@inproceedings{benedetto-etal-2026-beyond,
    title = "Beyond Names: How Grammatical Gender Markers Bias {LLM}-based Educational Recommendations",
    author = "Benedetto, Luca  and
      Donvito, Antonia  and
      Lucchetti, Alberto  and
      Cappelli, Andrea  and
      Buttery, Paula",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.264/",
    pages = "5648--5668",
    ISBN = "979-8-89176-380-7",
    abstract = "This paper investigates gender biases exhibited by LLM-based virtual assistants when providing educational recommendations, focusing on minimal gender indicators. Experimenting on Italian, a language with grammatical gender, we demonstrate that simply changing noun and adjective endings (e.g., from masculine ``-o'' to feminine ``-a'') significantly shifts recommendations. More specifically, we find that LLMs i) recommend STEM disciplines less for prompts with feminine grammatical gender and ii) narrow down the set of disciplines recommended to prompts with masculine grammatical gender; these effects persist across multiple commercial LLMs (from OpenAI, Anthropic, and Google). We show that grammatical gender cues alone trigger substantial distributional shifts in educational recommendations, and up to 76{\%} of the bias exhibited when using prompts with proper names is already present with grammatical gender markers alone.Our findings highlight the need for robust bias evaluation and mitigation strategies before deploying LLM-based virtual assistants in student-facing contexts and the risks of using general purpose LLMs for educational applications, especially in languages with grammatical gender."
}