Generative AI in Higher Education: Evidence Synthesis of Applied Learning Outcomes
DOI:
https://doi.org/10.65310/x5zzye23Keywords:
assessment, generative artificial intelligence, higher education, learning outcomes, evidence synthesisAbstract
Generative artificial intelligence has spread through higher education faster than institutions have produced stable evidence on learning effects. This study synthesized recent empirical reviews, meta-analyses, and applied higher-education research to identify where generative artificial intelligence improved learning and where outcomes remained contingent on instructional design. A structured evidence-synthesis method compared five quantitative or systematic anchor reviews with complementary studies on assessment, feedback, academic integrity, and learner regulation. The anchor literature ranged from 34 to 103 included studies, while a 2026 three-level meta-analysis reported an overall learning effect of g = 0.499 across 36 studies, 132 effect sizes, and 7,229 participants. Larger effects were reported for collaborative learning and cognitive or creative outcomes, while other reviews showed positive achievement and motivation effects but inconsistent self-efficacy findings. The synthesis concluded that generative artificial intelligence produced stronger outcomes when embedded in guided, task-specific pedagogy rather than used as an unrestricted answer generator. Assessment redesign, verification routines, and learner metacognition remained necessary implementation conditions
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Copyright (c) 2026 Muhammad Nazirul Mubin Bin Abdul Moen, Alfa Rohmatin, Mia Siti Nurazizah, Mehwish Kaleem (Author)
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