Pre-class Preparation
As a college teacher, I have been exploring how to integrate AI technology into teaching practice over the past year. I remember worrying about lesson preparation when school started last September. I teach "Introduction to Artificial Intelligence," one of the most anticipated courses for sophomore computer science students. Walking around campus, I often heard students enthusiastically discussing AI tools like ChatGPT and DALL-E, and their knowledge and passion for AI far exceeded my expectations.
Traditional teaching methods clearly couldn't satisfy the curiosity of these "AI natives." I vividly remember spending almost every day the week before school started thinking about how to keep the class both professional and interesting for students. After some exploration, I began trying to integrate AI tools throughout the teaching process, and this attempt brought me unexpected delights.
Course Design
In designing the course, I first tried using ChatGPT to help organize the teaching syllabus. I remember the first time I asked ChatGPT: "Please help me design an introductory AI course syllabus for sophomore students." ChatGPT's suggestions were eye-opening. It not only listed a complete knowledge system from machine learning basics to advanced deep learning but also suggested controlling the ratio of theoretical explanation to practical operation at 6:4 based on students' cognitive characteristics, which aligned with my years of teaching experience.
Even more delightful was that AI helped me solve three key problems that had always troubled me in specific lesson preparation. The first problem was how to make abstract algorithms lively and interesting. Previously, when explaining decision tree algorithms, I always started with mathematical formulas, often leaving students drowsy. Now with AI's help, I can introduce it using real-life cases like "how to choose lunch," helping students understand algorithm principles through daily decision-making processes.
The second problem was exercise design. In the past, I always struggled with difficulty levels, either making them too simple and lacking challenge, or too difficult and discouraging students. With AI assistance, I can set up question banks of varying difficulty for each knowledge point. For example, when explaining neural networks, from basic perceptron models to complex convolutional networks, each level has corresponding exercises, and students can choose suitable questions based on their understanding.
The third problem was presentation creation. Traditional PPTs often feel uniform, but now with AI tools like DALL-E, I can generate unique illustrations for each knowledge point. I remember when explaining reinforcement learning, I used DALL-E to generate a series of progressive images showing robots learning to walk. These images not only visually demonstrated algorithm principles but also received unanimous praise from students for their unique artistic style.
Classroom Practice
Interactive Teaching
Interactive teaching in the classroom is my favorite part. I remember once when explaining neural networks, I used AI to generate a vivid analogy: "A neural network is like a vast domino array, where each neuron is like a domino, and information transmission is like dominoes falling. When the first domino falls, it triggers subsequent dominoes, creating a chain reaction, just like information transmission in neural networks." This metaphor immediately caught students' attention, and the classroom filled with sounds of enlightened exclamation.
To deepen students' understanding, I often use AI tools spontaneously in class. For instance, when explaining machine learning algorithms, I have students work in groups, each using ChatGPT to generate different test datasets, then using these data to verify algorithm effects. Interestingly, each group's dataset has different characteristics, which sparks students' discussion interest. They begin analyzing why the same algorithm performs differently on different datasets, and this immediate feedback learning method makes abstract concepts tangible.
During a computer vision lesson, I tried a bold idea. I had students use AI image generation tools to create a series of images from simple to complex based on their understanding of convolutional neural networks. Some students generated gradient sequences from simple geometric shapes to complex landscapes, while others created artistic works evolving from sketches to paintings. This experiment not only helped students understand the layered feature extraction capabilities of convolutional neural networks but also sparked their creativity.
Personalized Tutoring
AI's performance in personalized tutoring truly surprised me. Last year, a student named Xiao Wang had difficulty understanding convolutional neural networks. Under traditional teaching methods, I might have just repeated the same concept until he understood. But with AI's help, things were different. I had Xiao Wang tell ChatGPT his confusion, such as "Why use convolutional layers instead of fully connected layers to process images?" AI not only patiently answered but also continuously adjusted its explanation based on his feedback.
Initially, AI used professional computer vision terminology to explain, but noticing Xiao Wang didn't quite understand, it switched to using daily life examples: "Imagine looking at a photo, your eyes don't take in all the details at once, but first notice main features, like whether it's a portrait or landscape, before focusing on details. Convolutional neural networks mimic this visual cognition process." This gradual explanation method eventually helped Xiao Wang breakthrough his understanding barrier.
Moreover, AI can automatically generate personalized review materials based on students' learning progress and mastery level. For example, after midterm exams, I had each student use AI to analyze their answer sheets, identify weak areas in their knowledge grasp, and then AI would customize review plans for each student. This precise tutoring method greatly improved students' learning efficiency.
Effectiveness Analysis
After a year of practice, the data shows this teaching method is indeed effective. Students' average scores increased from 78 to 89, a 15% improvement. More encouragingly, classroom participation showed significant improvement. Previously, only a few active students would raise hands during class questions, but now with AI tools' assistance, students have become more willing to express their ideas. Statistics show classroom participation increased from 35% to 75%.
The increase in after-class review time is also an interesting phenomenon. Through tracking students' learning behavior, I found their average weekly review time for this course increased from 4 hours to 5 hours. Students reported that with AI as a learning companion, review is no longer tedious but full of exploration and discovery.
The end-of-term questionnaire results were even more encouraging, with 93% of students indicating AI assistance made learning more interesting. One student wrote in the questionnaire: "I used to think AI was distant, but now I find it's right beside us, helping us learn, which feels really amazing." Another student said: "Learning AI knowledge through using AI tools, this 'learning by doing' experience is fantastic."
However, amidst the joy, I also discovered some issues worth noting. Some students began overly relying on AI, turning to it as their first response to problems rather than thinking independently. Some students also reported that AI's explanations weren't always accurate, especially regarding cutting-edge knowledge points. This reminds us that AI is ultimately an auxiliary tool and cannot completely replace traditional teaching methods and independent thinking ability.
To address these issues, I specifically added "AI Tool Usage Guidelines" content to the course, teaching students how to properly use AI tools. I emphasize that AI should be a "thinking assistant" rather than an "answer provider," encouraging students to think independently before using AI, then use AI to verify or supplement their ideas.
Future Outlook
With technological development, the prospects of AI education are promising. According to McKinsey's latest report, the global AI education market will reach $6 trillion by 2025. However, in my view, market size growth isn't most important; what's key is how to make AI truly serve educational essence and become a powerful tool for improving education quality.
I often think about what future classrooms will look like. I believe they'll be like a carefully orchestrated educational performance: teachers are like directors, responsible for overall instructional design and classroom rhythm control; AI is like various props and special effects, providing rich teaching resources and immediate interactive experiences; while students are the true protagonists, exploring knowledge mysteries and developing innovative abilities under teacher guidance and AI assistance.
In such future classrooms, teachers' roles will inevitably change. We may no longer be the sole source of knowledge but will become learning guides, AI tool integrators, and companions in student growth. We need to continuously learn new technologies, improve digital literacy, while maintaining educational professional judgment and humanistic care.
Experience Summary
Through this year's practice, I've summarized some valuable experiences. First, we must clearly position AI as an assistant, not the protagonist. Throughout course design and implementation, I've consistently maintained a teaching goal-oriented approach, choosing appropriate AI tools based on needs rather than using AI for its own sake.
Second, as teachers, we must continuously update our knowledge to keep pace with AI development. I spend time weekly learning about the latest AI field developments, testing new educational AI tools, and thinking about how to integrate them into teaching practice. This not only keeps course content current but also helps me better understand and use AI tools.
Most importantly, we must always maintain our educational original intention, putting students' growth first. No matter how powerful AI tools become, they're only means to achieve educational goals, not the purpose. We need to pay attention to each student's individual characteristics and learning needs, using AI to assist their personalized development rather than having all students learn in the same pattern.
In class, I often discuss AI's limitations and potential problems with students. This not only helps them form rational understanding but also cultivates their critical thinking ability. I tell students that in this era of rapid AI development, what's most important isn't how much knowledge they master, but learning how to learn, how to think, and how to innovate.
I believe that as long as we can correctly understand and use AI, we can definitely create a new era of education. Education's essence is igniting the flame of learning, and AI can become the catalyst, helping us light the lamp of knowledge in every student's heart. Let's look forward to the exciting future this educational transformation will bring!
So the question is: How will teachers' roles change in the future of widespread AI-assisted teaching? Feel free to share your thoughts in the comments.
