STEM College Readiness and Cross-Cultural AI Support for International Students: A Systematic Review of Non-Developing Country Assessment Systems

This systematic review protocol establishes a rigorous methodological framework for investigating how developed nations assess STEM college readiness and how artificial intelligence tools can support international students navigating these diverse assessment systems. The review addresses a critical gap in educational research at the intersection of international student mobility, STEM education, and emerging AI technologies. SCOPE AND PURPOSE The investigation focuses on five exemplar nations with distinct approaches to STEM education—the United States, Germany, Japan, South Korea, and France—examining how their assessment frameworks impact international students' academic transitions. The review will synthesize empirical research from 2010-2025 to identify effective AI-supported interventions that address specific barriers faced by international STEM students, including academic readiness gaps, language challenges, and cultural adjustment issues. THEORETICAL FOUNDATIONS The protocol employs three complementary theoretical frameworks that provide a multidimensional lens for analysis: 1. Cultural-Historical Activity Theory (CHAT) examines how international students navigate educational systems embedded within broader cultural and institutional contexts, particularly how they interact with assessment structures that may not align with their prior learning experiences. 2. Self-Determination Theory (SDT) serves as a framework for evaluating how AI tools might foster autonomy, competence, and relatedness for international students, distinguishing between technologies that promote meaningful engagement versus those that reinforce passive compliance. 3. Cognitive Load Theory (CLT) addresses how AI interventions can reduce the additional cognitive burden international students face when adapting to unfamiliar academic systems, including decoding assessment formats and translating specialized STEM terminology. METHODOLOGICAL RIGOR The protocol adheres to PRISMA 2020 guidelines and incorporates multiple quality assurance mechanisms: A transparent three-stage screening process with dual independent review Blinded screening using the Rayyan web application Custom Python scripts to assist with screening efficiency and data extraction Comprehensive documentation of inclusion/exclusion decisions Standardized data extraction tools aligned with the theoretical framework Quality assessment using validated instruments (CASP for qualitative studies, MQQ for quantitative/mixed methods) A tailored evidence certainty framework with explicit criteria for four confidence levels SYNTHESIS APPROACH The review will integrate findings through a multi-method synthesis strategy: Thematic synthesis for analyzing national readiness frameworks and cultural dimensions Narrative synthesis for evaluating AI tool implementation and effectiveness Descriptive matrices to map key features across educational systems and technologies Visual representations including heatmaps and comparative matrices to highlight patterns and gaps The synthesis will balance analytical depth with practical applications, generating both theoretical insights and actionable recommendations for different stakeholders. IMPLEMENTATION TIMELINE The protocol outlines a seven-month implementation plan: - May 2025: Protocol registration with OSF - June-July 2025: Literature search and screening - August-September 2025: Data extraction and synthesis - October-November 2025: Manuscript preparation - December 2025: Submission for peer review ETHICAL AND TECHNOLOGICAL CONSIDERATIONS The protocol explicitly addresses the role of technology in the review process itself, detailing how AI writing tools assisted in protocol development while establishing clear boundaries for human oversight in analytical phases. Custom Python scripts will enhance efficiency in pre-screening, data extraction, and analysis preparation, with all code undergoing peer review and being made available as supplementary material to ensure reproducibility. EXPECTED CONTRIBUTIONS This systematic review aims to advance both theoretical understanding and practical applications in three domains: 1. Educational Policy: Providing evidence-based recommendations for higher education institutions seeking to improve international student outcomes 2. Technological Design: Identifying principles for developing culturally responsive AI tools that address specific transition challenges International 3. Education Practice: Highlighting promising technology-enhanced support systems and examining equity considerations related to AI implementation By synthesizing evidence across national contexts, the review will facilitate knowledge transfer between educational systems that have traditionally operated in isolation, potentially catalyzing innovative approaches to supporting international student transitions in an increasingly globalized STEM education landscape.