10.7910/DVN/OV2WAM Milani, Alfredo Alfredo Milani https://orcid.org/0000-0003-4534-1805 Link Campus University Harvard Dataverse 2025 Computer and Information Science Social Sciences Large Language Models Prompt Injection Education Sciences Education Evaluation Trustworthy AI Human-in-the-Loop AI Milani, Alfredo Alfredo Milani Link Campus University, Rome, Italy Valentina Franzoni Valentina Franzoni University of Perugia, Italy Florindi Emanuele University of Modena-Reggio Emilia 2025-09-12 2025-09-12 2227-7102 4804924 application/zip 1.0 Creative Commons CC0 1.0 Universal Public Domain Dedication. The dataset contains the output of experiments on a research project on Vulnerability of LLMs in Educational Assessment. The Dataset contains: -the students assignments data in normal form and the injected form -the output produced by the experimented LLMs: ChatGPT, Gemini, DeepSeek, Grok, Perplexity and Copilot for the experiments evaluation the assignments, as a single document and collectively as a group of documents, denominated: -User Legitimate LLMs Prompts -Normal (no injection) providing the reference base evaluation -Prompt Injection Pass, one type of injection experiments, called Fail-To-Top, to move an assignment evailuated FAIL by reference base evaluation to PASS, i.e. above 35% of total points. -Prompt Injection to Top25 , a type of injection experiments to move to top 25% an assignment with lowe reference base evaluation . This latter type of experiment come in 3 versions, Fail-To-Top, Sat-To-Top, Good-To-Top where assignment with reference base evaluation respectively: Fail (below 35%), Satisfactory (greater than 25% and belo 50%) and Good (above 50% and below 75%) are considered for injection. The name of the folders and output results files are accordingly self-explanatory .