人类与AI生成的测试：潜在特质评估中的维度和信息准确性

Computer Science > Human-Computer Interaction arXiv:2510.24739 (cs) [Submitted on 15 Oct 2025 (v1), last revised 24 Sep 2026 (this version, v4)] Title:Human- vs. AI-generated tests: dimensionality and information accuracy in latent trait evaluation Authors:Mario Angelelli, Morena Oliva, Serena Arima, Enrico Ciavolino View a PDF of the paper titled Human- vs. AI-generated tests: dimensionality and information accuracy in latent trait evaluation, by Mario Angelelli and 3 other authors View PDF HTML (experimental) Abstract:Artificial Intelligence (AI) and large language models (LLMs) are increasingly used in social and psychological research. Among potential applications, LLMs can be used to generate, customise, or adapt measurement instruments. This study presents a preliminary investigation of AI-generated questionnaires by comparing two ChatGPT-based adaptations of the Body Awareness Questionnaire (BAQ) with the validated human-developed version. The AI instruments were designed with different levels of explicitness in content and instructions on construct facets, and their psychometric properties were assessed using a Bayesian Graded Response Model. Results show that although surface wording between AI and original items was similar, differences emerged in dimensionality and in the distribution of item and test information across latent traits. These findings illustrate the importance of applying statistical measures of accuracy to ensure the validity and interpretability of AI-driven tools. Comments: Subjects: Human-Computer Interaction (cs.HC); Information Theory (cs.IT); Methodology (stat.ME) MSC classes: 62Bxx, 62F15, 62P15 Cite as: arXiv:2510.24739 [cs.HC] (or arXiv:2510.24739v4 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2510.24739 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Mario Angelelli [view email] [v1] Wed, 15 Oct 2025 09:20:35 UTC (10,226 KB) [v2] Tue, 30 Dec 2025 15:56:34 UTC (10,244 KB) [v3] Thu, 12 Feb 2026 19:44:04 UTC (10,244 KB) [v4] Thu, 24 Sep 2026 12:18:29 UTC (10,244 KB) Full-text links: Access Paper: View a PDF of the paper titled Human- vs. AI-generated tests: dimensionality and information accuracy in latent trait evaluation, by Mario Angelelli and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.HC new | recent | 2025-10 Change to browse by: cs cs.IT math math.IT stat stat.ME References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)