Title: AIMS – AI-based Mentoring for Women in STEAM Studies
Project duration: 2025-2028
Research Area: Responsible AI

Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Education and Culture Executive Agency (EACEA). Neither the European Union nor EACEA can be held responsible for them.

Project Description

AIMS promotes gender equality in higher education through AI-driven mentoring and STEAM-oriented methodologies, strengthening digital readiness and innovative teaching and learning to increase women’s participation and success in STEM. Addressing persistent barriers such as underrepresentation, imposter syndrome, limited access to mentoring, and gaps in mathematical and digital competencies, the project builds on a comprehensive needs analysis including literature review, focus groups and expert consultation. AIMS develops an AI Companion based on adaptive learning algorithms and explainable, ethical AI to provide transparent, bias-aware and personalized mentoring. By integrating arts, design thinking and ethics into STEM, the project establishes a holistic, pedagogically sound and ethically robust mentoring framework for inclusive STEAM education.

Aims

AIMS develops an AI-based mentoring system that provides individualized academic and socioemotional support for women in STEAM study programmes. The project:

• Enhances digital competencies and resilience in higher education,
• Promotes equal opportunities through gender-sensitive learning pathways,
• Integrates STEAM methodologies linking technology, creativity, and ethics,
• Establishes AI literacy and responsible AI use in higher education teaching.
• Strengthens democratic participation, critical judgment, and reflexive decision-making in
• digitally mediated learning environments.

Problem

Women in STEM continue to face structural barriers, including underrepresentation, gender bias, limited access to role models and mentoring, and insufficient personalized support, which negatively affect retention, confidence and academic progression. At the same time, higher education institutions lack scalable, ethical and AI-based mentoring solutions that combine digital transformation with STEAM-oriented, gender-sensitive learning and guidance frameworks.

Practical example

The AI Companion will be piloted with first-year female STEM students at partner universities, providing personalized study planning, competency development and career guidance. In addition, project results will be implemented through MOOCs and E-Courses integrated into institutional learning management systems and teacher training programmes, supporting AI-based mentoring, STEAM-oriented pedagogy and gender-sensitive teaching practices within regular higher education curricula.

Technology

AIMS develops an AI-based mentoring companion that combines a language-model-supported dialogue component for explanation and reflection with an adaptive recommendation component for personalized study planning and learning resource support. Personalization is based on a learner model constructed from course progress data, self-assessments, and user-consented profile information. The system is integrated into the learning management systems of the partner universities using established interoperability and authentication standards (e.g., SSO/LTI). To support transparent and responsible use, the AI Companion provides understandable rationales for its recommendations and allows for escalation to human support in situations of uncertainty. Data protection and ethical considerations are addressed through GDPR-compliant data minimization, EU-based data storage, audit logging, and ongoing monitoring for potential bias in mentoring and recommendation processes.

Outlook

AIMS establishes a scalable and sustainable model for AI-based, gender-sensitive mentoring in higher education. Its results will support the long-term integration of STEAM-oriented and ethical AI mentoring into institutional strategies, contribute to increased retention and success of women in STEM, and provide a foundation for further research on adaptive learning, explainable AI in education, and inclusive digital support systems across European universities.

Team

Lead

Team Members

External Team Members

• Mana-Teresa Donner, MSc MA
• Prof. Dr. Rudolf Egger (University of Graz)
• Carina Koch, BA (University of Graz)
• Res. Asst. PhD Gulizar Karahan Balya (METU)
• Prof. Dr. Halil Turan (METU)

Partners

• University of Graz, Austria
• Middle East Technical University, Turkey