Relationships among AI Literacy, Critical Evaluation of AI-Generated Information, and Academic Integrity among Higher Education Students: A Cross-Sectional Study with Implications for Assessment Practice
DOI:
https://doi.org/10.65327/es.v12i2.2539Keywords:
AI literacy, critical evaluation, academic integrity, generative AI, higher educationAbstract
The integration of generative AI in higher education has sparked learning opportunities as well as concerns over students' ability to assess information generated by AI and ensure academic honesty. This study explored the relationships between the three measures, AI literacy, critical evaluation, and academic integrity, by analyzing the data from a quantitative, cross-sectional, openly available dataset of 972 students enrolled in higher education and 60 seven-point Likert items. The four domains of affective, behavioral, cognitive, and ethical dimensions of AI literacy were measured; critical evaluation was measured by the items of justification and verification; and academic integrity was reverse-scored as academic dishonesty. Descriptive statistics, Cronbach's alpha, Pearson and Spearman correlations, Welch's tests, partial correlations and sensitivity analysis were performed. The relationships were strong and positive between AI literacy and critical evaluation (r=.749, p<.001) and weak but positive between AI literacy and academic integrity (r=.171, p<.001). The relationship of ethical AI literacy towards academic dishonesty was the strongest negative one, and institutional difference was also found to be significant. The results suggest a stronger link between AI literacy with the critical evaluation than with integrity-related behaviour. The incorporation of verification, ethical considerations, and disclosure of the use of AI tools into the curriculum and policy of universities is therefore necessary.
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