Synopsis
This study introduces the adaptation and validation of two psychometric instruments (i.e., the Perceived Agency Scale and UTAUT2) within the Chilean higher education context. The goal is to investigate how students utilize Generative Artificial Intelligence (GenAI). The study provides a foundation for an explanatory-predictive model to understand the individual, social, and technological factors influencing academic integrity in the "post-plagiarism" era.
Keywords
Generative Artificial Intelligence; Academic Integrity; Psychometric Validation; Higher Education
Introduction
The massive emergence of GenAI, led by tools such as ChatGPT, has provoked a paradigm shift in higher education. The traditional boundaries of human authorship have become increasingly blurred, placing significant strain on conventional perspectives of academic integrity (Xia et al., 2024). According to Eaton (2023), we have entered what is termed the post-plagiarism era. This shift requires institutions to move beyond simply detecting fraud. Instead, they must understand the ethical criteria students use when engaging with these technologies.
While research in this field is growing, most current studies are descriptive (Avello & Zurita, 2025). They focus on basic usage patterns rather than deep causes. There is a need for theoretical models that provide strong empirical evidence to guide educational policy and training. Such evidence is also essential for informing assessment designs and training programs with both national relevance and global impact.
This project addresses this gap by constructing an explanatory-predictive model that articulates three complementary dimensions: a) individual: analyzing variables such as moral disengagement and moral unacceptability (Urdan & Kaplan, 2020; van Zyl et al., 2022); b) social: evaluating the culture of misconduct, social norms and peer observation (Perry et al., 2025; Zhao et al., 2022); and c) technological: focusing on acceptance, use and the perception of agency attributed to GenAI (Mohd Rahim et al., 2022).
While the broader objective of this study is to evaluate an integrated model of these factors to predict (dis)honest use of GenAI, this specific study focuses on the foundational phase of the adaptation and validation of instruments: the Perceived Agency Scale and the Unified Theory of Acceptance and Use of Technology within the Chilean higher education context.
Methodology
This quantitative study uses an explanatory-predictive design. It begins with instrumental research (Venkatesh et al., 2012) centered on the adaptation and psychometric evaluation of two primary scales: the Perceived Agency Scale and the Unified Theory of Acceptance and Use of Technology (UTAUT2).
The Perceived Agency Scale (Trafton et al., 2023) is a unidimensional instrument that assesses the degree to which individuals perceive a non-human entity (such as GenAI) as acting based on internal thoughts and feelings rather than merely responding to external factors. The short version consists of eight items rated on a 5-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree). Its original English version applied to GenAI has demonstrated high reliability levels (α = 0.90).
The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) (Mohd Rahim et al., 2022; Kerlinger & Lee, 2002) focuses on several key dimensions: a) performance expectancy: perception that technology improves performance; b) effort expectancy: perceived ease of use; c) habits: level of automation or acquired custom; d) perceived trust: the degree of trust in the technology; e) behavioral intention: the conscious intent to continue using the technology; and f) usage behavior: the actual level of system use. All items are answered on a 7-point Likert scale (1 = Strongly Disagree to 7 = Strongly Agree), with reliability levels in English ranging from α = 0.78 to 0.93.
Validation Process
To ensure validity and reliability, a rigorous cultural adaptation and translation process was conducted. Instruments underwent a translation and back-translation process by bilingual experts who understand the specificities of Chilean student culture. For content validity, the questionnaires were administered to a pilot sample of at least 20 individuals from the target population to verify that instructions and items were correctly understood. Structural validity was evaluated through independent Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) for each instrument. The goal was to examine dimensionality and confirm if empirical item grouping aligns with the proposed theoretical structure.
For the EFA, factor retention criteria included eigenvalue analysis, scree plots, and theoretical interpretability. Item factor loadings were analyzed, retaining those with acceptable saturations (> 0.40) that contributed conceptually to the construct (Lloret-Segura et al., 2014; Ferrando et al., 2022; Kline, 2023). For the CFA, goodness-of-fit measures included the Chi-square/degrees of freedom ratio (χ²/df), the Comparative Fit Index (CFI), the Tucker-Lewis Index (TLI), the Root Mean Square Error of Approximation (RMSEA), and the Standardized Root Mean Square Residual (SRMR) (Kline, 2023).
Sample and Power Analysis
This phase employed non-probabilistic quota sampling (Salkind, 2022), maintaining proportions by field of study, years of study, and gender, based on national data from the Chilean Ministry of Education (MINEDUC). The sample size was estimated through an a priori power analysis using the semPower package in R (Kalkbrenner, 2021). Based on the UTAUT2 instrument (19 items, 5 factors) and considering a significance level of α = 0.05 and a power of (1−β) = 0.95, the analysis indicated a requirement of two samples of at least N = 346 participants each.
Preliminary Reflections
This project addresses an urgent global issue. In Chile, official policies regarding GenAI are still in the early stages. Creating validated tools in Spanish is a major contribution, as there is currently a shortage of reliable instruments for academic integrity research in Spanish-speaking countries.
By providing these instruments, the study facilitates an empirical discussion on the factors that inhibit or facilitate GenAI honest and dishonest uses. Ultimately, this research aims to move beyond descriptive data, providing a scientific foundation for developing interventions and training that promote a culture of integrity in the face of rapid technological evolution.
References
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