The AI Revolution in Education
June 4, 2026
While there is widespread agreement that the new AI technology will revolutionize education, the exact nature of why it will have such a dramatic effect is often left unclarified. It is argued that understanding the nature of the AI educational challenge requires us to recognize that the overarching epistemic goal of education is to cultivate
Editorial Judgment and Machine Reasoning: A Longitudinal Case Study in Human-AI Collaboration on a Digital Edition
June 4, 2026
This paper presents findings from a collaboration between a scholar and a large language model (ChatGPT) during four months of sustained editorial work on a digital edition of a major nineteenth-century literary-philosophical text, documented in approximately eighty working sessions preserved from a live editorial workflow. The task involved creating 940 new Wikidata items for persons,
Lost – or Filtered – in Translation: Machine Translation, Large Language Models, and the Challenge of Hate Speech
June 4, 2026
Twenty years after its launch in 2006, Google Translate stands as the dominant specialised Machine Translation (MT) tool worldwide (Yao 2026), enabling communication across languages and cultures. Nevertheless, modern MT approaches mirror biases rooted in training data (Savoldi et al. 2025), an issue that becomes particularly relevant when the content being translated is hate speech
Human–AI Double Anchoring
June 4, 2026
Recent work in Human-Centered Computing has shown that people interacting with generative AI systems are susceptible to what psychology of judgment describes as the Anchoring Bias (Rosbach et al. 2026, Haag et al. 2024, Hosanagar et al. 2024). When users receive an initial estimate, explanation, or suggestion from a system, their subsequent judgments tend to
Normative Influence and Epistemic Injustice through AI-Generated Over-Idealised Bodies
June 4, 2026
This paper addresses how generative AI systems encode and reproduce cultural values andbiases, focusing on AI-generated bodies as a site where such norms become visible andaffectively powerful. It develops conceptual strands from an interdisciplinary project thatdesigned and tested a video-based intervention to raise awareness among young people aboutpotential harms of AI-generated idealised body images on
Mirror, Colleague, Rival: LLMs and the Reconfiguration of Epistemic Agency
June 4, 2026
Large language models (LLMs) are increasingly described as mirrors, colleagues, and rivals in academic research. This paper takes these three roles as phenomenological entry points rather than fixed categories. As mirrors, LLMs reflect sedimented linguistic, cultural, and epistemic patterns embedded in their training data, often revealing biases and structures that remain implicit in human discourse.
LLMs as Mirrors of Ignorance
June 4, 2026
Large language models (LLMs) are often described as mirrors of human language and culture. This paper argues that what they reflect is not neutral linguistic content, but socially structured forms of ignorance. I develop this claim through the concept of automated ignorance. AI-generated language appears meaningful and informative despite being produced without understanding, intention, or
Through the AI Looking-Glass: LLMs as Mirror, Window, and Lens in Philosophical Research
June 4, 2026
Focusing specifically on philosophical research, this paper rearticulates the metaphor of LLMs as “mirrors” of human thought by situating it within a conceptual triad: mirror, window, and lens. While LLMs undeniably reflect entrenched patterns of philosophical writing—its biases, stylistic conventions, and sedimented argumentative structures—they also function as windows that expose philosophers to forms of textual
Mirror mirror: Unmet presuppositions in LLM collaborator language
June 4, 2026
The AI mirror metaphor [6] foregrounds the threat of large language models (LLMs) absorbing human error and bias from their training sets and rehearsing them in their output. Key to the concern is that inherent limitations prevent LLMs from critically engaging with their training sets: in short, LLMs do not think [3]. In this talk
LLMs & Visual Monolingualism: How Large Language Models Dictate the Appearance of Digital Graphics
June 4, 2026
Although LLMs promise endless possibilities, online content is increasingly looking the same. In this talk, I argue that this unifying style is harmful; it tyrannises other graphic styles and, in doing so, may silence marginalised visual languages and the stories these languages can tell. Based on Jacques Derrida’s (1998) concept of the ‘monolingualism of the
Intralinguistic Concerns and LLMs
June 4, 2026
Many studies on the state-of-the-art AI technologies, such as LLMs, have found that they pose interesting challenges to a democratic state, such as the problem of unequal access to democratic participation and deliberation, unjustifiable control unfairly imposed on linguistic groups, and unequal cultural competence. As the analysis shows, LLMs offer clear benefits, such as enabling
Understanding disinformation in the era of generative artificial intelligence
June 4, 2026
The concept of disinformation has long been contested, but the rise of generative artificial intelligence (Gen AI) introduces new challenges. In my talk, I would like to address them and present a new concept of disinformation that could deal with them. Firstly, disinformation is usually defined as misleading information intentionally created and disseminated. The rise
Large Language Models as Unreliable Narrators: Toward a Postdigital Literacy
June 4, 2026
The public release of ChatGPT in 2022 brought large language models (LLMs) into everyday textual communication, accelerating the proliferation of machine-generated discourse online and offline. Because LLM outputs often circulate without clear markers of origin, distinctions between human and artificial authorship have become increasingly unstable, complicating judgments of textual reliability and trust. This article argues
LLMs as Annotator-Colleagues: Constrained Collaboration for Frame Detection in News Headlines
June 4, 2026
Large language models are increasingly used in humanities and communicationresearch, but their role remains ambivalent. They can support interpretive work, yet theycan also encourage the delegation of tasks that traditionally depend on human judgment.A central concern is that, when LLMs are used to replace rather than supportinterpretation, research becomes less grounded in shared deliberation and
Using Large Language Models to simulate human behavior for testing and improving study materials
June 4, 2026
In this talk I will discuss my experiences conducting role-playing exercises with larglanguage models (LLMs), where I put artificial intelligence in the shoes of potentialstudy participants. I will cover two different use cases, one involving an LLMinteracting with a chatbot we designed, and one where I interviewed an LLM actingas a hospital patient. In both
Why Philosophers Should Use GenAI
June 4, 2026
Philosophers tend to be sceptical about the use of GenAI for professional purposes, often emphasizing its tendency to hallucinate, flatten conceptual nuance, or encourage intellectual laziness. I will argue that philosophers should not only abandon this scepticism but actively use GenAI if they want to become better at their job. The argument builds on the
LLMs and Epistemological Alienation in Academic Knowledge Production
June 4, 2026
This contribution begins from the observation that large language models (LLMs) are commonly understood through two dominant metaphors. First, LLMs are frequently conceived as tools, a framing that mistakenly attributes to them a pre-industrial character. Second, they are anthropomorphized and described as ‘assistants’, ‘agents’, ‘co-pilots’. The point of departure for this contribution is that LLMs
Towards a Constructivist AI Pedagogy for Critical Thinking
June 4, 2026
Critical thinking is fundamental not only to academic success but also professional competence and informed citizenship. Yet emerging evidence suggests that regular reliance on generative AI (i.e., those digital technologies developed using large language models such as ChatGPT), particularly when performing analytical tasks, can result in lower proficiency during reflection and problem-solving; increased reliance on
Keynote: Should we count on LLMs in mathematics?
June 4, 2026
The project of Artificial Intelligence has to a large part been driven by questions like: Can “AI” play chess? Can “AI” take legal decisions? Can “AI” write poetry? How about maths? Intriguingly, while LLMs on the one hand have appeared to come up with correct solutions to hard Math Olympiad problems, at the same time
LLMs and Cognitive Deskilling
June 4, 2026
In this talk, I conceptualize and evaluate how using large language models (LLMs) transforms our writing practices as well as our cognition and critical thinking skills. Drawing on empirical research, I outline how people use LLMs for writing tasks and how this transforms their writing practices, cognition, and critical thinking skills. This shows that LLMs