# Can Artificial Intelligence Write Like [PERSON]? An Evaluation Protocol for Spanish Microfiction
## 4 Results
### GrAlmes Evaluation of Human-Written Microfiction by the Experts Group
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{3}{c}{**Story Overview and Text Complexity**} & \multicolumn{3}{c}{**Technical**} & \multicolumn{3}{c}{**Editorial/Commercial**} & \multicolumn{3}{c}{**Microfiction Total Analysis**} \\ \hline
**\#** & **MF** & **AV** & **SD** & **MF** & **AV** & **SD** & **MF** & **AV** & **SD** & **MF** & **AV** & **SD** \\ \hline
1 & 4 & 4 & 1 & 1 & 3.2 & 0.8 & 4 & 4.3 & 1.1 & 4 & 3.4 & 0.8 \\ \hline
2 & 5 & 4 & 1 & 5 & 3 & 0.6 & 6 & 4.1 & 1.2 & 6 & 3.4 & 0.8 \\ \hline
3 & 6 & 3.5 & 2.1 & 6 & 2.8 & 0.3 & 5 & 3.3 & 2.5 & 5 & 3.2 & 1.4 \\ \hline
4 & 1 & 3 & 2.8 & 4 & 2.5 & 0.4 & 1 & 2.8 & 1.8 & 1 & 3 & 1.4 \\ \hline
5 & 2 & 3 & 2.8 & 2 & 2.5 & 1 & 2 & 1.5 & 0.7 & 2 & 2.2 & 1.1 \\ \hline
6 & 3 & 1 & 0 & 3 & 1.8 & 0.6 & 3 & 1 & 0 & 3 & 1.4 & 0.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Literary expert evaluation of microfiction generated by Monterroso and ChatGPT-3.5 by GrAlmes questionnaire section. Figure 14: Literary experts group’s evaluation of AI-generated microfiction (average by section). Figure 15: Line charts showing literary expert summarized AV and SD by GrAlmes questionnaire section. One of the most notable results in this evaluation concerns the interpretative engagement of readers. The highest-rated question, \"Does the text require your participation or cooperation to complete its form and meaning?\", received an average score (AV) of 4.3 with a standard deviation (SD)of 0.5. This suggests that the evaluated texts demand significant reader interaction, a crucial trait of literary complexity (see Table 16). Conversely, aspects related to innovation in language and genre were rated lower. The question \"Does it propose a new vision of the language itself?\" obtained an AV of 1.2, which was the lowest among all items, along with an SD of 1.2, indicating high variability in responses. Similarly, the question \"Does it propose a new vision of the genre it uses?\" received an AV of 1.6 and an SD of 0.6, further emphasizing the expert perception that the generated texts did not significantly redefine literary conventions. Regarding textual credibility, the question \"Is the story credible?\" was rated highly, with an AV of 4.2 and an SD of 0.7. This suggests that the narratives effectively maintain verisimilitude, an essential criterion for reader immersion. Additionally, evaluators were asked whether the texts reminded them of other literary works, yielding an AV of 3.4 and an SD of 0.8, indicating a moderate level of intertextuality. GrAlmes was also used to examine subjective aspects of reader appreciation. The questions \"Would you recommend it?\" and \"Would you like to read more texts like this?\" received AV scores of 2.7 and 2.8, respectively, with higher SD values (1.4 and 1.6), reflecting diverse expert opinions. Similarly, the willingness to offer the text as a gift scored an AV of 2.3 with an SD of 0.9, suggesting a moderate level of appreciation but not a strong endorsement. There are currently no existing state-of-the-art references available for direct comparison with the present study, highlighting the novelty of our approach. This study introduces an innovative evaluation protocol for microfiction validated by literary experts, and contributes to the field by assessing both human-written and AI-generated texts. The absence of direct SOTA comparisons is due to the lack of prior work that simultaneously addresses the evaluation of microfiction across these two distinct origins. This gap in the literature underscores the significance of our research, which seeks to establish a comprehensive framework for assessing literary quality that can be applied to both human-written and AI-generated works. Our focus on this specific research area is justified by the increasing prominence of AI in literary production and the need for reliable expert-validated evalu