Ethical Literary Criticism, founded by Chinese scholars, is a critical theory and methodology for reading and interpreting literary works from the perspective of ethics. Its central concept, ethical choice, operates under new conditions in the age of artificial intelligence. AI systems intervene deeply in human cognition and affect ethical choice by altering its cognitive conditions. Cognitive ethics is a critical concept advanced within Ethical Literary Criticism to examine the internal connection between cognition and ethics. Viewed through the lens of cognitive ethics, AI systems intervene in all three stages of ethical choice. At the stage of perception, the information a person relies on has already been filtered by the systems. At the stage of weighing, ethical considerations that resist quantification are either converted into numerical values or excluded. At the stage of decision, ethical choice no longer occurs in the concrete situation but has already occurred at the point of system design. Ethical choice accordingly takes on new characteristics, namely mediation, the dispersal of responsibility, and task-orientation. Ethical wisdom therefore comes to include metacognitive capacity, and it reaffirms ethical choice as a practical activity that advances moral cultivation.

Public concern is growing that AI is out of control, and about to consume much of culture, including a plethora of jobs. Nonetheless, many of these criticisms are superficial, due to a lack of focus on an important underlying concern. Specifically, an assessment of history is missing. Regularly, this technology is portrayed as riding the crest of history and delivering the future. As a result, an AI-dominated future seems almost inevitable. This outlook rests on an autonomous image of history that is questionable, due to recent changes in philosophy. The aim of this paper is to illustrate this philosophical change, along with the implications for understanding history and the future of AI. This shift in orientation will enable persons to supply more direction to this technology.

This paper presents a methodological framework for the digital curation of the post-Tang Chinese lyric — the ci, qu, and sanqu composed for musical realization between the late Tang and the end of the Qing (ninth century to 1911). The framework coordinates three mutually constraining annotation layers: a textual layer containing the canonical poem, variants, and the commentarial tradition; a multimodal layer linking the text to its notated music — chiefly the jianzipu (减字谱) tablature of the qin-song repertory, alongside the suzipu pitch notation of the sung ci — and to its calligraphic carriers (ink tracings, rubbings, engraved transcriptions); and a Six-W provenance layer capturing who wrote, addressed, and recited the poem, what it sets out to do, when and where it was composed, why it was occasioned, and how it was transmitted. Situated against contemporary annotation and provenance models (TEI, W3C PROV-O, FRBR, CIDOC-CRM, Dublin Core, and the W7 model), the paper articulates an AI-assisted annotation pipeline — NLP for the textual layer, computer vision and handwriting recognition for the calligraphic layer, optical music recognition for the score layer — under a human-in-the-loop protocol that preserves the philological prerogative of interpretive judgment. The argument is illustrated by one fully worked case drawn from that dataset, the qin song Gu Yuan (古怨) in Jiang Kui’s Baishidaoren Gequ, whose surviving editions support a documented evidential gap that the schema is designed to carry rather than resolve. The framework is not a proposal awaiting implementation: the pipeline described here has been built and run across all three annotation layers, and the resulting dataset comprises approximately 1.3 million post-Tang lyric compositions, each carrying a Six-W provenance record, with the full three-layer treatment applied wherever a non-textual witness survives. The dataset is not yet publicly released; the schema, the pipeline, and the epistemic principles are set out here in full so that the data may be assessed independently of the release schedule.

Generative AI has raised difficult questions about how copyright systems should respond when creative works are used for AI training. This article examines two recent developments in Northern Europe that bring compensation into that debate. In Norway, Kopinor and the National Library of Norway have developed a collective licensing arrangement for the use of newspaper content in AI training. In Denmark, the collective management organization Koda has turned to litigation against the AI music platform Suno, seeking transparency and remuneration for rights holders. Although the two cases rely on different legal mechanisms, they raise a common question about how the value generated through AI training should be shared. Comparing these developments with the U.S. experience, this article considers whether compensation may become a more important part of AI copyright governance.

This paper presents a case study of marking up TEI (Text Encoding Initiative) documents using large language models. We detail our experiments using LLMs for named entity recognition and entity linkage in a corpus of TEI documents. The corpus consists of four journals co-edited by the Swiss-German theologian, Karl Barth (1886–1968). We aimed to enrich the TEI markup by adding XML elements to mark up entities and attributes to link to QIDs on Wikidata. We experimented with using both cloud-based frontier models and local open-source models to enrich a subset of 34 articles from the corpus. After analyzing the outcome using both human reviewers and automated analysis, we indicate where our methodology proved successful and where we encountered problems. We conclude that LLMs can perform named entity recognition and linking in TEI documents, but that the combined financial and labor cost of scaling this procedure to the full corpus would be relatively high without optimizing our current pipeline.