Abstract
This research presents a meta-analysis of 25 studies examining artificial intelligence (AI) applications in STEM higher education from 2015-2025. The analysis identifies effective AI teaching methods that prepare students for modern technology careers and support economic growth through improved STEM education outcomes. The findings reveal three main AI teaching approaches used successfully across universities: intelligent tutoring systems that provide personalized feedback, automated grading tools, delivering instant assessment results, and adaptive learning platforms that adjust content difficulty based on student progress. These AI tools help students learn more effectively while preparing them for technology-focused careers. Analysis shows that students using AI-enhanced learning demonstrate improved academic performance, better understanding of complex concepts, and increased confidence in pursuing STEM careers. Universities implementing these AI approaches report stronger connections with technology companies and better job placement rates for graduates. The research identifies key factors for successful AI implementation in STEM programs: adequate technical support, faculty training on new technologies, and integration with existing courses. Institutions that follow these practices see measurable improvements in student learning outcomes and graduate career readiness. These findings support recent government initiatives to strengthen STEM workforce development, including federal legislation aimed at increasing technology talent. The meta-analysis demonstrates how AI-enhanced education can address skills shortages in growing fields like computer science, engineering, and data analysis while contributing to economic competitiveness. This study provides practical guidance for educators and administrators seeking to improve STEM education through AI technology.
Presenters
Maria BurnsDirector, Technology Leadership & Innovation Management, Information Science Technology, University of Houston, Texas, United States
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
Designing Social Transformations
KEYWORDS
Meta-Analysis, AI, Education, STEM, Workforce, Technology
