# Can Artificial Intelligence Write Like [PERSON]? An Evaluation Protocol for Spanish Microfiction
## 4 Results
### GrAlmes Evaluation of Human-Written Microfiction by the Experts Group
The most consistent responses pertained to the credibility of the stories (AV = 3.1, SD = 1.0), indicating strong agreement among participants on the narratives' believability. This suggests that regardless of their other literary attributes, the microfictions maintained a sense of realism that resonated with readers. The question regarding whether the text required the reader's participation or cooperation to complete its form and meaning received the highest average rating (AV = 3.6, SD = 1.3). This suggests that the generated microfictions engaged actively the readers, requiring interpretation and involvement in order to fully grasp their meaning. The relatively low SD indicates moderate consensus on this aspect. Questions concerning literary innovation, e.g., whether the texts proposed a new vision of language (AV = 2.7, SD = 1.3), reality (AV = 2.6, SD = 1.4), or genre (AV = 2.4, SD = 1.4), showed moderate variation in responses. This suggests that while some readers perceived novelty in these areas, others did not find the texts to be particularly innovative. Similarly, answers to the question of whether the texts reminded readers of other books (AV = 3.2, SD = 1.4) present a comparable level of divergence in opinions. The lowest-rated questions relate to the desire to read more texts of this nature (AV = 2.3), readers' willingness to recommend them (AV = 2.2), and their inclination to gift them to others (AV = 2.1), all with SD = 1.4. These results suggest that while the generated microfictions may have some engaging qualities, they do not strongly motivate further exploration or endorsement. Interestingly, the question about whether the texts propose interpretations beyond the literal received the highest standard deviation (SD = 1.6, AV = 2.9). This indicates significant variation in responses, suggesting that some readers found deeper layers of meaning while others perceived the texts as more straightforward. The intra-class correlation coefficient (ICC) analysis of the GrAlmes answers (see Table 13 and Figure 12) revealed varying degrees of reliability among the 16 literature enthusiast raters when assessing texts generated by Monterroso and ChatGPT-3.5. Three questions demonstrated poor reliability (ICC < 0.50), reflecting high variability in responses, with Question 8 exhibiting a negative ICC (\(-\)0.44) suggesting severe inconsistency, possibly due to misinterpretation or extreme subjectivity. In contrast, Questions 5 and 6 showed excellent reliability (ICC > 0.90), indicating strong inter-rater agreement, while Questions 9, 11, 12, and 13 displayed moderate reliability (ICC 0.6--0.70), implying acceptable but inconsistent consensus. These findings highlight the need to refine ambiguous or subjective questions in order to improve evaluative consistency in microfiction assessment. Figure 11: Line charts of literature enthusiasts’ summarized AV and SD by GrAlmes questionnaire section. This study assessed microfiction based on ability to propose interpretations beyond the literal meaning, finding notable differences between texts generated by the Monterroso (MFs 1-3) and ChatGPT-3.5 (MFs 4-6) models. Monterroso's MF 2 had the highest average score (AV = 3.2), showing stronger ability to suggest multiple interpretations, while ChatGPT-3.5's MF 4 had the lowest score (AV = 2.4), indicating limited interpretive depth. Standard deviation values were consistent across all MFs (1.5 to 1.7), showing moderate response variability among the literature enthusiasts. Thus, while certain MFs were seen as being more interpretively rich, the response variability was similar for all texts. The technical quality of the MFs was assessed through questions related to credibility (Question 5), reader participation (Question 6), and innovation in reality, genre, and language (Questions 7-9). MF 6, generated by ChatGPT-3.5, scored highest in credibility