Provenance record

Understanding Game Coaching on Gig Platforms

arXiv cs.CY (Computers and Society) (tier 1, academic) 2026-09-14T04:00:00.000Z Original ↗

Source note: Preprints on societal impact.


Discourse valence
+10
Mixed or uncertain
confidence 60% · 1 item
Adverse readingFavourable reading
Single-model reading. This item did not meet the threshold for a multi-model panel.
Employment and displacementHuman agency and epistemicsConcentration of power
Excerpt as ingested

arXiv:2609.12695v1 Announce Type: cross Abstract: Freelance game coaches monetize their gaming expertise by offering personalized instruction to players seeking to improve, working through gig platforms, yet little is known about how they operate. To address this gap, we conducted semi-structured interviews with 20 experienced freelance coaches across 17 competitive games on Fiverr. Despite lacking shared formal training, these coaches converged on similar practices centered on rapport-building, individualized diagnosis, and adaptive feedback. We identify two structural conditions shaping this work: dual precarity, in which coaches navigate both gig platform instability and the lifecycle volatility of live-service games; and earned authority, in which coaches must continually establish legitimacy through visible competitive achievement within the same gaming spaces as their students. These coaches welcomed AI for administrative and analytic support but resisted its use in live interactions where trust, relational engagement, and situated judgment remained central. We discuss implications for Games HCI and the design of computational coaching systems.

Every model that read this

ModelProviderStageScoreConf.LatencyPromptWhen
Llama 3.3 70BMetaanalysis +10 60%5233ms v1.0.0 / m1.0.1 2026-09-14 05:41
Llama 3.3 70B · reading

The study explores game coaching on gig platforms and AI's potential role.

evidence: speculative horizon: n/a

Evidence extracted

The chain
SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
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DOCUMENT   49db250c-ce22-4cfe-b60a-e02d25e635fe
           https://arxiv.org/abs/2609.12695
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EVIDENCE   3 extracted excerpts
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MODEL RUN  1 run, methodology 1.0.1
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SCORE      +10  (Mixed or uncertain)
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CONFIDENCE 60%