cs.CY · 2026-05-25 · No. 9

Computers and Society, 2026-05-25.

3 new papers in cs.CY. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

3 entries
  1. 01

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

    John Paul P. Miranda, Emmanuel B. Parreño, Jovita G. Rivera

    cs.CY · cs.AI · cs.HC

    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...

    arxiv.org/abs/2605.23123 · PDF

  2. 02

    Whose Good, Whose Place? The Moral Geography of Agentic AI for Social Good

    Poli Nemkova, Haeshitha Indukuri, Jaedon Charles

    cs.CY · cs.AI

    Agentic AI systems are increasingly proposed for social-good domains, often invoking the United Nations Sustainable Development Goals (SDGs) as a vocabulary of global benefit. Yet claims of social good do not establish accountability to the communities a system claims to serve. We present a structured survey of 112 papers on agentic AI for social good published between 2015 and 2026. We find a moral-geographic asymmetry: papers are least...

    arxiv.org/abs/2605.22995 · PDF

  3. 03

    Healthcare LLM Benchmarks Are Only as Good as Their Explicit Assumptions

    Naveen Raman, Santiago Cortes-Gomez, Mateo Dulce Rubio, Fei Fang, Bryan Wilder

    cs.CY · cs.AI · cs.LG

    Benchmarks are necessary for healthcare evaluation, but are not sufficient for predicting deployment performance. Our position is that the evaluation--deployment gap arises not because of poorly designed benchmarks, but from implicit assumptions about how users interact with models that cannot be surfaced from benchmarks alone. To make this precise, we propose a classification of assumptions into two categories: task, which can be tested from...

    arxiv.org/abs/2605.22612 · PDF

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