A Human-AI Collaborative Paradigm for Micro-Lecture Production Empowered by Multimodal AIGC: A Case Study in Computer Networks Education

DOI

10.2991/978-94-6239-766-8_5How to use a DOI?

Keywords

multimodal AIGC; human-AI collaboration; micro-lecture production; digital media design; instructional design; computer education; educational technology

Abstract

The education sector is going through rapid digital transformation right now. This trend has pushed up demand for scalable, high-quality micro-lectures that work as reusable digital learning resources. Multimodal artificial intelligence-generated content, or AIGC for short, has made notable progress in recent years. It opens up new possibilities to cut the cost, lower the technical barriers and reduce the time spent on making micro-lectures. But current educational applications for this technology are still mostly disconnected, built around single tools, and don’t align well with actual instructional design needs. To fix these gaps, this study puts forward a human-AI collaborative paradigm for micro-lecture production supported by multimodal AIGC. The paradigm has three closely connected layers. First is the instructional design layer, which sets clear pedagogical objectives and sorts out knowledge structures. Next is the multimodal AIGC execution layer, which generates scripts, visual assets, animations, audio and video clips. The last one is the human-AI collaboration layer, which supports prompt-based interaction, multi-level quality checks and iterative optimization.

We ran an empirical validation with the topic “DNS Working Principle” from a computer networks course. Two computer science teachers took part in this experiment. The AIGC toolchain covers several tools. DeepSeek-R1 is used to generate scripts, Midjourney v6 and Qwen-Image 2.0 handle conceptual diagram production, Kling AI makes dynamic demonstration content, iFlytek does audio synthesis, and Jianying is for video editing. We compared this method with the traditional fully manual production workflow. The proposed paradigm cut the average production time from 700 minutes to roughly 95 minutes. This works out to an 86.7% total time reduction, and efficiency went up around 7.4 times. The most obvious time cut shows up in visual asset generation. The average time saving in this part hit 89.2%.

Further qualitative analysis has found that this paradigm reshapes how teachers position their work. It shifts their role from someone who polishes teaching content step by step, to an architect who designs the whole instruction process and a creative director who guides the learning direction. In the work process supported by AIGC, teachers spend most of their energy on prompt engineering and scientific content review. They no longer need to sink time into repetitive, low-level technical operations. It’s worth noting that the content generated by AIGC sometimes has conceptual errors, for example, it may mix up recursive and iterative DNS resolution. This clearly shows that teacher review is still totally necessary to make sure the content is scientifically accurate and fits teaching needs. This study puts forward a systematic, workable framework for future practice. It turns scattered, casual use of AIGC tools into a human-centered digital media design process, and offers practical references for developing educational resources empowered by AI.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Cheng Zhong
AU  - Yanhui Wang
AU  - Huang Geng
AU  - Shaoqi Zhang
PY  - 2026
DA  - 2026/09/04
TI  - A Human-AI Collaborative Paradigm for Micro-Lecture Production Empowered by Multimodal AIGC: A Case Study in Computer Networks Education
BT  - Proceedings of the 2026 6th International Conference on Education, Information Management and Service Science  (EIMSS 2026)
PB  - Atlantis Press
SP  - 35
EP  - 52
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6239-766-8_5
DO  - 10.2991/978-94-6239-766-8_5
ID  - Zhong2026
ER  -