## 8 Conclusions  
The e-learning sector has grown enormously during the COVID-19 pandemic, and therefore having artificial intelligence tools to support employees has become necessary. A virtual assistant that responds autonomously to a large group of users' requests can reduce the effort of human tutors and increase the scalability of an e-learning platform. This paper aims to describe the implementation of a chatbot that fits the company's needs. The first step to implement a chatbot consists of choosing the best platform on the market through an accurate and severe comparison. Thus, we presented four different experiments which compared, from different points of view, the ability of different cloud-based NLU platforms to recognize the underlying intent of sentences, referencing an Italian dataset from a real business class. After that, we integrated the chatbot in the assistant system, and we collected data concerning the production outcomes. Finally, we compared the performance of the system implemented with the results obtained by different NLU platforms. In our analysis, Dialogflow shows the better overall results in experiment A; however, Watson achieves a similar performance and, in some cases, it outperforms Dialogflow. Luis also performs well, but in no case does it provide better results than the services already mentioned. In experiment A, Wit.ai had the worst results. Experiment (B) proves that both Dialogflow and Watson can achieve impressive results even when trained with just 40% of the whole training set. Experiment (C) shows that Luis is the fastest platform to recognize the intention associated with an input sentence. The response time of all platforms is on average below 400 ms, which can be considered acceptable for a chat messaging system. In Experiment (D), we notice that Watson is the most robust service when the sentences contain spelling errors. In fact, Watson's error rate is less than 0.3 when 30 characters are changed, while the other platforms in the same case have an error rate superior to 0.5. In the light of the considerations above, in order to build a chatbot able to answer questions in Italian on an e-learning platform, the best NLU services are Watson and Dialogflow if we consider only raw performance in terms of F-score or Error rate. In the specific case of _Mega Italia Media_, the users of their e-learning platform are students with a wide variety of language skills. In this context, Watson is probably the best choice because it is the most robust service and the performance difference in terms of F-score is not statistically significant compared to Dialogflow, as shown in Section 6.1. The system implemented achieves satisfactory results, with 79.0% of the users' requests satisfied during an evaluation which lasted more than a year. This paper shows that developing a virtual assistant quickly, with little data, achieving satisfactory performance and building a robust solution is possible using a cloud-based NLU platform. The significant reduction in the development times is mainly due to the drastic reduction in data \begin{table}
\begin{tabular}{l l} \hline \hline
**Occurrence** & **Percentage** \\ \hline Success (Without Human Operator intention) & 78.9 \\ Disambiguation & 15.1 \\ Fail and Redirect to Human Tutor & 6.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the intention recognition during the performance evaluation from 1 November 2020 to 1 January 2022.  
collection and preprocessing effort, which usually represent crucial and time-consuming tasks in this type of projects. Although we presented an application in the context provided by _Mega Italia Media_, similar behavior and performance are obtained in other contexts, such as the realization of a chatbot for a mobility transport domain and an Italian broadcasting company. These case studies are in the Italian language, so in the future we are planning to define an exhaustive linguistic analysis and compare the performance in other domains, with other languages.