LC-IAF: Low-Code AI Framework for Image Analysis in the Context of Digital Learning Materials

Ngan-Giang Ma, Kim-Quoc Nguyen, Son-Hai Tran

doi:https://doi.org/10.59232/DSM-V2I3P101

Research Article | Open Access | Download Full Text

Volume 2 | Issue 3 | Year 2025 | Article Id: DSM-V2I3P101 DOI: https://doi.org/10.59232/DSM-V2I3P101

LC-IAF: Low-Code AI Framework for Image Analysis in the Context of Digital Learning Materials

Ngan-Giang Ma, Kim-Quoc Nguyen, Son-Hai Tran

Received	Revised	Accepted	Published
06 Jun 2025	05 Jul 2025	02 Aug 2025	15 Aug 2025

Citation

Ngan-Giang Ma, Kim-Quoc Nguyen, Son-Hai Tran. “LC-IAF: Low-Code AI Framework for Image Analysis in the Context of Digital Learning Materials.” DS Journal of Multidisciplinary, vol. 2, no. 3, pp. 1-14, 2025.

Abstract

This paper proposes a Low-Code Image Analysis Framework (LC-IAF) to address teachers' rules when analyzing images in learning materials. This work is time-consuming and subjective, while AI is too complex. LC-IAF bridges the gap between AI's image analysis capabilities and pedagogical needs. This framework analyzes deep learning and computer vision to extract attributes of images in digital learning materials, providing a low-code interface that allows teachers without programming expertise to analyze and develop image content in teaching materials. Based on visual learning theory, LC-IAF provides actionable insights, supports design guidance, personalized learning, and automated assessment, empowering teachers to improve visual teaching materials.

Keywords

Image analysis, Low-code, Educational technology, Digital learning materials, Personalized learning.

References

[1] Rustam Abdrakhmanov et al., “Applying Computer Vision and Machine Learning Techniques in STEM-Education Self-Study,” International Journal of Advanced Computer Science & Applications, vol. 15, no. 1, pp. 1-9, 2024.

[CrossRef] [Google Scholar] [Publisher Link]

[2] Asma Khazaal Abdulsahib et al., “Artificial Intelligence Based Computer Vision Analysis for Smart Education Interactive Visualization,” Fusion: Practice and Applications, vol. 15, no. 2, pp. 245-260, 2024.

[CrossRef] [Google Scholar] [Publisher Link]

[3] Houichi Asma, and Sarnou Dallel, “Cognitive Load Theory and its Relation to Instructional Design: Perspectives of Some Algerian University Teachers of English,” Arab World English Journal (AWEJ), vol. 11, no. 4, pp.110-127, 2020.

[CrossRef] [Google Scholar] [Publisher Link]

[4] Archontia Foutsitzi, “Images in Educational Textbooks and Educational Audiovisual Media,” European Journal of Language and Literature Studies, vol. 4, no. 2, pp. 30-33, 2018.

[CrossRef] [Google Scholar] [Publisher Link]

[5] Ross Girshick et al., “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation,” 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, pp. 580-587, 2014.

[CrossRef] [Google Scholar] [Publisher Link]

[6] Hamza Ouhaichi, Vogel Bahtijar, and Daniel Spikol, “Exploring Design Considerations for Multimodal Learning Analytics Systems: An Interview Study,” Frontiers in Education, vol. 9, pp. 1-15, 2024.

[CrossRef] [Google Scholar] [Publisher Link]

[7] Famuyide Alaba Johnson et al., “Assessing the Awareness and Usability of Artificial Intelligence Software Among Secondary School Teachers,” Educational Perspectives, vol. 13, no. 1, pp. 1-10, 2024.

[Google Scholar] [Publisher Link]

[8] Maria Kandaurova, Daniel A. Skog, and Petra M. Bosch-Sijtsema, “The Promise and Perils of Low-Code AI Platforms,” MIS Quarterly Executive, vol. 23, no. 3, pp. 1-16, 2024.

[Google Scholar] [Publisher Link]

[9] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton, “Imagenet Classification with Deep Convolutional Neural Networks,” Communications of the ACM, vol. 60, no. 6, pp. 84-90, 2017.

[CrossRef] [Google Scholar] [Publisher Link]

[10] Evan Shelhamer, Jonathan Long, and Trevor Darrell, “Fully Convolutional Networks for Semantic Segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 4, pp. 640 - 651, 2017.

[CrossRef] [Google Scholar] [Publisher Link]

[11] Katerina Mangaroska et al., “Multimodal Learning Analytics to Inform Learning Design: Lessons Learned from Computing Education,” Journal of Learning Analytics, vol. 7, no. 3, pp. 79-97, 2020.

[CrossRef] [Google Scholar] [Publisher Link]

[12] Richard E. Mayer, The Cambridge Handbook of Multimedia Learning, 2^nd ed., Cambridge University Press, 2005.

[CrossRef] [Google Scholar] [Publisher Link]

[13] Mothwa Louis, “Smart Attendance Monitoring System Using Computer Vision,” Doctoral Dissertation, University of KwaZulu-Natal, Westville, 2019.

[Google Scholar] [Publisher Link]

[14] Fred Paas, Alexander Renkl, and John Sweller, “Cognitive Load Theory and Instructional Design: Recent Developments,” Educational Psychologist, vol. 38, no. 1, pp. 1-4, 2010.

[CrossRef] [Google Scholar] [Publisher Link]

[15] A. Paivio, “Dual Coding Theory and Education, Pathways to Literacy Achievement for High Poverty Children,” University of Michigan School of Education, 2006.

[Google Scholar]

[16] Aravind Sasidharan Pillai, “Student Engagement Detection in Classrooms through Computer Vision and Deep Learning: A Novel Approach Using YOLOv4,” Sage Science Review of Educational Technology, vol. 5, no. 1, pp. 87-97, 2022.

[Google Scholar] [Publisher Link]

[17] Jamal Raiyn, “The Role of Visual Learning in Improving Students' High-Order Thinking Skills,” Journal of Education and Practice, vol. 7, no. 24, pp. 115-121, 2016.

[Google Scholar] [Publisher Link]

[18] Raimundo da Silva Soares et al., “Integrating Students’ Real-Time Gaze in Teacher–Student Interactions: Case Studies on the Benefits and Challenges of Eye Tracking in Primary Education,” Applied Sciences, vol. 14, no. 23, pp. 1-17, 2024.

[CrossRef] [Google Scholar] [Publisher Link]

[19] Maria Trigka, and Elias Dritsas, “A Comprehensive Survey of Deep Learning Approaches in Image Processing,” Sensors, vol. 25, no. 2, pp. 1-46, 2025.

[CrossRef] [Google Scholar] [Publisher Link]

[20] V.P. Joshith, and S. Asha, “Multimodal Learning Analytics (MMLA) In Education-A Game Changer for Educators,” Indian Journal of Educational Technology, vol. 7, no. 1, pp. 329-344, 2025.

[Google Scholar] [Publisher Link]

[21] Rina Zviel-Girshin, “The Good and Bad of AI Tools in Novice Programming Education,” Education Sciences, vol. 14, no. 10, pp. 1-17, 2024.

[CrossRef] [Google Scholar] [Publisher Link]