Assessing the Baseline Landscape of Generative Artificial Intelligence (AI) Integration: An Empirical Study among University Students

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• Semantic Scholar2026-07-16 23:31:52
  Assessing the Baseline Landscape of Generative Artificial Intelligence (AI) Integration: An Empirical Study among University Students

  This study investigated the extent of generative artificial intelligence (AI) integration among university students. Data were collected from a sample of 115 students at Agriculture College Garhwa, a constituent college of Birsa Agricultural University, Ranchi. An adapted, validated behavioural scale was utilised, with responses recorded on a 5-point Likert scale, on which a score of 3.000 represented strict theoretical neutrality. A standard one-sample t-test was conducted to evaluate whether the empirical mean differed significantly from this neutral midpoint. Descriptive statistics revealed a mean AI usage score of M = 3.852 (SD = 0.482), indicating an overall positive inclination towards technology adoption. Inferential analysis confirmed a highly significant positive deviation from the baseline, t(114) = 18.959, p < .001, supporting rejection of the null hypothesis. The low variance indicates broad agreement within the student cohort, suggesting that generative AI tools are shifting from novel forms of digital assistance to foundational elements of student workflows. The empirical findings indicate that university students have moved beyond tentative experimentation and have actively integrated generative AI frameworks into their primary educational practices. As student behaviour is rapidly outpacing traditional institutional structures, higher education policymakers must move beyond restrictive or neutral frameworks. Instead, academic institutions should proactively embed explicit AI literacy, ethical boundaries, and context-sensitive digital guidance in curriculum design to support critical thinking.