Large language models are increasingly capable of producing creative texts, yet most studies on AI-generated poetry focus on English -- a language that dominates training data. In this paper, we examine the perception of AI- and human-written Czech poetry. We ask if Czech native speakers are able to identify it and how they aesthetically judge it. Participants performed at chance level when guessing authorship (45.8\% correct on average), indicating that Czech AI-generated poems were largely indistinguishable from human-written ones. Aesthetic evaluations revealed a strong authorship bias: when participants believed a poem was AI-generated, they rated it as less favorably, even though AI poems were in fact rated equally or more favorably than human ones on average. The logistic regression model uncovered that the more the people liked a poem, the less probable was that they accurately assign the authorship. Familiarity with poetry or literary background had no effect on recognition accuracy. Our findings show that AI can convincingly produce poetry even in a morphologically complex, low-resource (with respect of the training data of AI models) Slavic language such as Czech. The results suggest that readers' beliefs about authorship and the aesthetic evaluation of the poem are interconnected.

In August, 1918, Virginia Woolf spent a quiet stretch at Asheham, the country house that she and her husband, Leonard, rented in rural Sussex. "We've been practically alone, which has a very spiritual effect upon the mind," Woolf wrote to a friend, the socialite Lady Ottoline Morrell. After six months spent in such isolation, Woolf quipped, "I should be a kind of Saint, and Leonard an undoubted prophet. We should shed virtue on people as we walked along the roads." Alas, any pretensions to holiness had been dispelled by the arrival of house guests the previous evening: "I had such a bath of the flesh that I am far from unspotted this morning.

In this paper, we propose the beginnings of a formal framework for modeling narrative \textit{qua} narrative. Our framework affords the ability to discuss key qualities of stories and their communication, including the flow of information from a Narrator to a Reader, the evolution of a Reader's story model over time, and Reader uncertainty. We demonstrate its applicability to computational narratology by giving explicit algorithms for measuring the accuracy with which information was conveyed to the Reader and two novel measurements of story coherence.

Roland Barthes was speaking metaphorically when he suggested in 1967 that "the birth of the reader must be ransomed by the death of the author". But as artificial intelligence takes its first steps in fiction writing, it seems technology may one day start to make Barthes' metaphor all too real. AI is still some way off writing a coherent novel, as surreal experiments with Harry Potter show, but the future isn't so far away in Hollywood. According to Nadira Azermai, whose company ScriptBook is developing a screenwriting AI: "Within five years we'll have scripts written by AI that you would think are better than human writing." Self-promotion aside, if there is the possibility of a decent screenplay from ScriptBook's AI within five years, then a novel composed by machines can't be far behind.