cs.CY · 2026-07-14 · No. 53

Computers and Society, 2026-07-14.

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

    Programming Language Policy as an AI Literacy Equity Problem: A 15-Nation Comparative Analysis

    Adrian-Marius Dumitran, Iulia-Maria Popescu

    cs.CY · cs.AI

    The promise of AI literacy ``for all'' confronts a structural challenge embedded in how nations organise secondary computer science education. In most systems, a general-track subject -- Digital Literacy, ICT, TIC, or SNT -- bears the weight of universal AI literacy, while a specialist Informatics course serves STEM pathways separately. Yet the content and depth of the general track are shaped by governance decisions made largely with...

    arxiv.org/abs/2607.11314 · PDF

  2. 02

    The Paternalistic Filter: Epistemic Injustice and Differential Refusal in LLM-Mediated History Education for Marginalized Romanian Students

    Alexis Popovici, Andrei Ionascu, Adrian-Marius Dumitran

    cs.CY · cs.AI · cs.CL

    As Large Language Models (LLMs) are increasingly deployed as conversational tutors, they risk institutionalizing systemic inequalities. This study presents a systematic API audit of four LLMs acting as history tutors, evaluating 1,800 responses regarding the 1989 Romanian Revolution across five student personas varying by ethnicity and socio-economic tier. We uncover four interconnected patterns of \emph{epistemic paternalism}:...

    arxiv.org/abs/2607.11292 · PDF

  3. 03

    DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs

    Anqi Li, Jie Zhang, Zhongqi Wang, Songkai Xue, Jiahao Wang, Shiguang Shan, Xilin Chen

    cs.CY · cs.AI

    While Large Vision-Language Models (LVLMs) demonstrate remarkable capabilities, they remain highly susceptible to embedded social biases. Existing bias evaluation protocols predominantly rely on static datasets, which provide only a superficial assessment, as their fixed test cases cannot adaptively evolve to measure the true depth and limits of model vulnerabilities. We introduce DeepBias, an adaptive framework for the in-depth probing of...

    arxiv.org/abs/2607.11228 · PDF

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