Proactive chatbot framework based on the PS2CLH model: an AI-Deep Learning chatbot assistant for students

Arlindo, Almada, Qicheng, Yu and Preeti, Patel (2022) Proactive chatbot framework based on the PS2CLH model: an AI-Deep Learning chatbot assistant for students. In: Intelligent Systems Conference (IntelliSys) 2022, 1 - 2 September 2022, Amsterdam, The Netherlands. (Submitted)

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Proactive Chatbot Framework based on the PS2CLH model An AI-Deep Learning chatbot assistant for students.pdf - Accepted Version

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Official URL: https://saiconference.com/IntelliSys

Abstract / Description

Nowadays, universities are using more technologies dealing with students’ interactions. The chatbot supported with Artificial Intelligence (AI) – Deep Learning (DL) technology exhibited a better ability and efficiency in various assistant situations. However, the effectiveness of the education chatbot is still not satisfactory. This paper proposes a new chatbot framework that integrated students’ learning profiles and enhanced chatbot components to improve student interaction. The new chatbot framework uses knowledge from the PS2CLH model and AI - DL to build a proactive chatbot for assisting students’ learning on their academic subjects and controllable learning factors. One of the principal novelties of the chatbot framework lies in the student lecturer/assistant facilitator. The proactive chatbot applies multimodality to students’ learning process to retain students’ attention and explain the content in different ways using text, image, video, and audio to assist students and improve their learning experience effectively. Furthermore, the chatbot proactively suggests controllable learning factors for students to work on, improving their academic performance. The testing results demonstrated that the proactive chatbot offered sound accuracy and more effective learning support than other chatbots.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: AI-Deep Learning chatbot; framework; students’ assistant; Artificial Intelligence (AI); Deep Learning (DL) technology
Subjects: 000 Computer science, information & general works
Department: School of Computing and Digital Media
Depositing User: Qicheng Yu
Date Deposited: 17 May 2022 10:19
Last Modified: 17 May 2022 10:19
URI: http://repository.londonmet.ac.uk/id/eprint/7643

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