Adaptive e-Learning AI-Powered Chatbot based on Multimedia Indexing
Open accessAbstract
With the rapid evolution of e-learning technology, the multiple sources of information become more and more accessible. However, the availability of a wide range of e-learning offers makes it difficult for learners to find the right content for their training needs. In this context, our paper aims to design an e-learning AI-powered Chatbot allowing interaction with learners and suggesting the e-learning content adapted to their needs. In order to achieve these objectives, we first analysed the e-learning multimedia content to extract the maximum amount of information. Then, using Natural Language Processing (NLP) techniques, we introduced a new approach to extract keywords. After that, we suggest a new approach for multimedia indexing based on extracted keywords. Finally, the Chatbot architecture is realized based on the multimedia indexing and deployed on online messaging platforms. The suggested approach aims to have an efficient way to represent the multimedia content based on keywords. We compare our approach with approaches in literature and we deduce that the use of keywords on our approach result on a better representation and reduce time to construct multimedia indexing. The core of our Chatbot is based on this indexed multimedia content which enables it to look for the information quickly. Then our designed Chatbot reduce response time and meet the learner’s need.
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The study by Janati et al. (2020) reported high effectiveness.
“| (Janati et al., 2020) | Peer agent, chatbot driven | N/A | High effectiveness |”
From Interacting with educational chatbots: A systematic review (2023) · cites this paper as Janati et al., 2020
The Results report improved multimedia indexing, including an 8% gain in validation-index performance and a 70% reduction in execution time. They do not establish high effectiveness for the chatbot as a whole; the Recommendation section says its functionality is limited for general learner questions. Page numbers are unavailable in our copy. (LLM, conf=0.86)
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Cited by
Sorted by citations
Interacting with educational chatbots: A systematic review
Kuhail, M. A., Alturki, N., Alramlawi, S., & Alhejori, K. (2023) · Education and Information Technologies · doi:10.1007/s10639-022-11177-3 · Cited by 841 · see citing works
Williamson, S. M., & Prybutok, V. (2024) · Information · doi:10.3390/info15060299 · Cited by 146 · see citing works
- Authors
- Janati, S. E., Maach, A., & El, D.
- Affiliations
- Mohammed V University (MA)
- Venue
- International Journal of Advanced Computer Science and Applications
- Published
- 2020
- Volume
- 11
- Issue
- 12
- ISSN
- 2156-5570 (ISSN-L)
- 2158-107X
- OpenAlex
- W3119013717
- Cited by
- 20
- References
- 20
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 6 · 2023: 5 · 2022: 2 · 2021: 1
- FWCI
- 0.79
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- International Journal of Advanced Computer Science and Applications (publishedVersion, cc-by)
- International Journal of Advanced Computer Science and Applications (submittedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Information Systems › Multimedia Learning Systems
- Keywords
- adaptive e-learning, multimedia indexing, keyword extraction, content recommendation, student needs
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