AI in the Architecture, Engineering and Construction (AEC) Design
Project Description
The architecture, engineering, and construction (AEC) design faces multiple challenges as the industry undergoes digitalization transformation. Engineers point to productivity and performance as key areas for improvement. Moreover, the industry is adopting new regulations, increasing the overall complexity of the AEC design phase. As modern design instruments produce large amounts of data, an opportunity for leveraging Artificial Intelligence (AI) to address these challenges arises. The goal of my research is to scrutinize the adoption of AI in the AEC design and explore opportunities for apply in state-of-the-art AI solutions in the AEC design application areas
Participating Institutions
Beyond Correlations: Leveraging Generative AI for Causal Machine Learning
Project Description
This project seeks to address the significant limitations of deep learning (DL) models in causal explainability and robustness in out-of-distribution contexts by exploring the integration of Generative AI (GenAI) into Causal Machine Learning (CML). The project focuses on leveraging GenAI to identify highlevel, causally relevant variables and formulate causal hypotheses, addressing challenges that currently require expert intervention or costly experimental procedures. Through a series of targeted work packages, the project will develop and evaluate a scalable CML pipeline incorporating GenAI, with applications in diverse domains such as healthcare, policy-making, and finance. The anticipated outcomes include ad-vancements in causal inference methodologies and contributions to both academic literature and practical applications.
Relevance to Liechtenstein
This project aligns with the University of Liechtenstein's research strategy by addressing critical and increasingly relevant challenges in the fields of data science and artificial intelligence, specifically focusing on causal explainability and model robustness. By leveraging the emerging capabilities of Generative AI, the project aims to contribute to these fields while addressing real-world problems in high-impact sectors such as healthcare, policy-making, and finance. For Liechtenstein, in particular, this research holds significant relevance as it supports the country's growing emphasis on fostering innovation in AI-driven industries. Furthermore, the project will actively engage with the local community through workshops, facilitating academic exchange, and strengthening regional industrial collaborations. This approach not only advances cutting-edge research but also ensures that the outcomes bring tangible benefits to both the academic community and the local economy, aligning with the university's mission to drive forward research that has practical, localized impact.
Scientific, Economic and Societal Impact
This project addresses key limitations of current deep learning architectures, particularly in achieving causal explainability and maintaining robustness in out-of-distribution scenarios, i.e., contexts where the data distribution differs from that on which a model was originally trained. By integrating Generative AI (GenAI) into a scalable Causal Machine Learning (CML) pipeline, this research aims to automate the identification of causally relevant variables and the formulation of causal hypotheses, which currently rely on costly experiments or expert intervention, while preserving the predictive accuracy of current deep learning architectures.
This approach has the potential to enhance not only the interpretability of AI models but also their adaptability across diverse application areas. The project is particularly impactful in high-stakes fields such as healthcare, policy-making, and finance, where reliable, causally informed AI can support sound decision-making, optimize interventions, and increase trust in AI-driven outcomes. For example, in healthcare, this approach could help clinicians better understand why certain treatments are more effective for specific patient groups, supporting more personalized and effective medical care. In finance, this approach could refine risk assessment processes by identifying causal factors that drive market behavior, contributing to enhanced financial stability. In public policy, such a framework could elucidate the causal impact of interventions, like the effects of new educational policies, enabling policymakers to make evidence-based decisions that benefit society.
This approach has the potential to enhance not only the interpretability of AI models but also their adaptability across diverse application areas. The project is particularly impactful in high-stakes fields such as healthcare, policy-making, and finance, where reliable, causally informed AI can support sound decision-making, optimize interventions, and increase trust in AI-driven outcomes. For example, in healthcare, this approach could help clinicians better understand why certain treatments are more effective for specific patient groups, supporting more personalized and effective medical care. In finance, this approach could refine risk assessment processes by identifying causal factors that drive market behavior, contributing to enhanced financial stability. In public policy, such a framework could elucidate the causal impact of interventions, like the effects of new educational policies, enabling policymakers to make evidence-based decisions that benefit society.
Participating Institutions
Investment Management Game
Project Description
In diesem Projekt möchten wir gerne, gemeinsam mit unseren Partnern ein kompetitives Investment Management Spiel (IMAG) entwickeln, in welchem Teams von Studierenden (aber auch Schüler*innen, Erwachsene und Investment-Profis) gegeneinander antreten um ein Klientenportfolio unter Nachhaltigkeitsaspekten Performance-optimal anzulegen. Unsere Motivation dabei ist, dass wir derzeit im BSc BWL sowie im MSc Finance ein veraltetes Portfolioplanspiel spielen, welches modernen Anforderungen wie z.B. der nachhaltigen Kapitalanlage in keiner Weise gerecht wird und wir dieses gerne ersetzen möchten, kein Anbieter jedoch ein entsprechendes Produkt auf dem Markt hat. Der Grund dafür ist die fehlende Expertise zur realistischen Simulation komplexer Marktszenarien, für welche das Projektteam eine langjährige Expertise hat. Erkundigungen in unserem Netzwerk haben ergeben, dass andere Universitäten IMAG nach dessen Fertigstellung sehr gerne im Rahmen ihrer Finance-Programme implementieren würden.In diesem Projektes haben wir vier Arbeitspakete definiert:
1) Entwicklung des Marktmodells und aller theoretischen Aspekte des Spiels (Marktszenarien, Klientenprofile, Evaluationskriterien, Entscheidungsvariablen)
2) Kalibrierung an echte Marktdaten, prototypische Implementierung (für Alpha-test), User-Interface und Server-Implementierung (für Betatest)
3) Erstellung von Trainingsvideos und Handbüchern sowie einer Mini-Vorlesungsreihe für https://courseware.uni.li
4) Breit aufgestellte Disseminationsstrategie mit (i) Launch Event in Kooperation mit Partnern am Finanzplatz, (ii) Promotionstournee zu einer Vielzahl verschiedener Lehrinstitutionen (Schulen mit Wirtschaftszweig, Universitäten, Volkshochschulen und Finanzdienstleistern) in mindestens 5 verschiedenen europäischen Ländern und (iii) eine rundenbasierte internationale IMAG-Meisterschaft in Kooperation mit dem Cesim Elite-Programm unseres Projektpartners.
Im Rahmen der Erasmus+ Charta möchten wir uns gerne in diesem Projekt den folgenden Prioritäten widmen:
1) Unterstützung des EU Digital Education Acts zur Bereitstellung Digitaler Ressourcen für den Lehrbetrieb auf allen Ebenen
2) Stärkung von Kompetenzen der allgemeinen europäischen Bevölkerung (Financial Literacy)
3) Unterstützung des Kampfs gegen den Klimawandel (EU Green Deal) durch die Einbeziehung von Nachhaltigkeitsaspekten bei der Vermögensanlage
1) Entwicklung des Marktmodells und aller theoretischen Aspekte des Spiels (Marktszenarien, Klientenprofile, Evaluationskriterien, Entscheidungsvariablen)
2) Kalibrierung an echte Marktdaten, prototypische Implementierung (für Alpha-test), User-Interface und Server-Implementierung (für Betatest)
3) Erstellung von Trainingsvideos und Handbüchern sowie einer Mini-Vorlesungsreihe für https://courseware.uni.li
4) Breit aufgestellte Disseminationsstrategie mit (i) Launch Event in Kooperation mit Partnern am Finanzplatz, (ii) Promotionstournee zu einer Vielzahl verschiedener Lehrinstitutionen (Schulen mit Wirtschaftszweig, Universitäten, Volkshochschulen und Finanzdienstleistern) in mindestens 5 verschiedenen europäischen Ländern und (iii) eine rundenbasierte internationale IMAG-Meisterschaft in Kooperation mit dem Cesim Elite-Programm unseres Projektpartners.
Im Rahmen der Erasmus+ Charta möchten wir uns gerne in diesem Projekt den folgenden Prioritäten widmen:
1) Unterstützung des EU Digital Education Acts zur Bereitstellung Digitaler Ressourcen für den Lehrbetrieb auf allen Ebenen
2) Stärkung von Kompetenzen der allgemeinen europäischen Bevölkerung (Financial Literacy)
3) Unterstützung des Kampfs gegen den Klimawandel (EU Green Deal) durch die Einbeziehung von Nachhaltigkeitsaspekten bei der Vermögensanlage
International Institutions in National Criminal Law Enforcement in Germany and Liechtenstein in the Context of Combating Financial Crime
Project Description
The dissertation examines international organizations in the field of financial crime. These organizations influence national criminal law enforcement and actively set standards in combating financial crime. The dissertation explores the background and critically questions the actions of these institutions and the effectiveness of the regulations issued.
Participating Institutions
Higher Education AI Resources & Teaching
Project Description
This project transforms how generative artificial intelligence (GenAI) supports authentic learning in higher education, emphasizing critical thinking and meaningful real-world tasks. As GenAI adoption accelerates, many educators, particularly those in non-technical disciplines, require more explicit guidance on integrating these tools effectively into their teaching. Using Design Science Research methodology, we address key challenges in GenAI implementation by consulting with educators to identify adoption barriers. These insights inform the development of a GenAI-based learning system and comprehensive instructional materials tailored for higher education contexts.
Led by three European universities, the project delivers practical resources disseminated across multiple platforms and rigorously evaluated by both educators and students. Beyond providing the learning system and materials, we establish ongoing community support to help educators integrate GenAI solutions into their teaching practices. Through these efforts, the project advances educational innovation by harnessing cutting-edge technology to foster more profound and authentic learning experiences.
Led by three European universities, the project delivers practical resources disseminated across multiple platforms and rigorously evaluated by both educators and students. Beyond providing the learning system and materials, we establish ongoing community support to help educators integrate GenAI solutions into their teaching practices. Through these efforts, the project advances educational innovation by harnessing cutting-edge technology to foster more profound and authentic learning experiences.
Participating Institutions
German Dialects: Document, Preserve and Learn (with the help of AI)
Project Description
The project is being realised in collaboration with Ludwig Maximilian University of Munich, the Austrian Research Institute for Artificial Intelligence, and the University of Liechtenstein. The objective is to document and study the dialects of Liechtenstein, western Austria, and South Tyrol.
The project is divided into three phases:
Phase 1: Documentation and archiving: Collection of audio recordings in dialects from public sources, enriched with audio recordings created specifically for the project.
Phase 2: Machine processing of the audio collection: The collected audio files are automatically transcribed into Standard German and metadata is created: gender of the speaker, associated dialect region and age group.
Phase 3: Learning: The used AI models are offered to interested parties on a platform. Instructions are created for linguists and AI researchers on how these models can be integrated into their research work. The platform also supports the learning of dialects. In this way, it helps to overcome the language barrier for relocators.
The project is divided into three phases:
Phase 1: Documentation and archiving: Collection of audio recordings in dialects from public sources, enriched with audio recordings created specifically for the project.
Phase 2: Machine processing of the audio collection: The collected audio files are automatically transcribed into Standard German and metadata is created: gender of the speaker, associated dialect region and age group.
Phase 3: Learning: The used AI models are offered to interested parties on a platform. Instructions are created for linguists and AI researchers on how these models can be integrated into their research work. The platform also supports the learning of dialects. In this way, it helps to overcome the language barrier for relocators.
Participating Institutions
GenAI-Natives - Educating (the next generation of) teachers on usage of generative artificial intelligence
Project Description
The project aims to enhance the understanding and effective Integration of generative AI primarily in secondary education by evaluating real-world AI usage. It seeks to provide best practices to use and self-implement AI tools that empower teachers to leverage AI ethically and effectively, fostering digital literacy, AI competency, and innovative teaching methods. It aims to be accessible for a wide audience by providing online material and hybrid events. Our data and results are going to be based on a large-scale pilot study with 100+ teachers using ChatGPTbased tools. Our mixed methods study design covers interviews, surveys, and computational analyses. We will develop and deliver teacher training workshops, create teaching materials, and design customizable AI tools for lecturers, as well as guidelines on how to do so. The project will produce evidence-based insights into Al's role in education, best practices for its use, unveil limitations, provide teacher training materials, and AI tools for lecturers. As we integrate our findings into teaching teachers, the benefits will be long-term and communicated through the next generation (as well as existing) teachers to children. These outcomes will support ongoing digital literacy and AI Integration, benefiting teachers, students, and educational institutions.