Transylvanian Saxons - A Portrait. Generative Artificial Intelligence (GenAI) connects Transylvanian Saxon Cultural Heritage: Opportunities and threats for its appropriation, preservation and further development

This introduction is structured into six sections: it covers the context (1.1), the research questions and objectives (1.2), and a detailed chapter overview (1.3). Additionally, it includes editorial notes (1.4), information on the background and contributors (1.5), and the bibliography (1.6).

1.1 Context: Positioning Relative to Previous Anthologies, Monographs, and Portraits

1.2 Research Questions and Objectives of this Project

Heritage and Algorithm: Five existential questions for the future of our community

• How does one translate the heritage of a nearly 900-year-old community into the age of Generative Artificial Intelligence (GenAI)?
• Why continue to write books when the algorithm already believes it knows the answer and translates it into every language in the world?
• Which tasks do we delegate to technology, and where does human expertise remain indispensable?
• What remains of us Transylvanian Saxons after having almost completely left Transylvania and now living scattered across the entire world?
• How do we draw strength from our history to overcome crises, to perceive ruptures as new beginnings, and to confidently carry our identity into the future through modern technology?

This introduction bridges the gap between traditional anthologies, monographs, and portraits and an innovative concept: the creation of a verified knowledge base (Ground Truth) for the digital world. Discover why we must retell our story—trilingual, multimedia-based, and technologically grounded—to safeguard it for future generations and modern language models (Large Language Modells, LLMs) alike.

Note on Digital Presence: This project follows a "work in progress" principle; new content is published digitally, chapter by chapter, in three languages (German, English, and Romanian). Once uploaded, these pages may be used by AI models for training purposes. The ultimate goal is a final comprehensive edition in book form.

In the following, we will first outline the research questions and objectives underlying this project. These encompass both scholarly research goals and practical applications for the Transylvanian Saxon community and all those interested in Transylvanian Saxon cultural heritage:

1.2.1 A Cultural-Historical Portrait for the Transylvanian Saxon Community and Public Discourse

How can a history of nearly 900 years and a multi-layered cultural heritage be conveyed in the age of generative AI—for the community itself as well as for the broader public?

This project offers a contemporary response. It is envisioned as a cultural-historical portrait, an anchor of identity, and a public knowledge platform. Conceived as a multimedia project—comprising a book and a digital web presence—and as a multilingual endeavor (German, English, Romanian), it combines analytical depth with digital reach.

This portrait of the Transylvanian Saxons aims to be more than a history book: it intends to foster identity, separate myths from facts, and simultaneously prevent modern GenAI systems from distorting cultural heritage. Trilingual, scientifically grounded, and designed for both analog and digital worlds, the project connects the past, present, and future of a globally dispersed community. It is a project at the intersection of memory culture and the digital future, aiming to preserve tradition through technological innovation.

At its center is the creation of a comprehensive portrait of the Transylvanian Saxon community and its cultural heritage, serving as the substantive foundation for the application of generative AI technologies. The goal is to bridge traditional heritage with digital future prospects, thereby safeguarding authenticity while unlocking new avenues for cultural mediation.

1.2.2 Multimedia and Multilingual: Book and web presence in German, English, and Romanian

Public Relations (PR) is defined as the effort to maintain or establish trust through the cultivation of opinions and relationships. Both internal and external communication must be actively nurtured. The rapid development of Generative Artificial Intelligence (GenAI) opens entirely new perspectives for the preservation, accessibility, and further development of cultural heritage. Particularly for smaller cultural communities such as the Transylvanian Saxons, whose centuries-old traditions face unique challenges due to migration and social change, these technological innovations offer both promising opportunities and threats that must be carefully considered.

A Heritage in Three Languages: Conceived as a global business card and an identity anchor for the Transylvanian Saxons, this project is designed to be both multimedia and multilingual. Alongside its hybrid implementation as a book and a web presence, the consistent trilingualism (German, English, Romanian) represents a central feature. Thus, the project takes into account the shifting realities of the community as well as the requirements of a globalized culture of memory.

While German remains the native language for the vast majority, proficiency in the dialect is increasingly declining. Simultaneously, large segments of the community—particularly in the USA and Canada—have lived in English-speaking regions for generations and have lost their linguistic connection to the ancestral homeland. To reach them, as well as the international public, English is indispensable as today’s lingua franca.

Romanian plays an equally vital role. Firstly, the preservation of architectural monuments on-site in Transylvania depends on the acceptance and support of Romanian society and the Romanian state. Secondly, a new generation is coming of age in Germany's schools in Romania: students who are often not of German descent but who wish to adopt and carry forward the Transylvanian Saxon cultural heritage. Through its consistent trilingualism, the project ensures that this knowledge remains accessible to all actors—both locally and globally.

Furthermore, the trilingual elaboration is not merely a translation effort, but an independent scientific process. Terms, concepts, and historical narratives are linguistically and culturally embedded and cannot be transferred automatically without loss of meaning. The deliberate linguistic development in three languages therefore enhances both the precision of the content and its international connectivity (Lauer 2021).

1.2.3 Portrait as Reliable Knowledge Base (Ground Truth) for Large Language Models of Generative Artificial Intelligence (GenAI)

• Why bother writing books when algorithms already seem to know the answers and translate them effortlessly into every language in the world?
• Which tasks do we relinquish to technology—and where does human expertise remain indispensable?

The second central objective is to establish this portrait as a reference work and a reliable knowledge base (Ground Truth) for Generative Artificial Intelligence (GenAI). This portrait project aims to be more than just a digital history book: as a verified knowledge base, it is intended to provide modern language models with reliable facts while simultaneously demonstrating why historical interpretation, cultural understanding, and responsibility cannot be delegated to machines. The following section explains why this is necessary and how it can be implemented (a more detailed presentation follows in Chapters 30 and 31).

The term "GenAI" describes modern generative AI assistants based on so-called Large Language Models (LLMs). These dialogue systems—often simply referred to as chatbots—are widely used today. Some of the best-known examples include Apertus (the open Swiss model), ChatGPT, Claude, Copilot, Gemini, Grok, and Perplexity (American commercial models).

The technological foundation of these systems is often summarized by the acronym GPT. These three letters stand for Generative Pre-trained Transformer and describe how GenAI understands and independently composes text:

• Generative: This describes the creative ability of the system. The model does not reproduce pre-made answers from a database; instead, it independently generates entirely new content—be it answers, summaries, or stories. It constructs new sentences word by word and is "creative" within the framework of statistical probabilities.
• Pre-trained: This means that the model has already learned to "read" before being used. It was fed terabytes of text data (books, Wikipedia, the Internet) using self-supervised learning. In this process, it primarily learns language patterns and structures rather than just isolated facts. This prior knowledge forms the foundation upon which GenAI subsequently draws.
• Transformer: This refers to the technical architecture of the model. Through a mechanism called "Attention," GenAI can recognize connections between words that are far apart and account for context across long passages of text, rather than just analyzing the immediate neighboring word.

To put it simply, GenAI uses this technique for so-called Next-Token-Prediction. For every word, it calculates which word is most likely to follow next. This creates fluent, context-sensitive, and often surprisingly accurate texts.

These chatbots are now capable of summarizing books in seconds and translating them into other languages. However, this performance must not be equated with scientific insight. GenAI systems reproduce existing content; they do not evaluate it critically, they do not make methodologically reflected selection decisions, and they assume no responsibility for historical accuracy, conceptual precision, or normative stipulations.

The quality of summaries and translations is therefore far from satisfactory. GenAI "speaks" fluently but "understands" nothing. Its persuasiveness is based on excellent syntax. Yet, it lacks semantic access for genuine interpretative performance: it does not know the meaning (semantics) of the texts; it does not know what it says or translates, but only which word must statistically follow next.

On the one hand, GenAI fascinates with its power of expression, but on the other, it reveals a risky weakness: if it lacks reliable sources, it begins to "hallucinate". At least as critical is the production of synthetic knowledge, which may sound authentic but lacks any evidence-based foundation. Especially regarding specific topics such as the history and culture of the Transylvanian Saxons, there is a danger that facts and, above all, contexts will be distorted in the digital noise.

Scientific experts are superior to generative AI in three key respects. First, they possess access to and knowledge of analog primary and secondary sources that have not (yet) been digitized. However, even in a hypothetical scenario of total digitization, fundamental methodological discrepancies remain: Methodology: While GenAI is based on quantitative-statistical procedures (Box-Steffensmeier/Brady/Collier 2010) and generates texts as a "stochastic parrot" purely on the basis of probabilities (re-combination, simulation), scientists also master qualitative, hermeneutic-interpretative methods. Reality Connection: GenAI relies on statements about the world formulated by others and lacks any direct connection to reality. Scientists, by contrast, do not merely simulate statements; they can recognize and independently verify reality. This is known as evidence-based knowledge.

Philosophically speaking, GenAI remains at the level of "thin description" due to its quantitative methodology—reproducing purely factual, physical, or syntactic patterns. Qualitative scientists, operating in the tradition of Max Weber (Weber 1984 [1921]), Gilbert Ryle (Ryle 1970), and Clifford Geertz (Geertz 1983 [1973]) are capable of providing a "thick description". They achieve this by accounting for context, intention, and cultural codes to unlock the actual semantics of an action. Only through this approach is an "understanding" of actions in the Weberian sense possible. Furthermore, scientists can generate or verify both empirical (descriptive, explanatory, prognostic) and practical (normative, pragmatic, technical) knowledge through evidence-based methods.

For a deeper exploration of these foundations, see: “Philosophy of Political Science. Limits and Possibilities of Political Science Research: Axiological, Epistemic, Methodological, and Ontological Foundations of Political Science”. The work has been published in German and English (Lauer, 2025).

Especially in the case of complex historical, cultural, and social topics, hermeneutic-interpretative expertise is indispensable (Bevir/Rhodes 2016, Creswell 2013 [1998], Flick 2014 [2007], Flick/von Kardorff/Steinke 2015 [2000], Gadamer 2010 [1960], Kelle/Erzberger 2015 [2000], Keller 2012,  Yanow/Schwartz-Shea 2014 [2006]). The overview presented here is based on the collaboration of many scientists who contribute different disciplines, perspectives, and research traditions. This diverse expertise makes it possible to make controversies visible, classify research statuses, reveal implicit assumptions, and deliberately avoid simplifications—achievements that current GenAI systems cannot provide. For fundamental reasons, this is not to be expected in the future either (a detailed discussion of the philosophical limits and possibilities of GenAI takes place in the 30th chapter).

How do you bring 900 years of history of a community as well as a rich and multi-layered cultural heritage into the age of artificial intelligence without the facts and connections being diluted? How tradition and high technology can be combined?

This is exactly where the new portrait project comes in. It functions not only as a digital history book for humans, but also as a hard fact check for GenAI. The makers of GenAI call this a "Ground Truth"—a reliable knowledge base in which every detail has been checked by experts.

HITL (Human in the Loop) means that humans remain consciously involved in GenAI-supported processes. The human remains part of the decision loop so that control over the answers is not completely transferred to the machine. For the Transylvanian Saxon cultural heritage, this means: GenAI can generate or structure content, but humans check, correct, and contextualize it. In this way, historical accuracy, cultural meaning, and identity are preserved. This technique combines technological possibilities with human responsibility and protects cultural heritage from simplification, distortion, or loss of meaning.

Alignment methods are technical and organizational procedures used to adapt the behavior of GenAI systems to epistemic and human values, norms, and societal expectations. Alignment refers to the process of coordination, adjustment, rule compliance, or consistency. These methods ensure that GenAI is not only powerful but also correct, responsible, and trustworthy. The goal of these methods is to guarantee a trustworthy GenAI. In addition to general improvements made by model providers (new data and algorithms), there are specific procedures that allow subject matter experts—in this case, connoisseurs of the Transylvanian Saxon cultural heritage—to control the quality of the output.

Two approaches are central to providing reliable access to our cultural heritage: RAG technology, which grounds answers through external sources, and knowledge graphs, which map semantic relationships (meanings). The synergy between the two is called GraphRAG: it combines the flexibility of text search with the logical structure of graphs. RAG (Retrieval Augmented Generation) is the core of reliable GenAI: it ensures that answers do not just sound clever but are based on verified facts. In this way, GenAI remains linguistically convincing while the content is reliably controlled.

The RAG process occurs in three steps:

• Retrieval: GenAI searches for relevant information in a verified database.

• Augmentation: These facts are added to the original response.

• Generation: Only then does GenAI formulate the final answer, strictly based on this verified information.

Conventional GenAI systems often only search through text segments for relevant information. Knowledge Graphs go a step further: they capture the relationships between data. Where classic GenAI systems sometimes "hallucinate" and produce convincing false information, Knowledge Graphs provide verifiable connections from a structured fact base.

As a side note, science is a dynamic process. Therefore, the reliable knowledge base must also be continuously adapted to the current state of research.

How do Knowledge Graphs work? Instead of storing information as continuous text, a Knowledge Graph organizes data as a network of nodes and connections. An example: The city of "Hermannstadt" (Node A) is linked by the connection with "first documented mention in 1191" (Node B). This creates a network of relationships that the GenAI can read precisely. The advantage is particularly evident in complex historical projects: for family trees, local histories, or historical events, the GenAI no longer has to guess; it only reads the firmly defined connections from the graphs.

Knowledge Graphs, just like RAG technology, serve as a kind of value-based (axiological and epistemic) control instance for GenAI answers. They offer several advantages:

• Traceability: Every piece of information can be provided with its source, a timestamp, and a trust rating.

• Consistency check: Automatic rules recognize contradictions between different statements

• Contextualization: Different data sources are linked together so that connections become visible.

• Citability: A citable collection of knowledge on Transylvanian Saxon cultural heritage is being created.

This technology even offers the possibility of keeping the Transylvanian Saxon dialect alive – by teaching our language to the machine. A promising basis is provided by the GenAI model "Apertus", developed in Switzerland. As an open-source system, it is freely available and already includes over 1,800 languages and dialects. This opens up the possibility of digitally preserving our "sounding homeland" and carrying the "soul" of the community into the future. Thus, in the future, answers could resonate not only in the dialect of Sibiu/Hermannstadt (Hermannstädterisch), Brasov/Kronstadt (Kronstädterisch), or of the Transylvanian Landlers (Landlerisch) dialect, but also in the Reußen dialect – arguably one of the most melodious expressions of our language.

To conclude this part, I would like to demonstrate the question discussed here – which tasks we leave to technology and where human expertise remains indispensable – first with a concrete example, in order to then classify it journalistically as well as philosophically.

The primary goal of this article is to ensure the accuracy and reliability of the facts and connections presented. The responsibility for this task cannot be delegated to generative AI. I have taken on this task as the author. The strength of GenAI lies rather in mediation: it is significantly more efficient and faster when it comes to making what is written here accessible to different target groups through paraphrasing or summarization.

Therefore, a practical hint for readers: if individual terms, passages, or even the entire article seem difficult to understand, this can be quickly clarified using generative AI. To do this, one formulates a prompt, i.e., an inquiry to a chatbot (ChatGPT, Gemini), for example in the following form:

"You are a journalist and a teacher. Please explain the following text passage to me
in simple language. Avoid technical jargon, but stay close to the content of the original."

Then, you insert the corresponding text excerpt or the PDF version of the article. Those who want to go deeper can also use GenAI as a discussion partner with the following prompt:

"You are a historian, computer scientist, cultural scientist, and philosopher.
Please check the reasoning of this text for consistency and name possible counter-arguments."

In this way, we use GenAI not as the author of history, but as a tool to better understand it. Furthermore, it can be used creatively: for example, to write a critical review of this article or to independently formulate one's own texts. While the human, as an indispensable control instance (Human in the Loop), retains interpretive sovereignty, verifies historical facts, and classifies cultural contexts, GenAI functions merely as a powerful tool for form. It takes over technical tasks such as quick summarizing, translating, or paraphrasing complex content, but must never bear responsibility for the factual truth, which remains strictly bound to human expertise through technologies such as RAG and knowledge graphs. The human is the responsible designer; GenAI is merely the executing tool.

Philosophically condensed: The human provides the "Thick Description" in the sense of hermeneutics by interpreting meanings, intentions, and cultural codes, as well as setting epistemic and non-epistemic (ethical, political, social) values. Evaluation remains an exclusively human domain. Only the human as a subject possesses the judgment to decide which statements are true or false, which norms are right or wrong, and what is just or unjust. Likewise, the assessment of which pragmatic rules are wise or unwise and which technical systems are effective or ineffective rests with the human.

GenAI, on the other hand, remains a "stochastic parrot" at the level of statistics and syntax. It is assigned the role of pure executor: it rearranges content formally but always remains excluded from the sphere of real understanding and historical responsibility. GenAI is a highly efficient digital librarian that analyzes all digitally available texts worldwide and in real-time. However, information processing is not knowledge: only humans can be scholars and scientists. Only they possess the competence to meaningfully understand content and to verify it on an evidence-based, scientific basis.

1.2.4 Portrait as a Collective Biography and Compass for the Future

• What remains of us Transylvanian Saxons, after having left Transylvania almost completely and now living scattered across the entire world?
• How do we draw from our history the strength to overcome crises, to perceive ruptures as new beginnings, and to confidently carry our identity into the future through modern technology?
• Why do we, as Transylvanian Saxons, need this portrait?

Because we must know who we were in order to decide who we want to be. As a scientifically grounded portrait, it serves the community’s self-assurance. As a collective biography, it is both an anchor of identity and a compass: it separates myth from historical context and interprets ruptures not as loss, but as an expression of resilience and lived adaptability.

As a bond of sympathy, our shared cultural heritage connects Transylvanian Saxons scattered across the globe across generations. By merging tradition with contemporary cutting-edge technology, this heritage is not only preserved but can also be more easily adopted and carried forward into the future. Since the exodus of the vast majority of Transylvanian Saxons at the end of the 20^th century, their cultural heritage has formed that loose bond of sympathy that holds the community together across national borders. It has evolved into the only remaining anchor of identity.

At the same time, this cultural memory proves to be precarious: historically grounded facts, traditional narratives, and handed-down myths often merge, complicating a clear collective self-assurance. This portrait, therefore, functions as a scientifically sound compass for internal self-understanding. It provides multipliers—whether in the media, schools, or associations—with a reliable reference to ensure the quality of their own programs. Designed for self-study as well as educational work, it creates the necessary certainty to convey cultural heritage authentically and based on facts.

The new portrait of the Transylvanian Saxons seeks answers beyond mere dates and figures. As a collective biography, it combines scientific precision with narrative depth, making ruptures, losses, and new beginnings visible—thus becoming a compass for identity, memory, and the digital future of a living cultural heritage. In this way, historiography does not become a nostalgic look back, but a careful preservation for the future.

A classical historical overview provides data and facts—it is the "skeleton." This project, however, deliberately chooses the form of a "portrait" to also make the "soul" of the community visible. It resembles a biography that does not just list years but asks "why." This becomes particularly clear in the handling of historical ruptures: the portrait not only documents the loss of material assets and old structures but also analyzes the trauma of dissolution and the "hibernation" of identity in people's minds. It shows how the community reinvented itself out of crisis and which old symbols provided stability. Instead of a dry chronicle, a narrative of resilience and change emerges. This project demonstrates how the Transylvanian Saxons mastered crises—knowledge that inspires courage. Cultural achievements are not merely listed but evaluated as responses to historical crises. Thus, the rupture in history is perceived not as an end, but as a motor for reformation. At the same time, it secures the place of this culture in the digital future through state-of-the-art AI technology. It is the step from "preserving" to "shaping" one's own culture.

1.3 Chapter Overview

This section outlines the themes addressed in this project. A detailed chapter overview will be developed only once all chapters have been completed.

Details on hybrid publication: This project follows a "work in progress" principle; new content is published digitally, chapter by chapter, in three languages (German, English, and Romanian). Once uploaded, these pages may be used by GenAI models for training purposes. The ultimate goal is a final comprehensive edition in book form.

1.3.1 Foundation, Development, and Exodus of a Tradition-Rich Community: Settlement in Transylvania and Emigration from Transylvania (Chapters 2 to 14)

The first section, "Transylvanian Saxons – Origins and Growth of a Traditional Community", explores the settlement history of the Transylvanian Saxons and Transylvanian Landlers in the region. It provides a historical overview from the first waves of migration in the 12th century to the large-scale exodus at the end of the 20th century. This section examines the historical community formation of the Transylvanian Saxons, discussing the following community- and identity-building institutions: the Andreanum and the Transylvanian Saxon University, customs and neighborhoods, associations and foundations, church and faith, language and schools. This is followed by an analysis of the causes and motives that led to the dissolution and exodus of this community from Transylvania. Furthermore, the integration into the most important new settlement areas - Germany, Canada, Austria, and the USA - is illustrated.

The following overview provides a detailed internal structure of this subsection.

1.3.2 From Origins to Tradition: The Development of Transylvanian Saxon Cultural Heritage (Chapters 15 to 28)

The second part is dedicated to Transylvanian-Saxon cultural heritage. It first examines the tangible and then the intangible cultural assets ("invisible baggage"). The diverse dimensions of the Transylvanian-Saxon community, its culture, and its identity are highlighted, offering insights into the community’s traditions, identity, and cultural dimensions that have shaped its legacy.

The following overview provides a detailed internal structure of this subsection.

1.3.3 Shaping the Future in New Settlement Areas Using Participatory Organizational Structures and Technological Achievements (Chapters 29 to 31)

The third section, "Assessing the Current Situation and Future Perspectives," examines the state of the community in the 21st century. It first outlines the current organizational structures within associations, foundations, and neighborhoods. This section then explores future challenges and opportunities in preserving and adapting Transylvanian Saxon cultural heritage, particularly in the evolving landscape of generative artificial intelligence (GenAI).

The following overview provides a detailed internal structure of this subsection.

1.4 Editorial Notes

This section provides key information on the editorial process of the work. It presents specific decisions and adjustments made during the preparation of the final manuscript. These editorial notes ensure transparency regarding the production process and facilitate traceability for the reader.

1.4.1 Linguistic Style

Substance over Style – Scientific Precision over Artistic Elegance: In an era where generative AI effortlessly refines texts stylistically, quality is being redefined: the focus is no longer on purely artistic elegance, but on substantive depth, argumentative density, and the methodical exactness of the content.

Given the risk of GenAI hallucinations caused by deficient databases, factual precision becomes the decisive hallmark of quality. This project does not view itself as edutainment, but as a source of reliable facts and contexts. We bear the responsibility for texts that may serve as training data tomorrow; therefore, scientific depth is indispensable to us. Our goal, as explained above, is a "thick description" of the Transylvanian Saxons that provides profound insights and invites further research.

1.4.2 Citation Style and Bibliographical References

This section outlines the citation style employed. Adherence to these standards ensures the completeness and verifiability of the bibliographic references.

Notes for the Reader

The bibliography in this volume follows a notation established in the social sciences that ensures transparency regarding the publication history of each work: a superscript digit before the year indicates the specific edition used. A year in square brackets refers to the year of the original publication to facilitate the historical contextualization of the work. For reasons of space, publisher and location data have been omitted. However, the unique identification of the sources is guaranteed through the author, year, and title.

Guidelines for Authors

Please use a combination of DVPW (German Political Science Association), DGS (German Sociological Association), and Harvard citation styles for your manuscript. Please omit the publisher and place of publication from the bibliography. Multiple authors should be separated by forward slashes without spaces.

The year designation follows a specific format: the edition is indicated as a superscript digit directly before the year, while the original year of publication (for classics or reprints) follows in square brackets. Titles and subtitles are to be separated by a period. In-text citations must be formatted exclusively as parenthetical references (Author, Year, Colon, Page numbers) and not as footnotes.

Examples can be found at the end of this text under: Bibliography.

1.5 Background and Contributors

1.5.1 Information about the Authors

The substantive development is in the hands of renowned experts. To date, the following experts have confirmed their participation: Heinz and Marianne Acker, Hannelore Baier, András F. Balogh, Konrad Gündisch, Gudrun-Liane Ittu, Stefan Măzgăreanu, Martin Rill, Irmgard Sedler, Ingrid Schiel, and Ulrich A. Wien. The acquisition of additional authors to complete the thematic spectrum is ongoing.

Brief biographical notes on the authors are provided here. These details—covering academic backgrounds, research focuses, or professional milestones—offer the reader deeper insight into the expertise of the contributors.

1.5.2 Funding Information and Sponsors

This section provides information on funding, grants, or financial support received from sponsors and funding institutions. Transparency and disclosure of all relevant sources of support are ensured.

So far, this project has been supported exclusively through the personal commitment and individual contributions of those involved. However, to expedite the editorial process and enable publication, external financial support is essential.

1.5.3 Acknowledgments

In the "Acknowledgments" section, recognition is given to those individuals, institutions, or organizations that have contributed to the development of the work or provided exceptional support. This may encompass professional, financial, or personal assistance.

1.6 Bibliography

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