Introduction

Humanistic theories serve as essential frameworks for interpreting phenomena, shaping how scholars understand research fields and formulate questions (Suppe 1998; Abend 2008; Zima 2007; Corvellec 2013). As Johanna Drucker (Suppe 1998; Abend 2008; Zima 2007; Corvellec 2013). As Johanna Drucker (2012) states: “Humanistic theory provides ways of thinking differently, otherwise, specific to the problems and precepts of interpretative knowing – partial, situated, enunciative, subjective, and performative.” Moreover, it is the plurality of theories, which is often regarded as a characteristic feature of the humanities (2012) states: “Humanistic theory provides ways of thinking differently, otherwise, specific to the problems and precepts of interpretative knowing – partial, situated, enunciative, subjective, and performative.” Moreover, it is the plurality of theories, which is often regarded as a characteristic feature of the humanities (Alvarado 2012; Kleymann et al. 2022; Kleymann 2024). As large language models (LLMs) increasingly enter research workflows within digital humanities (DH) scholarship, they influence or even distort practices of theorizing (Alvarado 2012; Kleymann et al. 2022; Kleymann 2024). As large language models (LLMs) increasingly enter research workflows within digital humanities (DH) scholarship, they influence or even distort practices of theorizing (Kirschenbaum 2023; Karjus 2023). At the same time, LLMs are presented as theory-free (Kirschenbaum 2023; Karjus 2023). At the same time, LLMs are presented as theory-free (Spinney 2022; Andrews 2025). While LLMs’ impact on interpretation in DH has been studied, the role of humanistic theories remains underexamined (Spinney 2022; Andrews 2025). While LLMs’ impact on interpretation in DH has been studied, the role of humanistic theories remains underexamined (Jannidis et al. 2025): What kind of theories do LLMs replicate potentially impacting the plurality of humanistic theories? And how could we study what LLMs know about humanistic theory? (Jannidis et al. 2025): What kind of theories do LLMs replicate potentially impacting the plurality of humanistic theories? And how could we study what LLMs know about humanistic theory?

This short paper seeks to investigate the impact of LLMs on humanistic theorizing. I argue that LLMs are highly theory-laden. They are modeled and fine-tuned by theoretically imbued data. This means that the data carries inherent theoretical assumptions and perspectives that shape how the LLM understands and generates theories. This “theory-ladenness” (Hanson 1965; Kuhn 1994; Leonelli 2020) of LLMs as cultural and sociotechnical artifacts needs to be reflected within the DH community. As work in progress, this short paper contributes to discussions on how DH scholars might critically engage with and against LLMs through humanistic theory (Hanson 1965; Kuhn 1994; Leonelli 2020) of LLMs as cultural and sociotechnical artifacts needs to be reflected within the DH community. As work in progress, this short paper contributes to discussions on how DH scholars might critically engage with and against LLMs through humanistic theory (Raley and Rhee 2023; Lindgren 2024). It invites DH scholars to reflect on shared practices of theorizing within our research community. Methodologically, I proceed as follows: starting with the semantic ambiguity of (Raley and Rhee 2023; Lindgren 2024). It invites DH scholars to reflect on shared practices of theorizing within our research community. Methodologically, I proceed as follows: starting with the semantic ambiguity of theory and theorizing in science and humanities, I describe four challenges for humanistic theorizing in the age of LLMs. In a second step, I shortly present one probing case study. It tries to reconstruct theory as a “machinic construct” (Offert and Phan 2022) through a reflective prompting approach. (Offert and Phan 2022) through a reflective prompting approach.

Humanistic Theory and its Plurality

The term theory has its etymological roots in the Greek verb “theorein (θεωρεῖν),” which translates to “to observe,” “to contemplate,” or “to see” (Liddell and Scott 1889). Although (Liddell and Scott 1889). Although theory is a foundational concept across scholarly domains, it remains polysemous. Neither the sciences nor the humanities have reached consensus on a single definition of theory, nor on shared criteria for how theories ought to be constructed or validated (Kuhn 1994; Nuzzo 1999). Theories serve as fundamental components of research, mediating the relationship between an object of study and research communities (Kuhn 1994; Nuzzo 1999). Theories serve as fundamental components of research, mediating the relationship between an object of study and research communities (Martus and Spoerhase 2022). What is so special about humanistic theories is on the one hand that they are not formalized, for example in an hypothesis or model. Rather they exhibit a “semantic-narrative” (Martus and Spoerhase 2022). What is so special about humanistic theories is on the one hand that they are not formalized, for example in an hypothesis or model. Rather they exhibit a “semantic-narrative” (Zima 2007) or discursive structure (Zima 2007) or discursive structure (Winther 2021). On the other hand, humanistic theories can be regarded as “written articulations of complex ideas” (Winther 2021). On the other hand, humanistic theories can be regarded as “written articulations of complex ideas” (Klein et al. 2025: 3). They often emphasize context-sensitive accounts of phenomena. For the purpose of this paper, humanistic theory is understood as a set of discursively structured, non-formalized, context-sensitive interpretive frameworks that mediate between research objects and disciplinary communities in the humanities.(Klein et al. 2025: 3). They often emphasize context-sensitive accounts of phenomena. For the purpose of this paper, humanistic theory is understood as a set of discursively structured, non-formalized, context-sensitive interpretive frameworks that mediate between research objects and disciplinary communities in the humanities.

Four Epistemic Challenges

Against this backdrop, LLMs introduce four epistemic challenges. First, although LLMs are often framed as theory-free, their training data embed theoretical commitments. Second, LLMs do not store knowledge as formal, rule-based symbolic representations of the world. Instead, they function as statistical, subsymbolic models where knowledge is implicitly encoded in training datasets, architectural components, and fine-tuning (Li et al. 2021; Holton 2023; Sudmann et al. 2023). As a consequence, LLMs seem to shift the relation between theory and data by replacing interpretive reasoning with statistical prediction. Third, their outputs may homogenize theoretical approaches by amplifying canonical, predominantly Western and Anglophone theories while marginalizing traditions from the so-called Global South (Li et al. 2021; Holton 2023; Sudmann et al. 2023). As a consequence, LLMs seem to shift the relation between theory and data by replacing interpretive reasoning with statistical prediction. Third, their outputs may homogenize theoretical approaches by amplifying canonical, predominantly Western and Anglophone theories while marginalizing traditions from the so-called Global South (Risam and Josephs 2021; Fiormonte et al. 2022) . This “epistemic bias” (Risam and Josephs 2021; Fiormonte et al. 2022) . This “epistemic bias” (Bueter 2022; Zermeño-Flores et al. 2024) might reduce the plurality of theories within AI-assisted workflows. Finally, because training data and fine-tuning remain inaccessible, DH scholars cannot easily assess which theoretical frameworks are encoded or excluded (except of (Bueter 2022; Zermeño-Flores et al. 2024) might reduce the plurality of theories within AI-assisted workflows. Finally, because training data and fine-tuning remain inaccessible, DH scholars cannot easily assess which theoretical frameworks are encoded or excluded (except of WIMBD by Elazar et al. 2023; Elazar et al. 2023; Patchscopes by Ghandeharioun et al. 2024). Together, these challenges have an impact on how to use LLMs in DH research.Ghandeharioun et al. 2024). Together, these challenges have an impact on how to use LLMs in DH research.

Probing Case Study

The case study investigates how three LLMs represent humanistic theory and theorizing. It examines which theoretical representations the models inherently possess and how these are augmented through a reflective probing framework. The models used are GPT5.1 (OpenAI), Gemini 3 (Google) and Mistral Large 3.

The models were selected to enable a comparative analysis across different institutional and infrastructural contexts. For the presentation, more up-to-date models may be used, which then comply with current standards.