ArticleBMC medical education2026
The AI-mediated metamorphosis of contemporary educational landscape: a multi-modal investigation into the impact of AI-augmented learning on academic outcomes.
Article in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
5 authors.
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Abstract
backgroundThe advent of AI has been revolutionary in multitude of industries. Due to its immense potential AI holds promise for improved quality of education. This study aims to empirically assess the impact of AI-based learning on academic outcomes and explore complex factors such as user competencies, perception, challenges, and ethical concerns that impact adoptability of AI-based learning models in higher education. METHODOLOGY: A quasi-experimental study was conducted among undergraduate medical students at institute over 4 weeks. After the experiment, a research instrument was utilised to gain insights into determinants shaping the adoptability of AI-based learning in higher education. Focus group discussions with supervising faculty members were conducted to gain expert opinion on AI-based learning in higher education.
resultsIn our study, AI-based learning (68.70%±12.40) improved academic outcomes among study participants compared to traditional-resources based learning (62.84%±17) (p-value < 0.001). User competencies (Spearman’s rho (ρ) = 0.616, p-value < 0.001) and user perception (ρ = 0.625, p-value < 0.001) significantly improved the adoptability of AI-based learning among students. In contrast, user challenges (ρ=-0.075, p-value = 0.336) hindered the adoptability of AI in higher education. A degree of ethical dissonance was seen among study participants with students being aware of ethical dilemmas posed by AI but still willing to adopt and use it (ρ = 0.013, p-value = 0.872). Additionally, our prediction model based on regression analysis explained 64.2% of variances in adoptability of AI and was statistically significant (R = 0.801, R2 = 0.642, F = 72.718, p-value < 0.001).
conclusionOur study demonstrated that AI-based learning can enhance short-term academic outcomes and can AI-based tools can effective in improving the quality of education in higher education.
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