**5. Conclusion**

proposed system helps students to manage their teamwork project. After the session with the

The system is also able to design presentations for specific learners. Notably, different students have different learning abilities; therefore, the system is able to compute a favorable learning style for each student. The teacher monitors the progress of each student through feedback about how each student performed in the sessions. This facilitates appropriate grading. Also, the virtual assistant is able to point out areas of the course that need to be explored further to enhance learning by providing additional reference materials to a topic. Also, the teacher is able to identify which students need extra help using the feedback provided by the system.

The proposed architecture is a reliable virtual assistant website that not only helps teachers and students to do their tasks in a shorter time but also allows them to coordinate their work.

The underlying technical implementation of the virtual assistant system starts with creating use cases for the product. The identification of virtual assistants and the underlying technology are required for moving ahead with the implementation of the proposed website [10]. The following technical specifications have been identified for building the virtual assistant website:

• Eclipse, Geany or your preferred interface for coding on Python Virtual COM Port.

To facilitate interaction between the virtual assistant website and the user, a software known as Wit.ai is installed. Wit.ai offers a perfect combination of voice recognition, and subsequent machine learning in the context of developers. The software offers services that concentrate on converting verbal commands into text. Moreover, Wit.ai has the capability of understanding the commands that are said. The most sophisticated forms of Wit.ai can be programmed to understand commands whose prior understanding was scant or non-existent. This is crucial in the educational context since learners tend to understand at varying paces. The extensive capability of Wit.ai software to improve the interactivity of virtual assistant website can be verified by the fact that it has been incorporated by a number of notable social media net-

Clarifai is another service that can be added to the virtual assistant website to improve its interactivity. Clarifai is a service geared towards AI, and it possesses the ability to decode contents that is in an image and video format. Another strength associated with Clarifai is that it possesses a deep learning engine that improves with its usage [10]. The tool is of paramount importance when there is a need to make improvements in the AI prototype and grant it the

system, a student is provided with the feedback about his progress.

180 Machine Learning - Advanced Techniques and Emerging Applications

**4. Technical implementation**

• BitVoicer: Speech recognition. • Python 2.7: Coding language

works, such as Facebook [10].

capability of seeing and recognizing objects.

Software used:

Machine learning with AI has opened incredible possibilities in various fields. This is especially the case in terms of the education sector and education-related fields. This means that future learning environments are likely to be highly personalized, with the ability to help learners realize their utmost potential in the most fulfilling way. There will be a steady adoption of machine learning in various areas of concern for educational technology. In the initial stages, its impact will not be clearly apparent or significant to the end user. Despite this, teachers have started to see how tasks can be simplified and more effectively completed through the employment and application of machine learning technologies. The advances made in adopting machine learning into education sector have significantly saved teachers' time in both the classroom and non-classroom-related activities. Stakeholders have welcomed this unprecedented benefit, as it makes learning easier and more appealing.

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[6] Lisetti C, Amini R, Yasavur U. Now all together: Overview of virtual health assistants emulating face-to-face health interview experience. KI – Künstliche Intelligenz.

[7] Bell B. Supporting educational software design with knowledge-rich tools. In Authoring Tools for Advanced Technology Learning Environments. Springer Netherlands. 2003.

[8] Haynes M, Anagnostopoulou K. Supporting educational software design with knowledge-rich tools. In Authoring Tools for Advanced Technology Learning Environments.

[9] Brinson JR. Learning outcome achievement in non-traditional (virtual and remote) versus traditional (hands-on) laboratories: A review of the empirical research. Computers

[10] Padró L, Stanilovsky E. Towards wider multilinguality. In: Proceedings of the 8th

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2015;**29**(2):161-172. DOI: 10.1007/s13218-015-0357-0

The future work on machine learning, especially in the education context, shall witness the development of more sophisticated AI tools. There are multiple prospects for designing complex chatbots that will improve the sophistication of virtual assistants. This development shall foster more human interactions that will replace emails and text messages. Already, plans are underway for developing online virtual assistants named "Amy" or "Andrew" at x.ai to schedule meetings with both tutors and learners. AI coupled with machine learning that incorporates deep learning and natural language processing is projected to go a level higher by incorporating more sophisticated systems laced with capabilities to adapt, learn and predict systems with utmost autonomy. The future works on these systems shall incorporate a combination of advanced algorithms and embedded massive data sets.
