PlagiAct: Measurement Instrument of STEM Student Attitudes Toward Academic Dishonesty in the AI Era
Cristina Cachero, José María Conejero, Yania Crespo, Julio Alberto López-Gómez, Carolina Lorenzo, Sergio Luján-Mora, Santiago Meliá, José Antonio Parejo Maestre, Juan Ramón Rico Juan, Roberto Rodríguez Echeverría, Arancha Simón-Hurtado, Miguel A. Teruel
IEEE Transactions on Education (IEEE TOE), Early access, p. 1-10, 2026. ISSN: 0018-9359. https://doi.org/10.1109/TE.2026.3694297
(IEEETOE'26)
Revista / Journal
JCR IF (2025): 2.5 - Education, Scientific Disciplines: 28/89 (Q2); Engineering, Electrical & Electronic: 204/369 (Q3)
Resumen
Contribution: This study presents PlagiAct-V3, an empirically explored and stabilized instrument to assess science, technology, engineering, and mathematics (STEM) students’ attitudes toward plagiarism in the era of generative AI. Background: The rapid rise of tools like ChatGPT has reshaped academic dishonesty. Existing instruments on plagiarism and generative AI (GenAI) lack a robust underlying theoretical basis, clear multidimensional structures, and comprehensive psychometric validation, especially in STEM and European contexts. PlagiAct addresses this gap by integrating four complementary theories, adding GenAI-specific items, and exploring a multidimensional structure with 725 STEM students. Research Questions: Which factors influence STEM students’ attitudes toward plagiarism in digital contexts? What is the factorial structure of plagiarism attitudes? Methodology: PlagiAct-V2 was developed through a literature review, expert content validation, and pretesting, and was administered to 725 engineering students from five Spanish universities. Internal consistency was examined using Cronbach’s alpha and McDonald’s omega, while exploratory factor analysis was used to examine the structure of the attitude scales, yielding eight empirically derived dimensions. These analyses led to the final PlagiAct-V3 instrument. Main limitations include convenience sampling, potential selection bias, and the need for periodic revalidation as GenAI practices evolve. Findings: Results provide evidence of satisfactory internal consistency and exploratory structural validity. The findings highlight the multidimensional nature of plagiarism attitudes in the digital age and offer an empirically grounded basis for designing targeted interventions to promote academic integrity.