Português
EnglishHow AI Helps Teachers Improve Teaching Efficiency
QwenWork Case Study
At the end of each class, Wang Yize would batch-send links to the day’s recorded classroom audio to Qwen Office.
"For example, if I taught five classes today, I just copy and paste five links and send them all at once, and the system automatically reads the content."
Behind each link is a real Western Economics class. It contains the teacher’s explanations, examples given on the spot, and student responses and interactions. In the past, this information was scattered across individual classes; now, Wang Yize uses Qwen Office and DingTalk AI Education Edition to connect and integrate these fragmented pieces into sustainable teaching resources.
First, Save Each Class Completely
Wang Yize is a teacher focused on college preparatory education, specializing in economics-related subjects, with 2,500 to 3,000 students each year.
In theory, as courses become more refined and knowledge systems stabilize, teaching should get easier. But his experience is just the opposite. "When a class is polished to perfection, it feels like all I do from then on is copy and paste."
The same material has to be repeated for different cohorts of students. Yet after class, examples added on the fly and points where students had questions often go unrecorded.
If students don’t understand something, they either ask again or rewind the video themselves. Meanwhile, teachers must spend time analyzing where exactly students are struggling, in addition to preparing lesson plans, designing exercises, and grading assignments.
A month ago, Wang Yize started using the AI Education Edition, and this “copy-paste” teaching model began to change. At the end of each class, the AI Education Edition not only transcribes speech to text but also automatically generates class summaries, chapter outlines, and study notes. If a student misses a concept, they don’t have to search back and forth through recordings—key concepts, difficult points, and example explanations can all be quickly found in the notes.
In earlier years, he tried using large language models to generate economics practice questions, but when asked to focus on specific topics within a chapter of Western Economics, the system often produced questions that were off-topic or unevenly challenging. Building a usable question bank required constant supplementation with old questions, textbooks, and classroom examples.
Now, the AI Education Edition can generate questions based on the actual content and key points covered in class, and Wang Yize only needs to make minor edits afterward.
"Because general-purpose large models don’t really ‘know’ you. But once the AI Education Edition listens to your class, it gains enough context to understand the core ideas, assessment requirements, and teaching flow. The questions it generates are much more relevant, so I feel more confident using it."
Students’ performance on the questions is also clearly recorded. Through the AI Education Edition, teachers can see the average time spent on each question, identify where students hesitate, and pinpoint concepts they haven’t mastered, providing data to support personalized instruction going forward.
Based on Wang Yize’s experience, tasks that used to take an entire afternoon—like organizing lesson materials, designing exercises, and compiling error lists—can now be completed in about half an hour. Common mistakes across a class, which previously took two days to identify, can now be spotted immediately after the exercise session ends.
The sounds, examples, and interactions from class are preserved, becoming valuable references for future question creation and explanations.
Then, Connect and Integrate Multiple Classes
Wang Yize may teach four to five classes a day, overseeing multiple classes simultaneously. With complete records for each session, he began looking at longer-term trends: Which topics keep appearing as weak areas over the course of a month? How do different classes progress at different rates? Which classroom content is worth turning into new teaching materials or question banks?
These questions go beyond the scope of any single class.
In his workflow, the AI Education Edition handles recording and analyzing individual class sessions, while Qwen Office receives the batch of class recordings and integrates them into a broader, time-spanning perspective for teaching research. After school each day, he sends several links at once, allowing the knowledge points, examples, and teaching approaches scattered across different classes to be consolidated and compared.
Currently, this data is still accumulating. Over the course of a month, a semester, or even longer, Wang Yize hopes to track how teaching priorities evolve and identify persistent blind spots among students. These conclusions will require time to confirm.
Qwen Office also takes on some routine, repetitive tasks.
Since he manages multiple classes and corresponding DingTalk groups, he used to have to individually remind students or parents after assigning homework, then follow up on submissions. Now, he’s set this process as a scheduled task in Qwen Office—every weekday evening at 7 PM, the system automatically sends reminders, and he handles follow-up on submission status all at once.
Classroom recordings are also gradually being applied to textbook and exam preparation. Practice questions for individual classes are generated with the help of the AI Education Edition; when preparing full textbooks or mock exams, Wang Yize hands existing textbook or reference templates over to Qwen Office for processing.
This workflow has been formalized into two Skills—“Textbook Setup” and “Exam Setup”—allowing for standardization and reuse.
Helping AI Understand My Classes Better
This teaching approach has prompted Wang Yize to rethink the true meaning of “AI-assisted teaching and research.”
He divides his journey with AI into three stages: first, “AI couldn’t do it”—it could generate questions but often made mistakes or went off-topic; next, “AI didn’t know me”—each time, he had to re-input textbooks, old questions, and course background; now, he aims for “AI that’s both efficient and understands me.”
This “understanding” comes from real classroom sessions—the teacher’s explanations and students’ answers. The AI Education Edition captures this information, and Wang Yize passes it along to Qwen Office as contextual input for ongoing teaching and research.
He believes that introducing AI isn’t just about speeding up textbook creation, exam preparation, or classroom analysis; more importantly, it dramatically reduces the need to move information between steps and re-enter data repeatedly.
In the past, he’d record a lecture, transcribe it, then copy and paste the materials into the model. Every time he switched tools, he had to re-explain the course objectives, assessment focuses, and teaching challenges. Even a powerful model, without context, could only start from scratch in understanding the instructor.
Now, the AI Education Edition captures real-world information during class, and Wang Yize forwards the audio recordings to Qwen Office. This allows AI to learn the course’s structure and participate in long-term teaching research and optimization.
"I think Qwen Office’s most important advantage—or what matters most to me—is its ecosystem, its interactivity, and its connectivity," Wang Yize says.
This approach also reflects a growing trend in AI development: for intelligent agents to handle real-world tasks, they need ample context. In the future, more and more devices will serve as sources of agent input, continuously collecting and recording dynamic data from classrooms, meetings, and daily work.
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