceptance or rejection regardless of application

Figures

Figures reproduced from arXiv: 2604.27812 by Fei Qin, Jiangxu Lin, Ting-Chen Hsu, Wenran Chen, Zheyuan Zhang.

Figure 2
Figure 2. Figure 2: integrates these findings by linking the eight application contexts to broader sites of AI intervention and to the six evaluative logics. The model shows that players' attitudes depend less on AI use in games in general than on where AI is introduced, what role it performs, and which values it affects. Player acceptance or resistance can therefore be understood as a context￾sensitive evaluation of AI's exp… view at source ↗
read the original abstract

As AI becomes increasingly embedded in digital games, players' attitudes de-pend not only on whether AI is used, but also on where and how it intervenes in gameplay. This study examines players' evaluative patterns toward eight AI application contexts, including intelligent NPCs, emergent narrative, dynamic balancing, recommendation systems, review and governance, art asset generation, co-creation gameplay, and gameplay evolution. Based on 1,856 valid open-ended responses from 310 questionnaires, we conducted thematic analysis to identify reasons for acceptance, rejection, and conditional acceptance. Results show that players welcomed AI when it enhanced immersion, personalization, novelty, efficiency, or convenience, but resisted it when it threatened creativity, emotional authenticity, autonomy, fairness, system stability, authorship, or accountability. We further identify six evaluative logics: experiential enrichment, instrumental efficiency, system reliability, agency and control, authorship and compliance, and human oversight. These preliminary findings highlight the context-sensitive nature of AI acceptance in digital games.

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 / 3 minor

Summary. This paper investigates players' attitudes toward AI in digital games across eight specific application contexts using a survey of 310 participants that collected 1,856 open-ended responses. Through thematic analysis, it categorizes reasons for acceptance (e.g., enhanced immersion, personalization, novelty, efficiency) and rejection (e.g., threats to creativity, emotional authenticity, autonomy, fairness, authorship), and proposes six evaluative logics: experiential enrichment, instrumental efficiency, system reliability, agency and control, authorship and compliance, and human oversight. The key finding is that acceptance depends on the specific use of AI rather than AI in general.

Significance. If the results hold, the paper makes a meaningful contribution to HCI and game studies by moving beyond binary views of AI acceptance to a context-sensitive framework of evaluative logics. The empirical basis with a reasonable sample size and focus on diverse AI applications (from NPCs to art generation) provides actionable insights for game designers and AI developers. It highlights important player concerns around agency and authenticity that could influence ethical AI deployment in entertainment media.

major comments (2)
  1. [Methods] The thematic analysis is not described with sufficient rigor. No details are provided on the coding process, including codebook creation, involvement of multiple researchers, inter-rater reliability statistics, or saturation assessment. This is a load-bearing issue for the central claim, as the six evaluative logics are directly derived from this analysis of the 1,856 responses, and without these elements, it is difficult to rule out researcher bias in theme identification.
  2. [Methods] The participant recruitment and sample characteristics are insufficiently detailed. While convenience sampling is implied, there is no discussion of potential biases (such as overrepresentation of dedicated gamers from specific online communities) or how this might influence the emergence of logics like 'agency and control' and 'authorship and compliance'. This affects the generalizability of the findings.
minor comments (3)
  1. [Abstract] Typographical error: 'de-pend' should read 'depend'.
  2. [Results] To strengthen the presentation, include representative verbatim quotes from participants for each of the six evaluative logics to demonstrate how the themes were identified from the data.
  3. Consider adding a table summarizing the acceptance/rejection reasons across the eight AI contexts for better readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and positive review, which highlights the paper's potential contribution while identifying areas for methodological improvement. We agree with the comments and will revise the manuscript to enhance transparency and rigor in the Methods section.

read point-by-point responses

  1. Referee: [Methods] The thematic analysis is not described with sufficient rigor. No details are provided on the coding process, including codebook creation, involvement of multiple researchers, inter-rater reliability statistics, or saturation assessment. This is a load-bearing issue for the central claim, as the six evaluative logics are directly derived from this analysis of the 1,856 responses, and without these elements, it is difficult to rule out researcher bias in theme identification.

    Authors: We appreciate the referee's emphasis on methodological transparency, as the thematic analysis underpins the six evaluative logics. The original manuscript provided a high-level overview but lacked the requested specifics. In the revision, we will expand the Methods section to describe: iterative codebook development via open coding on an initial subset of responses; involvement of two researchers who independently coded all 1,856 responses with weekly consensus discussions to resolve discrepancies; inter-rater reliability assessed via Cohen's kappa on a 20% random sample (targeting >0.75); and thematic saturation evaluation, which was reached after coding approximately 65% of responses with no new themes emerging. These details will strengthen confidence in the derivation of the logics and address potential bias concerns. revision: yes

  2. Referee: [Methods] The participant recruitment and sample characteristics are insufficiently detailed. While convenience sampling is implied, there is no discussion of potential biases (such as overrepresentation of dedicated gamers from specific online communities) or how this might influence the emergence of logics like 'agency and control' and 'authorship and compliance'. This affects the generalizability of the findings.

    Authors: We agree that fuller disclosure of recruitment and sample limitations is needed to contextualize the findings. We will revise the Methods section to detail the convenience sampling approach (primarily via Reddit gaming subreddits, Discord servers, and university networks) and include comprehensive demographics (e.g., age distribution, gender, weekly gaming hours, and self-reported expertise). A new Limitations subsection will explicitly discuss self-selection bias toward dedicated gamers, noting that this may amplify logics such as agency and control or authorship and compliance. We will also suggest that future work with more representative samples could test broader applicability, thereby improving the paper's transparency on generalizability without overstating the current results. revision: yes

Circularity Check

0 steps flagged

No circularity: central claims derived from primary data collection and inductive thematic analysis

full rationale

The paper collects original questionnaire responses from 310 participants yielding 1,856 open-ended answers, then applies thematic analysis to surface acceptance/rejection reasons and six evaluative logics. No equations, fitted parameters, self-citations, or prior-work ansatzes are invoked to generate these results; the logics and patterns are presented as emerging directly from the new data. The derivation is therefore self-contained and does not reduce to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper does not introduce free parameters or invented entities. It relies on standard assumptions in qualitative HCI research about the validity of self-reported attitudes and the reliability of thematic analysis for identifying underlying logics.

axioms (1)
  • domain assumption Self-reported attitudes in open-ended survey responses can be thematically analyzed to reliably reveal distinct evaluative logics without substantial loss or distortion of meaning.
    This assumption is invoked when mapping the 1,856 responses to the six evaluative logics of experiential enrichment, instrumental efficiency, system reliability, agency and control, authorship and compliance, and human oversight.

pith-pipeline@v0.9.0 · 5486 in / 1582 out tokens · 70235 ms · 2026-05-07T05:57:06.852456+00:00 · methodology

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Reference graph

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    High personalization/richness: Players believe AI-NPCs can create personalized experiences, enrich game content, make experiences more diverse, and bring greater freshness

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    High replayability: Players believe it improves gameplay value, increases replayability, and reduces repetition

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    Strong novelty/freshness: Players expect greater freshness, higher anticipation, stronger ex- ploration desire, varied experiences per playthrough, lower repetition, more randomness and sur- prise, higher openness, and more diverse endings

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    Extending game lifespan: Players explicitly mention that AI -generated narratives make the game more enduring and extend its lifecycle. Scenario 3: Attitudes toward AI for Dynamic Game Difficulty/Balance Adjustment [9 themes] Themes: [Enhanced personalization/adaptability]; [Reducing balancing costs]; [Unstable balancing]; [Dis- rupting game rhythm]; [Enh...

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    Enhanced personalization/adaptability: AI can meet the needs of players at different skill levels and create personalized experiences

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    Reducing balancing costs: AI lowers the design effort and repeated manual adjustments re- quired for game balance, improving efficiency

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    Enhancing immersion: AI adjustments improve player engagement and willingness to partic- ipate

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    Reducing frustration: Helps lower -skill players avoid frustration and reduces dropout rates (new-player friendly)

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    Maintaining challenge: Prevents the game from becoming too easy for high -skill players, maintaining motivation through balanced challenge

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    Reduced sense of achievement: Dynamic easing makes victories feel unearned or diminishes the reward of overcoming difficulty. Scenario 4: Attitudes toward AI for Personalized Matching or Recommendation Systems [7 themes] Themes: [Prone to monotony]; [Inaccurate matching]; [Enhancing gaming experience]; [Improving gam- ing efficiency]; [Privacy concerns]; ...

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    Prone to monotony: Long-term recommendations based on past behavior create filter bubbles, aesthetic fatigue, and lack of novelty

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    Inaccurate matching: AI fails to accurately understand player preferences, abilities, or char- acteristics

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    Enhancing gaming experience: Improves immersion, engagement, interaction quality, bal- ance, fairness, etc

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    Improving gaming efficiency: Saves time and mental effort in selecting content or opponents

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    Catering to diverse preferences: Effectively meets varied player needs and delivers unique personalized experiences

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    Impaired autonomy: Players resist being overly guided or having choices made for them. Scenario 5: Attitudes toward AI for Report Review or Community Governance [7 themes] Themes: [Lack of emotional feedback]; [Strong fairness/objectivity]; [Missed/misjudgment concerns]; [Bias/fairness concerns]; [High efficiency]; [Need for human-AI collaboration]; [Lack...

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    Lack of emotional feedback: AI responses feel robotic, insincere, or perfunctory

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    Strong fairness/objectivity: AI delivers logical, consistent, and fair rulings in most cases

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    Missed/misjudgment concerns: AI may be unstable, miss violations, or wrongly classify con- tent

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    Bias/fairness concerns: AI may systematically favor certain player groups

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    High efficiency: Much faster processing and timely feedback than human moderation

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    Need for human-AI collaboration: Players prefer a hybrid approach rather than full AI or full human control

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    Copyright risks: Concerns about infringement or unclear legal status of AI-generated assets

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    AI -like,

    Weak innovation/creativity: Outputs feel generic, “AI -like,” and lack the spark of human creativity

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    Low aesthetics: Reduces visual/auditory quality and causes aesthetic downgrade

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    High aesthetics: Delivers superior visual/auditory quality compared to traditional methods

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    Poor controllability: Frequent hallucinations, anatomical errors, style inconsistency, etc

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    Benefiting developers: Accelerates iteration, lowers costs, and improves development effi- ciency

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    Lack of creator empathy: Players cannot emotionally connect with or feel the human artistic intent behind the work

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    Low generation quality: Assets look bad, sound bad, or feel uninteresting

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    High generation quality: Assets look good, sound good, and feel high-quality

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    Lack of authenticity/emotion: Images feel unreal, music/voice lacks soul, etc

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    Strong perfunctory feeling: Use of AI assets feels disrespectful to players, as if the develop- ers cut corners. Scenario 7: Attitudes toward AI Supporting Player Co -Creation of Personalized Content [11 themes] Themes: [Copyright/compliance concerns]; [Lowering creation threshold]; [Meeting diverse demands]; [Improving creation efficiency]; [Enhancing im...

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    Copyright/compliance concerns: Risk of infringement, violation of platform rules, ethics, or law

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    Lowering creation threshold: Enables players with limited skills to realize their ideas

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    Meeting diverse demands: Allows realization of varied personal visions and unique experi- ences

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    Improving creation efficiency: Saves time realizing ideas

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    Enhancing immersion: Increases engagement and investment in the game

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    Enhancing novelty/richness: Continuously introduces new, unique elements

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    Quality concerns: Generated content may be low-quality or fail to meet expectations

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    High freedom/openness: Expands game boundaries and player creative freedom

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    Lack of authorship: Players feel the creations are not truly “theirs.”

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    Meeting sense of achievement: The creation process provides strong accomplishment

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    Meeting sharing desire: Encourages players to share their creations. Scenario 8: Attitudes toward AI for Emergent/Dynamic Gameplay Mechanisms [8 themes] Themes: [Meeting diverse demands]; [Content quality concerns]; [Disrupting balance/system stability]; [Improving development efficiency]; [Increasing cognitive load]; [Enhancing immersion/explo- ration de...

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    Meeting diverse demands: Realizes many player-desired mechanics and personalized experi- ences

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    Content quality concerns: Emergent elements may be low-quality or poorly implemented

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    Disrupting balance/system stability: New elements break designed balance, world tone, or cause bugs and chaos

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    Improving development efficiency: Reduces developer workload for new content, mods, or expansions

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    Increasing cognitive load: Constant new elements cause fatigue and make deep system mas- tery difficult

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    Enhancing immersion/exploration desire: Encourages deeper engagement and motivation to explore

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    Increasing novelty/richness: Provides ongoing fresh experiences and greater content depth

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    Extending game lifespan: Makes the game more enduring and worthy of long-term play

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