What is this article talking about?
This article summarizes the knowledge management process behind the "Lecturer's Agent Workflow". It starts from the production of presentations and extends to how a lecturer can turn the materials he usually sees, student questions, community feedback, pre-class questionnaires, and post-class verbatim drafts into materials that can be directly used when preparing for the next class. Who is
· Lecturers, consultants, teachers, and content creators often have to teach the same set of knowledge to different audiences
· Usually there are a lot of materials, but when it comes to preparing lessons, I still can’t find or connect them.
· People who want to use Agent to help prepare lessons, but don’t want to stop at “help me make a presentation”
· An eight-stage knowledge management process for lecturer preparation
· Understand how to connect the inspiration pool, pre-class questionnaire, post-class reproduction, and cross-curricular reuse
· A way to turn every lecture into reusable material for the next time
Eight-stage process: from material to reusable system
This class breaks down the instructor’s workflow into eight sections. Each paragraph will generate knowledge assets that can be reused next time, not only to serve the current task.
Use multi-agent crossover to allow information from different angles to be found first, rather than just following a single context.
Whenever I see cases, feedback, and questions, write them down and use the labels to connect them to future courses.
Not only ask what you want to learn, but also what step you are stuck on now, so that you can have a real focus in preparing lessons.
Promotional copywriting, storyboards, and message feedback will in turn calibrate the formal courses.
Use tags and reverse links to bundle the materials that you usually accumulate, and then let AI follow the manuscript.
Powerpoint slides, web presentations, handouts, and examples must be saveable, shareable, and readable by AI.
Verbatim drafts, students’ questions, and whether the lecture went smoothly or not, are all recycled into materials that can be used next time.
Write effective processes into skill packages, break knowledge into cards, and recombine them when facing different objects.
Finding information: Don’t let an AI search in the same direction
AI can easily be biased by context when looking for information. If you ask it to evaluate a certain press conference, it may first find official information, and then the entire context will be taken away by the official statement.
The approach in the course is multi-agent cross-fertilization: let different sub-agents find official information, positive views, and negative views respectively, and then pull the sources and original text excerpts back for cross-comparison. AI is not responsible for drawing conclusions directly. It is first responsible for finding all the information and listing the sources clearly.
This matter is very important to the lecturer. Because what you fear most when preparing for a course is that you only see information that you already believe in, and then package this preference into course content. The value of multi-agent crossover is to allow you to see different perspectives before preparing lessons.
Organize categories and inspiration pools
Lecturers usually see a good example, hear a student question, or read a trend, but they may not necessarily use it immediately. But if you don’t write it down now, it will be difficult to remember it again when it’s time to actually prepare for the lesson.
This class discusses this matter into three actions: daily accumulation, label threading, and regular summary.
The usual accumulation is not just to save the content, but also to write down why you find it useful at the moment. Label threading uses general labels and event labels to connect materials to courses that may be used in the future. Timed summarization is to establish a rhythm so that the data will not take too long to organize.
Pre-class questionnaire: Change what you want to learn into which step you are stuck on
Many pre-course questionnaires will ask: "What do you want to learn?" But students sometimes don't know what they want to learn.
The most important thing in the course is to ask: "Where are you stuck now?"
This questioning method is closer to the real demand survey. Students may not be able to tell you the complete solution, but they usually know where they are stuck. After the lecturer gets the questionnaire, he can use Agent to do placement analysis to see which levels and needs of the students are concentrated.
This will change the way you prepare for lessons. Before instructors design courses, they can first see where this group of people are really stuck, and then go back and calibrate their assumptions. The core area handles most people's problems first, and the supplementary area handles a few extended needs.
Promotion before class starts is also part of class preparation
Promotion before the course starts is not just for recruiting students, but also to test how the audience understands the topic.
The course mentioned the process of social carousel cards: write the copy first, then the storyboard script, and finally produce the pictures. Copywriting is the backbone, storyboards are the bridge, and pictures are the meat.
The meaning of knowledge management behind this process is: every promotion will generate feedback. Someone leaves a message asking "Will you teach a certain question?" This type of signal can be turned into lesson preparation material. It can be put back into the inspiration pool, or it can change the way the formal session is opened.
If the lecturer has a fixed brand role or visual style, like Mika, the style definition, three views, and common storyboarding principles should also be preserved. These materials will make the next picture-text instruction consistent.
Lesson preparation: use tags to bundle materials
When actually preparing lessons, the key is to gather together the materials that have been marked in normal times, without having to start from scratch.
The method mentioned in the course is: on the day of lesson preparation, open the lecture search page and use backlinks or tags to pull out relevant materials, such as "Lecturer Workflow" and "2026-05-23 Course". These materials may be work diaries, semi-finished articles, student feedback, community messages, and questions and answers from the previous class.
Then let AI follow the manuscript. AI can handle the sense of language, rhythm, and transition; the lecturer is responsible for his own stance and choices.
Making deliverables: allowing finished products to be saved, shared, and read by AI
The deliverables can be slides, web presentations, after-class handouts, sample files, prompts, or complete teaching manuals.
The course uses a web presentation as a demonstration, focusing on the deliverables being saveable, shareable, and readable by AI. This is why using HTML presentations is valuable: it can be projected, and it can also become a piece of material that can be searched, recycled, and used by the next agent.
Public content can be placed on GitHub Pages. If it contains meeting minutes, customer reports, personal information or sensitive content, it must be semi-privately arranged or not made public.
Reproduction after class: leave details, not just summary
The really important thing after class is to save the details of what happened in class, don’t just make a summary.
This class mentioned three levels of editing of verbatim manuscripts: super detailed, very detailed, and 1:1. Its value is not only in content organization, but also in the insight layer. You can ask AI to analyze where you speak smoothly and poorly, which paragraphs students respond well to, and which parts need to be re-told.
You can also make a teaching manual after class, and organize the lecturer’s own lecture notes into a version that others can take over. Students' questions can also be entered into the database one by one and linked back to the search page for the next class on the same topic.
In this way, students’ questions will be extended from classroom interaction to the best material for the next lesson preparation.
Reuse across courses: turning courses into building blocks
When the previous seven paragraphs are retained, the instructor does not need to start from scratch every time.
A successful operation can be written into a skill package; a set of stable lectures can be broken into cards; a batch of student questions can be turned into FAQs for the next course; a verbatim manuscript can be turned into a teaching manual or in-depth article.
Building blocks are used as a metaphor in the course: card-based knowledge can be rearranged and combined according to the needs of the next class. The same set of knowledge can be presented in different ways to university professors, administrators, content creators, editors, or business executives, but the underlying materials can be shared.