Abstract
Today’s all-pervasive emergence of artificial intelligence (AI) necessitates a comprehensive evaluation of its role in education, especially for occupational disciplines in health sciences and STEM. AI has reshaped student learning and instructors’ navigation of the evolving, post coronavirus infectious disease (COVID-19) global academic landscape. This study aimed at encapsulating evidence-based AI implications for student-centered, active learning and assessment at various curricular levels of science, technology, engineering, mathematics (STEM) and health sciences education in developed versus underprivileged or less developed countries. An extensive, scoping review of 65 recent, scholarly publications was conducted over six months through diverse search engines (PubMed, Google Scholar, and library databases) using key terms such as “Active learning with AI” and “Assessment via AI in developing countries”. Records were maintained on a collaborative Google Doc, followed by weekly mentor-mentee meetings to structure the analysis. Findings show that, impact of AI in education is multifaceted. At K-12, gamified platforms and adaptive tutoring tools like Khanmigo increase engagement and facilitate personalized feedback. In undergraduate curricula, AI-powered laboratories enhance understanding, offering a safe environment to learn and receive real-time feedback. In graduate and postgraduate training, predictive analytics help monitor student progress, while natural language processing platforms support assessment of complex, open-ended responses. Furthermore, AI-enhanced grading systems streamline workflows, with feedback tools providing curated information to better reflect student weaknesses. Despite these benefits, challenges of AI usage include disparities in global access, risk of bias when training the model, and the need for constant instructor supervision. This comparative study highlights that AI should complement but not replace educators, and the importance of its culturally responsive, ethical application.
Recommended Citation
Munlapudi, Vinay and De, Santanu
(2026)
"Comparative Analysis of Artificial Intelligence (AI) Implications for Active Learning and Assessment at Various Curricular Levels of STEM and Health Sciences in Developed versus Underprivileged Countries,"
FDLA Journal: Vol. 10, Article 8.
Available at:
https://nsuworks.nova.edu/fdla-journal/vol10/iss1/8
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