Educational innovation through generative artificial intelligence: benefits, ethical challenges, and institutional conditions for its adoption
DOI:
https://doi.org/10.62697/rmiie.v5i4.439Keywords:
Generative artificial intelligence, educational innovation, ethics, higher education, technology adoption, institutional governanceAbstract
The integration of generative artificial intelligence (GenAI) into higher education has shifted the debate from technological availability toward pedagogical quality, ethical responsibility, and institutional capacity to guide its use. This article analyzed the relationships among perceived benefits, ethical dilemmas, institutional conditions, and responsible adoption of GenAI through an exploratory quantitative model. A structured methodological dataset of 390 cases was used, evenly distributed across undergraduate students, master’s students, and faculty members, with 24 Likert items organized into four constructs. Internal consistency ranged from α = .753 to α = .805. Exploratory factor analysis showed strong sampling adequacy (KMO = .881) and a significant Bartlett test (χ² = 2418.76; df = 276; p < .001), yielding four factors consistent with the conceptual design. A path model based on composite scores explained 33.3% of the variance in responsible adoption. Perceived benefits (β = .381; p < .001) and institutional conditions (β = .356; p < .001) were positively associated with adoption, whereas ethical dilemmas showed a negative adjusted association after controlling for the other predictors (β = -.177; p < .001). Findings are interpreted as a methodological test of the model rather than population estimates. The analysis suggests that educational innovation with GenAI requires a combination of pedagogical usefulness, critical AI literacy, transparency rules, and institutional support before intensive adoption is encouraged.
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