First-year medical students want to effectively adapt to university studies, master the core medical disciplines, and achieve the expected learning outcomes. However, one of the main obstacles faced by students is related to the gaps between school and university learning models, differences in delivery methods, variations in prior preparation and study skills, and unbalanced course structure. 

The project aims to use modern EdTech approaches to systematically identify these gaps by analyzing the curricula of secondary schools and universities in selected countries, drawing up a spiral structure of medical programs, and validating the findings through surveys and interviews with students and teachers. Key activities include developing an AI-driven diagnostic system to assess individual STEM knowledge gaps, piloting personalized micro-modules with gamification elements, and monitoring student progress after filling knowledge gaps with additional personalized educational materials through AI analytics. 

These activities will create a supportive ecosystem for first-year medical students, facilitating the integration of diagnostic tools, adaptive modules, and curriculum alignment strategies to support learning and adaptation. 
Expected outcomes - Improved first-year adaptation, mastery of STEM concepts, actionable insights for faculty and curriculum committees, and a proven AI-powered learning platform. In addition, the project provides tangible tools to both identify educational gaps and address them through personalized, student-centered materials that can be used in a variety of educational settings or potentially commercialized as startup solutions.
 

Project Goal

Improved learning and mastery of STEM core knowledge in medical education, supported by an evidence-based ecosystem of personalized and AI-driven interventions, ensuring successful adaptation and integration of the first year into modern medical curricula.

Project Objectives

1. Analyze the curricula of at least 4 high schools and 2 universities (2023/24 or 2024/25 academic year) from selected countries whose residents are most frequently in the international medical student cohort in Georgia, in order to identify structural and content gaps that affect the readiness of international first-year medical students. This analysis will provide an evidence base for designing targeted interventions and an ecosystem of personalized support.
2. Develop a spiral and multidisciplinary structuring of the medical curriculum, visualizing connections between school-level subjects and university modules, 
3. Assess the balance of STEM and clinical content in BAU first-year courses and at least one different medical program by the 2024/25 academic year, identifying areas where insufficient STEM preparation may hinder the achievement of learning outcomes 
4. Develop and pilot an AI-driven diagnostic system on a student body (at least 50 international first-year students) to assess individual students’ STEM knowledge gaps in relation to national curriculum standards, enabling personalized intervention planning.
5. Monitor and track the progress of a cohort (50 students) of integrated biomedical courses throughout the second semester, using AI analytics to ensure mastery of key concepts and adaptively adjust learning paths for each student.
6. Develop a module and learning path to address identified gaps, promote engagement, and support effective skill acquisition, then pilot it in two seminar groups of 25 students, with control groups of the same size for comparison.
7. Develop evidence-based recommendations for an integrated student support ecosystem that includes curriculum alignment, diagnostic assessment, AI-based modules, gamification, and adaptive learning strategies to improve first-year engagement and long-term academic outcomes.
 

● A catalog of STEM curricula and learning outcomes from selected countries, including the identification of structural and content gaps that affect international first-year medical students.
● Reports of surveys and interviews with students and educators that confirm perceived gaps in STEM preparation and adaptation challenges.
● Visual maps of spiral medical curricula that connect school-level subjects to university modules.
● An evaluation report on the balance between STEM and clinical content in BAU first-year courses and at least one additional medical program.
● MVP AI-driven diagnostic chatbot to assess individual STEM knowledge gaps.
● MVP of personalized micromodules with adaptive learning paths and gamification elements that address the identified gaps.
● Boards and Analytical tools that track student progress, mastery of key concepts, and adaptive learning interventions.
● Pilot study reports, including cohort performance, usability, engagement, and feedback on AI-driven interventions. 
● Evidence-based recommendations for a scalable ecosystem of AI-powered student support modules.
● Dissemination materials: at least 2 conference abstracts, at least 2 scholarly articles, a seminar, a policy brief, and institutional communication content.
 

• Medical students. Personalized AI-driven interventions will help reduce the complexity of navigating a spiraling and multidisciplinary curriculum.

● University faculty. Access to data-driven information about student preparation and performance will help adapt teaching strategies and inform the allocation of instructional resources.
● Medical program directors, curriculum committees. Evidence-based recommendations to guide curriculum design decisions, balance STEM and clinical content, and ensure alignment with international standards.
● Educational researchers and assessment specialists. Access to structured datasets, validated metrics, and AI analytics to assess learning outcomes and inform curriculum design and intervention strategies.
● Other universities. Comparative data and scalable tools for implementation, facilitating the wider adoption of AI-enabled student support systems.
● Accreditation bodies. Empirical evidence and standardized reporting to support alignment with international medical education standards.
● Policymakers. Research on effective interventions, curriculum design, and AI-enabled learning to inform national education standards and funding priorities.