The 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. 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. In 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, R 2 = 0.642, F = 72.718, p-value < 0.001). Our 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.
Sami et al. (Wed,) studied this question.