Intelligent Tutoring System Framework and Learning Platform Development

Song-Hwan Kwon; Yun-Ho Ko; Chung-Song Ko; Jong-Nam Rim; Sin-Hye Jang; Yun-Sok Ham; Ha-Gyong Kim; Hyon-Il Son

doi:doi:10.11648/j.sf.20250604.16

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Intelligent Tutoring System Framework and Learning Platform Development

Song-Hwan Kwon^*

, Yun-Ho Ko, Chung-Song Ko, Jong-Nam Rim, Sin-Hye Jang, Yun-Sok Ham, Ha-Gyong Kim, Hyon-Il Son

Published in Science Frontiers (Volume 6, Issue 4)

Received: 16 November 2025 Accepted: 3 December 2025 Published: 30 December 2025

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Abstract

As a general-purpose intelligent tutoring system framework for the development of intelligent tutoring system for their subjects, any subject teachers at universities and schools at all levels developed a platform for the operation of editors and intelligent tutoring systems. The knowledge graph is used to extend the subject graph, hyper-concept graph, basic concept graph, subconcept and help-concept graph. The intelligent tutoring system is designed to emulate the teaching style of human teachers, but to provide individual teaching to each individual student. Therefore, the skills included lecture slide display, video playback, comment display, comment reading by speech synthesizer, student recognition by face recognizer, question presentation and response processing, response results processing by natural language processing, and student’s learning performance prediction et al. Hidden Markov models are known as models that can probabilistically and statistically model the learning path and history of students through time intervals, estimate learning states, and predict learning outcomes. The developed intelligent tutoring system platform estimated and predicted the learning status of the student by the Hidden Markov model and then generated the learning path using a specific algorithm. We also directly generated the learning path using the Markov decision process model. The results of the analysis of the results, using the general-purpose intelligent tutoring system development framework, were very positive, while developing the intelligent tutoring system for AI subjects and allowing students to use it.

Published in	Science Frontiers (Volume 6, Issue 4)
DOI	10.11648/j.sf.20250604.16
Page(s)	170-200
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Tutor Softbots, Intelligent Tutoring Systems, Individual Instruction, Knowledge Graph, Intelligent Tutoring System Platform

References

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APA Style

Kwon, S., Ko, Y., Ko, C., Rim, J., Jang, S., et al. (2025). Intelligent Tutoring System Framework and Learning Platform Development. Science Frontiers, 6(4), 170-200. https://doi.org/10.11648/j.sf.20250604.16

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ACS Style

Kwon, S.; Ko, Y.; Ko, C.; Rim, J.; Jang, S., et al. Intelligent Tutoring System Framework and Learning Platform Development. Sci. Front. 2025, 6(4), 170-200. doi: 10.11648/j.sf.20250604.16

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AMA Style

Kwon S, Ko Y, Ko C, Rim J, Jang S, et al. Intelligent Tutoring System Framework and Learning Platform Development. Sci Front. 2025;6(4):170-200. doi: 10.11648/j.sf.20250604.16

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@article{10.11648/j.sf.20250604.16,
  author = {Song-Hwan Kwon and Yun-Ho Ko and Chung-Song Ko and Jong-Nam Rim and Sin-Hye Jang and Yun-Sok Ham and Ha-Gyong Kim and Hyon-Il Son},
  title = {Intelligent Tutoring System Framework and Learning Platform Development},
  journal = {Science Frontiers},
  volume = {6},
  number = {4},
  pages = {170-200},
  doi = {10.11648/j.sf.20250604.16},
  url = {https://doi.org/10.11648/j.sf.20250604.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sf.20250604.16},
  abstract = {As a general-purpose intelligent tutoring system framework for the development of intelligent tutoring system for their subjects, any subject teachers at universities and schools at all levels developed a platform for the operation of editors and intelligent tutoring systems. The knowledge graph is used to extend the subject graph, hyper-concept graph, basic concept graph, subconcept and help-concept graph. The intelligent tutoring system is designed to emulate the teaching style of human teachers, but to provide individual teaching to each individual student. Therefore, the skills included lecture slide display, video playback, comment display, comment reading by speech synthesizer, student recognition by face recognizer, question presentation and response processing, response results processing by natural language processing, and student’s learning performance prediction et al. Hidden Markov models are known as models that can probabilistically and statistically model the learning path and history of students through time intervals, estimate learning states, and predict learning outcomes. The developed intelligent tutoring system platform estimated and predicted the learning status of the student by the Hidden Markov model and then generated the learning path using a specific algorithm. We also directly generated the learning path using the Markov decision process model. The results of the analysis of the results, using the general-purpose intelligent tutoring system development framework, were very positive, while developing the intelligent tutoring system for AI subjects and allowing students to use it.},
 year = {2025}
}

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TY  - JOUR
T1  - Intelligent Tutoring System Framework and Learning Platform Development
AU  - Song-Hwan Kwon
AU  - Yun-Ho Ko
AU  - Chung-Song Ko
AU  - Jong-Nam Rim
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AU  - Yun-Sok Ham
AU  - Ha-Gyong Kim
AU  - Hyon-Il Son
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DO  - 10.11648/j.sf.20250604.16
T2  - Science Frontiers
JF  - Science Frontiers
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EP  - 200
PB  - Science Publishing Group
SN  - 2994-7030
UR  - https://doi.org/10.11648/j.sf.20250604.16
AB  - As a general-purpose intelligent tutoring system framework for the development of intelligent tutoring system for their subjects, any subject teachers at universities and schools at all levels developed a platform for the operation of editors and intelligent tutoring systems. The knowledge graph is used to extend the subject graph, hyper-concept graph, basic concept graph, subconcept and help-concept graph. The intelligent tutoring system is designed to emulate the teaching style of human teachers, but to provide individual teaching to each individual student. Therefore, the skills included lecture slide display, video playback, comment display, comment reading by speech synthesizer, student recognition by face recognizer, question presentation and response processing, response results processing by natural language processing, and student’s learning performance prediction et al. Hidden Markov models are known as models that can probabilistically and statistically model the learning path and history of students through time intervals, estimate learning states, and predict learning outcomes. The developed intelligent tutoring system platform estimated and predicted the learning status of the student by the Hidden Markov model and then generated the learning path using a specific algorithm. We also directly generated the learning path using the Markov decision process model. The results of the analysis of the results, using the general-purpose intelligent tutoring system development framework, were very positive, while developing the intelligent tutoring system for AI subjects and allowing students to use it.
VL  - 6
IS  - 4
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Author Information

Song-Hwan Kwon

Department of Information Science, University of Science, Pyongyang, Democratic People’s Republic of Korea

Contact Email

http://orcid.org/0009-0005-2162-081X
Yun-Ho Ko

Department of Information Science, University of Science, Pyongyang, Democratic People’s Republic of Korea
Chung-Song Ko

Department of Information Science, University of Science, Pyongyang, Democratic People’s Republic of Korea

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Jong-Nam Rim

Department of Information Science, University of Science, Pyongyang, Democratic People’s Republic of Korea

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Sin-Hye Jang

Department of Information Science, University of Science, Pyongyang, Democratic People’s Republic of Korea

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Yun-Sok Ham

Institute of Information Technology, University of Science, Pyongyang, Democratic People’s Republic of Korea

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Ha-Gyong Kim

Department of Information Science, University of Science, Pyongyang, Democratic People’s Republic of Korea

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Hyon-Il Son

Information Department, Sinuiju College of Industrial Technology, Sinuiju, Democratic People’s Republic of Korea

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APA Style

Kwon, S., Ko, Y., Ko, C., Rim, J., Jang, S., et al. (2025). Intelligent Tutoring System Framework and Learning Platform Development. Science Frontiers, 6(4), 170-200. https://doi.org/10.11648/j.sf.20250604.16

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ACS Style

Kwon, S.; Ko, Y.; Ko, C.; Rim, J.; Jang, S., et al. Intelligent Tutoring System Framework and Learning Platform Development. Sci. Front. 2025, 6(4), 170-200. doi: 10.11648/j.sf.20250604.16

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AMA Style

Kwon S, Ko Y, Ko C, Rim J, Jang S, et al. Intelligent Tutoring System Framework and Learning Platform Development. Sci Front. 2025;6(4):170-200. doi: 10.11648/j.sf.20250604.16

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@article{10.11648/j.sf.20250604.16,
  author = {Song-Hwan Kwon and Yun-Ho Ko and Chung-Song Ko and Jong-Nam Rim and Sin-Hye Jang and Yun-Sok Ham and Ha-Gyong Kim and Hyon-Il Son},
  title = {Intelligent Tutoring System Framework and Learning Platform Development},
  journal = {Science Frontiers},
  volume = {6},
  number = {4},
  pages = {170-200},
  doi = {10.11648/j.sf.20250604.16},
  url = {https://doi.org/10.11648/j.sf.20250604.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sf.20250604.16},
  abstract = {As a general-purpose intelligent tutoring system framework for the development of intelligent tutoring system for their subjects, any subject teachers at universities and schools at all levels developed a platform for the operation of editors and intelligent tutoring systems. The knowledge graph is used to extend the subject graph, hyper-concept graph, basic concept graph, subconcept and help-concept graph. The intelligent tutoring system is designed to emulate the teaching style of human teachers, but to provide individual teaching to each individual student. Therefore, the skills included lecture slide display, video playback, comment display, comment reading by speech synthesizer, student recognition by face recognizer, question presentation and response processing, response results processing by natural language processing, and student’s learning performance prediction et al. Hidden Markov models are known as models that can probabilistically and statistically model the learning path and history of students through time intervals, estimate learning states, and predict learning outcomes. The developed intelligent tutoring system platform estimated and predicted the learning status of the student by the Hidden Markov model and then generated the learning path using a specific algorithm. We also directly generated the learning path using the Markov decision process model. The results of the analysis of the results, using the general-purpose intelligent tutoring system development framework, were very positive, while developing the intelligent tutoring system for AI subjects and allowing students to use it.},
 year = {2025}
}

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TY  - JOUR
T1  - Intelligent Tutoring System Framework and Learning Platform Development
AU  - Song-Hwan Kwon
AU  - Yun-Ho Ko
AU  - Chung-Song Ko
AU  - Jong-Nam Rim
AU  - Sin-Hye Jang
AU  - Yun-Sok Ham
AU  - Ha-Gyong Kim
AU  - Hyon-Il Son
Y1  - 2025/12/30
PY  - 2025
N1  - https://doi.org/10.11648/j.sf.20250604.16
DO  - 10.11648/j.sf.20250604.16
T2  - Science Frontiers
JF  - Science Frontiers
JO  - Science Frontiers
SP  - 170
EP  - 200
PB  - Science Publishing Group
SN  - 2994-7030
UR  - https://doi.org/10.11648/j.sf.20250604.16
AB  - As a general-purpose intelligent tutoring system framework for the development of intelligent tutoring system for their subjects, any subject teachers at universities and schools at all levels developed a platform for the operation of editors and intelligent tutoring systems. The knowledge graph is used to extend the subject graph, hyper-concept graph, basic concept graph, subconcept and help-concept graph. The intelligent tutoring system is designed to emulate the teaching style of human teachers, but to provide individual teaching to each individual student. Therefore, the skills included lecture slide display, video playback, comment display, comment reading by speech synthesizer, student recognition by face recognizer, question presentation and response processing, response results processing by natural language processing, and student’s learning performance prediction et al. Hidden Markov models are known as models that can probabilistically and statistically model the learning path and history of students through time intervals, estimate learning states, and predict learning outcomes. The developed intelligent tutoring system platform estimated and predicted the learning status of the student by the Hidden Markov model and then generated the learning path using a specific algorithm. We also directly generated the learning path using the Markov decision process model. The results of the analysis of the results, using the general-purpose intelligent tutoring system development framework, were very positive, while developing the intelligent tutoring system for AI subjects and allowing students to use it.
VL  - 6
IS  - 4
ER  -

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