Strategic integration of artificial intelligence in education: enhancing learning through innovative teaching practices
DOI:
https://doi.org/10.71068/gp000036Keywords:
generative artificial intelligence, teaching innovation, self-regulation, metacognition, authentic assessmentAbstract
The integration of artificial intelligence (AI) into higher education requires moving beyond the occasional use of tools and linking them to learning objectives, activities, assessment, self-regulation and academic supervision. The aim was to analyse, within the sample studied, the relationship between an intervention based on the responsible pedagogical use of AI and learning outcomes. A quasi-experimental pre-test–post-test design with a comparison group was employed, comprising 240 records, distributed as 120 cases per group. The instrument included 12 Likert-scale items; internal consistency, descriptive statistics, Welch’s t-test, Cohen’s d and pre-test-adjusted linear regression were estimated. The intervention group achieved a post-test score of 70.35 (SD = 10.05), compared with 60.49 (SD = 10.25) in the comparison group; t = 7.521, p < 0.001, d = 0.971. Adjusting for the pre-test, the estimated difference was 7.53 points (95% CI [6.29, 8.76], p < 0.001), with α = 0.894. The results point to a favourable outcome for pedagogically structured integration, the interpretation of which must take into account the quasi-experimental design and its limitations. It is concluded that responsible adoption requires AI literacy, authentic tasks, redesigned assessment, content verification, data protection and follow-up studies with a broader scope and longer-term monitoring.
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