Living Models Lab

The Living Models Lab is a cross-disciplinary research incubator for Human-Centered Intelligence where early-stage ideas are translated into feedback-enabled prototypes (living models). These models function as experiential platforms, enabling rethinking knowledge and reimagining technological advancement through lived experience.

Living models embody a new paradigm of development in which software is not simply assembled and deployed, but continuously generated and evolved through Generative and Human-AI co-development. The model remains central to this development process, which is both value- and learning-driven.

What distinguishes this approach from conventional software development and traditional low-code is that value and learning are intrinsic to the model by design. The models represent socio-technical systems organized around societal challenges and are feedback-centric by design. They are designed to support continuous learning by the people who use them while also learning from interactions with the system itself. This feedback is used to update the model, allowing the socio-technical system to continuously adapt and evolve. The development process is generative and Human-AI co-developed, with the model—not the code—evolving to reflect changes in the socio-technical system, enabling a new low-code approach to continuous development.

Co-created with stakeholders and empirically grounded, they serve as proof-of-concept mechanisms for innovation in various domains guided by principles of eduation 5.0 treating technology as learning systems. The Lab focuses on high-impact contexts including, among others, higher education, (inter-)organisational and lifelong learning, digital health and hospital education, as well as AI-enabled learning environments.

Originating from value- and learning-driven design philosophy the Living Models Lab currently connects several methodological clusters such as (H)iCARE (responsible AI for digital health, Conversational AI for Education 5.0, and HUMANE.AI (human-in-the-loop hybrid intelligence), founded and led by G. Sedrakyan, co-designed in collaboration with M. Amir Haeri, S. Borsci, OOST Simulation Center (HF-CODE & BMS Lab) to advance human-centered intelligence.

Methodological distinctiveness

Translational Research through Living Models

The Living Models Lab advances cross-disciplinary translational research by translating domain theory into data and technology that supports learning and human practice.

1. Translating domain knowledge into dynamic models of value and change

The Lab supports researchers and industry partners by translating theory and professional knowledge into executable models and early-stage prototypes—what we call living models.

The Lab’s methodological distinctiveness lies in addressing a critical yet often overlooked layer: while domain modelling, semantic technologies, interoperability mechanisms typically operate on already-defined representations, the Lab focuses on how abstract domain concepts are first rendered into operational and computational forms (e.g. by identifying what data captures those abstract concepts meaningfully), that are also value- and learning-centric by design. A central component of this approach is the development of executable living models that make these choices and operationalisations visible, executable, testable, and revisable in real-world contexts. They provide a shared boundary object through which researchers, technology developers, practitioners, and end users can examine whether theoretical ideas remain meaningful when implemented. Through iterative evaluation in real-world settings, living models enable empirical validation, responsible refinement, and the translation of scientific knowledge into practical impact.

2. Integrating cross-disciplinary theories and reciprocal learning

The Lab develops technology that is not only informed by domain knowledge but also grounded in cross-disciplinary theories (such as human behaviour and learning among others). It investigates how complementary theoretical perspectives can be integrated into coherent technological designs.

The concept of a living model also implies reciprocal learning: systems support users in developing knowledge and making informed decisions while learning from user interactions, feedback, and changing contexts, which also implies systems are feedback-centric by design. These feedback processes are made explicit and subject to human oversight, enabling both the technology and its underlying representations to be evaluated and responsibly adapted over time.

The theory-integrative approach helps ensure that technological innovation accounts for meaningful technology design but also for how people learn, interpret information, make decisions, collaborate, change their behaviour over time and how technology adapts to support this dynamics. The aim is to develop systems that are scientifically grounded, human-centred, context-sensitive, and sustainable in practice.

3. Developing interdisciplinary talent through NextGenMinds

Through NextGenMinds, the Lab brings together students and early-career researchers from technology, behavioral and social science, healthcare, education, and related disciplines. Working in multidisciplinary teams and in close collaboration with researchers, practitioners, and industry partners, participants co-design and develop research-driven solutions to complex societal challenges.

Technology students gain experience in developing systems with and for stakeholders such as healthcare professionals, patients, psychologists, educators, and learners. Social and domain scientists learn to translate theoretical constructs and professional knowledge into data requirements, computational representations, and testable prototypes.

In this way, the Lab serves as both a research environment and an interdisciplinary learning space—transforming scientific ideas into living models while preparing a new generation of professionals to develop responsible, evidence-informed AI and digital technologies across disciplinary boundaries.

Methodological software support

MAESTRO: Model-driven AI for Engineering SofTware and feedback-Driven oRchestration
MERGE: Model-driven Engine for Retrieval-Generated Execution

Contact: Gayane Sedrakyan (IEBIS-BMS)

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