# Chatots for Cultural Venues: A Topic-Based Approach
## 2 Materials and Methods
### Mapping of Structured Information to Google DialogFlow Elements
#### 2.3.2 Automating Mapping of Data to DialogFlow Information Elements
Afterwards, participants were presented with the process of mapping narratives and exhibits to intents, including the chunking of lengthy descriptions into multiple, sequentially connected intents. Participants were allowed to ask questions and request clarifications and were also given the opportunity to practice with the creation of intents. The duration of this introduction ranged between 23 and 36 min. Afterwards, each participant was asked to create a new narrative and insert four exhibits into it. The data needed for the creation of the narratives and the exhibits (title and description) were made available as word processor files, and participants could copy and paste text from the word processor file to the DialogFlow console. Users were asked to think aloud during the chatbot creation process and were permitted to turn to the interviewer for help. The time that each participant needed to perform each individual step and the whole process was recorded, and after the process was completed, participants filled in a short questionnaire (c.f. Table 3), rating aspects in a 1-5 Likert scale, and were asked to express their opinion about the process. The time needed for the participants to complete the task varied significantly, ranging from 28 min (for the computer science postgraduate student) to 1 h and 10 min. This difference accounts not only for the proficiency in using the (browser-based) DialogFlow interface but also to the practices used for copying text between the word processor and the browser; in particular, the computer science postgraduate student used the key combination ALT + TAB to rapidly switch between the word processor and the browser, whereas other participants used the mouse, which resulted in the increased time. It is worth noting that the time needed to complete the entry of an exhibit was found to reduce up to 23% between the entry of the first exhibit and the entry of the last (fourth) exhibit, indicating that the process is quickly learnable. Users responded that the process was fairly straightforward (average: 4.25, min: 3, and max: 5) and learnable (average: 4.5, min: 4, and max: 5). The user interface received \begin{table}
\begin{tabular}{r} \hline
**Question** \\ \hline The chatbot creation process was straightforward \\ \hline The chatbot creation process was easy to learn and memorize \\ \hline The user interface was convenient/easy to use \\ \hline The chatbot creation process was interesting and satisfying \\ \hline \end{tabular}
\end{table} 
Table 3: Questionnaire for assessing the subjective aspects of the chatbot creation process. lower grades (average: 3.25, min: 3, and max: 4), mainly due to the fact that a number of fields in the intent modeling forms were unused and this encumbered the whole process. All users noted that the process was of low interest (average: 1.75, min: 1, and max: 3) and were positive towards the prospect of being assisted by an automation procedure. Only few errors, between 0 and 3 per subject, were observed; about 60% of the errors were related to entering information in the wrong user interface field, and the remaining 40% was related to omissions (i.e., some information was not entered in the DialogFlow console). Notably, most errors were observed in the creation of the narrative and the first two exhibits, i.e., in the first tasks that the users performed, while in the modeling of the last two exhibits only two errors were observed, further supporting the subjective view that the system is characterized by high learnability. #### 3.2.2 Evaluation of Chatbot Use

The second part of the evaluation was targeted toward the use of the chatbot by end-users. For the determination of the aspects to be evaluated and the formulation of the pertinent questionnaire, relevant chatbot usage surveys were consulted [7; 49; 50; 51; 52]; the aspects of ease of use, dialogue fluidity [49; 53], user interface intuitiveness and pleasantness, and user willingness to use the chatbot were discerned to be measured.