Key Points
- Students learn to read reflectivity and surface response by combining theory with manual evaluation of simulation outputs.
- Emotional light metrics remain non-universal; teaching focuses on structured frameworks, not formulas.
- “As Found” vs “Restored” simulations train students to see light as an interpreter of material history.
- Environmental data such as light pollution acts as a design input, not a constraint, during sourcing and specification.
- Mapping 70+ façade elements enables more accurate sourcing and restoration decisions rooted in documented material–light behaviour.
Full interview with Thanos Balafoutis
1. Your methodology prioritises a detailed material register for lighting simulations. How do you train students to evaluate material reflectivity and surface response as a critical design parameter?
The evaluation of materials and their relationship with the environment in which they are found is a complex process that algorithms in three-dimensional digital space can analyze with relative ease, especially nowadays with the use of AI. However, it is the human factor that defines, examines, and evaluates each result. So students understand that every piece of data in a simulation must be evaluated by themselves, as long as they have studied in depth and mastered every aspect of the theory of the issues they are dealing with, and all this study is based on a methodology that guides them. The reflectivity of a material and the response of the surface on which it is located in a simulation involves the relationship between multiple factors, such as the qualitative characteristics of the material, the object, the lighting, the method of extracting the result, and, of course, the software settings. Of course, however, it is the relationship between the material and the light that will determine the final result.
2. In your lighting simulations, the emotional impact of light is quantified through photorealistic renderings. What challenges arise when translating emotional responses into teachable, repeatable metrics?
In translating emotional responses to light into teachable and repeatable metrics, the main challenge lies in the inherently subjective nature of emotion itself. While photorealistic renderings can accurately reproduce lighting conditions, they cannot fully capture the perceptual and cultural diversity that shapes how individuals perceive light. Emotional responses, such as awe, tranquillity or tension, depend on context, memory and spatial experience, factors that cannot be quantified. Thus, any attempt to measure them risks reducing a complex, integrated response to abstract values.
Another challenge is methodological. Even when participants evaluate outcomes under controlled conditions, their interpretations remain dependent on context and language. What appears "calm" or "intense" in one cultural context may evoke a different emotional tone in another. For this reason, the pedagogical transfer of such results, the conversion of emotions into design rules, requires balancing empirical consistency with interpretive openness. Rather than seeking universal metrics, the goal becomes to define structured frameworks that guide the emotional intentions of design without determining them, allowing lighting education to incorporate both measurable parameters and aesthetic intuition.
3. When dealing with aged facades where material surfaces have deteriorated, how do you determine whether to simulate ‘as found’ or ‘restored’ conditions, and what does this choice teach students about material history?
When working with historic facades, surfaces, or murals, the decision to simulate the "as found" or "restored" condition depends on the narrative purpose of the lighting study. Choosing the "as found" condition allows us to engage with the authentic patina of time, the irregular reflectivity, corrosion, and discoloration that reveal how light interacts with decay.
This approach tells us that decay isn't just visual noise, but part of the building's historical texture and atmosphere. At the same time, studying the existing situation using the appropriate equipment in our faculty's Lighting Laboratory will provide us with the answers we need regarding the authentic identity of the material. Simulating the "restored" condition shifts the focus to the design intention and ideal perception, exploring how light could once reveal the original chromatic and morphological clarity of the surface but also, in particular, the living conditions in the spaces under these lighting conditions. The educational value lies in comparing the two. Students learn that light does not illuminate a neutral object, but a material history. Each condition of the surface transforms the way in which light narrates the past, making the act of simulation itself a form of historical interpretation rather than a simple representation.
4. Your simulations account for ambient light pollution and context. How do you advise future designers to balance site-specific environmental data with aesthetic intent during sourcing or specification?
Balancing environmental data with aesthetic intent begins with recognizing that lighting design is never autonomous, but always dependent on the environment. For this reason, we as designers should approach data relating to specific spaces not as constraints, but as formative factors that influence creative decisions. Light pollution, sky glow, and ambient light levels set the baseline for any aesthetic intervention. Understanding these measurements allows designers to decide when to harmonize with existing conditions and when to create.
In particular, light pollution should not be treated as a mere technical limitation, but as a complex environmental and cultural phenomenon that designers must study and understand in depth.
As shown in the recent publication “Light Pollution Beyond the Visible: Insights from People’s Perspectives” (Balafoutis et al., 2025), knowledge of the mechanisms and effects of artificial light at night is essential for developing contextually responsible design strategies. The key is to let environmental measurements inform, rather than dictate, the artistic expression.
During sourcing and specification, this means selecting luminaires and their quality characteristics that respond to both the measured realities of the site and the desired emotional tone of the project. Ultimately, the goal is not to impose beauty on the context, but to reveal it through a dialogue between data and intention, where the accuracy of measurement serves, rather than suppresses, the poetry of light.
5. The research maps over 70 morphological elements of neoclassical facades. How can this level of typological specificity inform better sourcing of historical components in restoration or academic projects?
A greater comprehension of the ways in which light interacts with architectural form and material over time is made possible by mapping such a broad range of morphological elements. This level of typological accuracy aids in the understanding by practitioners and students that neoclassical façades are complex assemblies of interdependent parts, each with a distinct optical and symbolic function, rather than stylistic monoliths.
This analytical framework facilitates more precise sourcing of historical components when used in restoration or academic projects because it allows decisions to be made based on documented relationships between geometry, materiality, and illumination rather than just gut feeling.
These morphological categories, such as bases, bodies, coronations, openings, and ornamental details, were analyzed extensively in a previous publication, “A Database of Architectural Details: The Case of Neoclassical Façades Elements” (Balafoutis & Zerefos, 2018), which catalogued over seventy distinct elements across international examples. Integrating that database with lighting simulations allows designers to understand not only what constitutes formal authenticity but also how each element historically responded to light.
In this way, typological research explores restoration options while teaching that material history is revealed in an optimal way by light, and that together they are inseparable dimensions of architectural heritage.
6. With the rise of AI-driven sourcing and digital twin models, how do you see tools like material traceability engines supporting lighting studies in the future?
The way lighting studies engage with materials and the environment could be drastically changed by digital twin and artificial intelligence-based procurement technologies. Systems that can map a material's origin, composition, and ageing behaviour are known as material traceability engines, and they can help close the gap that currently exists between digital simulation and physical substance. Designers may be able to forecast how particular materials will react to light over time and in various environmental settings with the use of such tools in future lighting studies.
Beyond accuracy, this traceability adds an ethical component, knowing where materials come from and how they deteriorate, strengthens the designer's obligation to preserve the environment and culture. This data becomes dynamic in digital twin environments, feeding photometric simulations with real-time updates from sensors or archives.
As a result, light stops being an abstract aesthetic aura added to architecture and instead becomes part of a living ecology of materials, measurable, interpretable, and narratively based on the material history of the building.
7. Texture and material gloss were shown to alter lighting outcomes significantly. What are the most common misconceptions students have when simulating material finish, and how do you correct them?
One of the most common misconceptions students have when simulating material finishes is that they treat texture and gloss as purely visual properties of the surface rather than optical behaviors that actually shape the distribution of light.
Many assume that increasing reflectivity will simply make a surface "brighter," overlooking how gloss and diffusion affect contrast, shadow softness, and color perception. Another common mistake is relying on "ready-made materials," i.e., predetermined digital presets, without adjusting them to the physical reflectivity or roughness of the actual materials we are working with, which leads to unrealistic or overly idealized results.
Equally important, I encourage students to observe materials under controlled lighting conditions in real life, comparing how diffuse and specular reflections behave at different angles and lighting levels. This experience teaches that finishing is not a stylistic adjustment, but a mediator of the dialogue between light and matter, and that fidelity in simulation begins with understanding how materials feel light, not just how they look.
8. Your findings show that lighting intensity directly shapes emotional response, from awe to boredom. How do you incorporate this emotional register into the academic curriculum without reducing it to a mere formula?
The integration of the emotional response to the effect of lighting into the curriculum requires that it be presented as a framework for exploration rather than as a fixed rule. I emphasize that lighting intensity affects perception and emotion, but that these effects depend on context, materiality, and individual interpretation. Students are guided to use intensity as a flashlight for experimentation, adjusting lighting levels in simulations and observing how subtle changes alter mood, atmosphere, and spatial hierarchy.
The emphasis is on cultivating perceptual awareness and critical thinking, encouraging students to articulate why a space evokes awe, tranquillity, or tension, rather than simply applying a numerical target.
Combining reflection exercises, controlled simulations, and case studies, the curriculum frames emotional impact as a design tool that is measurable enough to influence decisions, yet flexible enough to preserve the richness and ambiguity of human experience. This approach teaches that the "emotional register" of light is a dialogue with perception, not a formula to be applied mechanically.
9. Architectural lighting often intersects with public regulation and heritage law. How do you educate emerging designers to navigate material specification when legislative constraints override aesthetic preference?
In approaching this topic, I emphasize that design operates within multiple interdependent contexts: legal, ethical, environmental, and aesthetic. Students first learn the principles of material behavior, light performance, and perception, and then study the specific regulatory and cultural contexts that govern the spaces they work with.
Case studies of successful projects show how constraints can be catalysts for creativity rather than obstacles. Legislative requirements should not be seen as obstacles, but as parameters within which experimentation must take place, teaching skills of negotiation and documented justification of design choices.
10. The integration of real-world light performance data (IES files, CRI indices, etc.) in your simulations is rigorous. How do you ensure students develop fluency in this technical literacy early in their studies?
Instead of giving the students abstract theory about the technical data, I slowly introduce it by using it in real-world design exercises. They start by examining basic simulations with illumination data and qualitative features, seeing firsthand how these elements impact highlights, shadows, and material perception. I ask them to explain how numbers relate to feelings or visual effects by combining these activities with deep conversation. Visualisation tools and comparative studies that show the same scene in different lighting conditions back up the real-world results of technical measurements. Fluency is built through practice and trial and error, which eventually lets them confidently use precise lighting data in simulations and real design decisions.
11. Do you foresee universities adopting shared material-lighting simulation libraries, akin to material banks, to support rapid prototyping and ecological sourcing in design studios?
Yes, I predict that shared material and lighting simulation libraries could become a standard resource in design education in the future, especially with the collaboration of AI.
Such databases, similar to digital material banks, will allow students, researchers, and professors to access calibrated material properties, photometric data, and realistic light behavior without having to create each element from scratch. After all, why not do this, since collaboration and knowledge sharing between, for example, researchers, is what advances us and our science?
12. In your view, how can cultural heritage lighting education evolve to prepare students not just to replicate historical aesthetics, but to innovate within constraints of sustainability, digital sourcing, and regional materiality?
Education in cultural heritage lighting can evolve by treating the historicity of an object not as an endpoint but as a starting point for exploration.
Students should be encouraged to analyze the interaction between light, material, and form in historical contexts, while having studied and understood the historical significance and architectural value of the historic building or, more generally, a cultural heritage monument, and then use this understanding to inform design choices that are sensitive to sustainability.
The integration of parametric tools (material databases and photorealistic simulation), based on the characteristics that document each object as a cultural asset, will allow them to experiment with energy-efficient solutions, adaptive lighting strategies, and materials suitable for the region, while preserving the perceptual and emotional qualities of cultural heritage sites.
Case-based tasks, where students balance conservation principles with ecological and technological constraints, cultivate both technical literacy and design intuition. By emphasizing repetitive and research-based experimentation, education can shift from reproduction to responsible innovation.
