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Wo ist der KI-Sweetspot? Nutzen und Herausforderungen für die Einbindung von KI-Assiszentzsystemen ins Geisteswissenschaftliche Asset Management System (GAMS)

Nutzen und Herausforderungen für die Einbindung von KI-Assiszentzsystemen ins Geisteswissenschaftliche Asset Management System (GAMS)
Authors: Schiller-Stoff, Sebastian David; Münzer, Leona Elisabeth; Citro, Chiara;

Wo ist der KI-Sweetspot? Nutzen und Herausforderungen für die Einbindung von KI-Assiszentzsystemen ins Geisteswissenschaftliche Asset Management System (GAMS)

Abstract

Digitale Editionen gehören zum Kernbereich der Digital Humanities und stützen sich zunehmend auf technische Infrastrukturen wie das GAMS an der Universität Graz. Mit dem Aufkommen großer Sprachmodelle (LLMs) stellt sich die Frage, wie KI-gestützte Assistenzsysteme die Umsetzung von GAMS-Editionen sinnvoll unterstützen können – etwa bei der TEI-Kodierung, der Entwicklung von Datenmodellen oder von Interfaces. Während der Mehrwert von KI-Systemen Gegenstand aktueller Forschung ist, ist der Wartungsaufwand von neuen infrastrukturellen Funktionalitäten unbestritten. Gerade im KI-Bereich sind jedoch instabile Standards, kurzlebige Dokumentationen und unsichere Rechtslagen weit verbreitet und drohen bei unvorsichtiger Einbindung eine Gefahr für die technische Nachhaltigkeit von DH-Infrastrukturen (wie dem GAMS) zu werden. Vorliegender Beitrag sucht den "technical debt sweet spot" zwischen spezifischer Eigenentwicklung und übergreifender Nachnutzung von KI-Assistenzsystemen im Falle von digitalen Editionen am GAMS.

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Keywords

Paper, Positionspapier, Digitale Editionen, DHd2026, Infrastruktur, Projekte, LLMs, Vortrag: Theorie, Software, Metareflexion

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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