Origin and Characteristics of AI
Weekly AI, ICT & STEAM Course Design (Full English)
Week 1: AI History and Development
1. AI General Course (40 minutes total: 20min + 20min)
Part 1 (20 minutes): AI History Overview
Objective: Help students understand the origin, key stages and milestone events of AI development, establish a basic cognitive framework of AI.
Process:
Warm-up (3 mins): Ask students "What do you know about AI?" and invite 2-3 students to share their understanding (e.g., smart speakers, facial recognition).
Lecture (12 mins): Introduce the key stages of AI development: Birth (1956, Dartmouth Conference), AI Winter (1970s-1980s), Revival (1990s-2000s), Deep Learning Boom (2010s-present). Highlight milestone events (e.g., IBM Watson defeating human champions in Jeopardy, AlphaGo defeating Go champion).
Discussion (5 mins): Guide students to discuss "How has AI changed our lives in the past 10 years?" and summarize key points.
Part 2 (20 minutes): AI in Daily Life & Project Introduction
Objective: Connect AI history with real-life applications, introduce Project 1 (Dialect Assistant Robot) and clarify the core tasks.
Process:
Case Analysis (10 mins): Show typical AI applications related to speech (e.g., voice assistants, dialect translation tools), explain how they relate to the AI development we learned earlier.
Project 1 Introduction (7 mins): Introduce "Dialect Assistant Robot" - core functions: recognize local dialects, convert dialects to standard Mandarin, and respond in dialect. Explain the project significance (protecting dialect culture, practicing AI speech technology).
Task Arrangement (3 mins): Assign pre-class task: Ask students to record 3 short sentences in their local dialect (10-15 seconds each) and bring them to the next class.
2. ICT Course (60 minutes): AI Development History Presentation (AI-generated Office)
Objective: Let students master the method of using AI tools to generate Office documents (PPT/Word) to display AI development history, improve information integration and tool application ability.
Process:
Tool Introduction (10 mins): Introduce AI tools for Office generation (e.g., Microsoft Copilot, WPS AI), demonstrate the basic operation (input prompts, adjust content, generate documents).
Prompt Design Guidance (15 mins): Teach students how to design clear prompts (e.g., "Generate a PPT about the history of AI development, including 5 key stages, 3 milestone events, and 2 real-life applications. Use simple and clear pictures and text.").
Hands-on Practice (30 mins): Students use AI tools to generate a 5-8 page PPT about AI development history. Teachers walk around to guide, help students solve problems (e.g., adjusting PPT layout, supplementing key content).
Summary & Feedback (5 mins): Invite 1-2 students to display their generated PPT, comment on the advantages and areas for improvement.
3. STEAM Course (60 minutes): Control Board and Wiring
Objective: Help students recognize the basic structure of the control board (e.g., Arduino), master the correct wiring method, lay the foundation for the subsequent robot production.
Process:
Component Recognition (10 mins): Introduce the control board, wires, power supply, LED lights and other components. Explain their functions (e.g., control board is the "brain" of the robot, wires are used to transmit signals).
Wiring Principle Lecture (15 mins): Demonstrate the correct wiring method (positive and negative poles, interface correspondence), emphasize safety precautions (e.g., do not reverse the positive and negative poles to avoid damaging the control board).
Hands-on Wiring Practice (30 mins): Students practice wiring: connect the control board to the power supply, connect the LED light to the control board, and test whether the LED light can light up. Teachers guide students to check and correct wrong wiring.
Summary & Task (5 mins): Summarize the key points of wiring, assign after-class task: Review the wiring steps and prepare for the next class's robot shape production.
Week 2: Fundamentals of Sound and Audio
1. AI General Course (40 minutes total: 20min + 20min)
Part 1 (20 minutes): Sound and Audio Fundamentals
Objective: Help students understand the basic principles of sound (generation, transmission, characteristics), and establish the connection between sound and audio signals.
Process:
Warm-up (3 mins): Do a small experiment: Tap a glass with a spoon, let students listen to the sound, and ask "How is this sound generated?" to lead into the topic.
Lecture (12 mins): Explain the generation of sound (vibration of objects), transmission medium (solid, liquid, gas), and basic characteristics (frequency, amplitude, timbre). Connect to daily life (e.g., different people have different timbres, high and low sounds are related to frequency).
Interactive Activity (5 mins): Let students make different sounds (e.g., speaking, clapping) and observe the changes of sound characteristics, then share their findings.
Part 2 (20 minutes): Audio Signals and AI Connection
Objective: Let students understand how sound is converted into audio signals, and how AI processes audio signals (foreshadowing for subsequent speech recognition and synthesis).
Process:
Principle Explanation (10 mins): Explain that sound (analog signal) needs to be converted into digital signals through sampling and quantization, so that AI can recognize and process it. Use simple diagrams to show the conversion process.
Case Demonstration (7 mins): Play a short audio (e.g., a sentence of speech), show the waveform of the audio (through simple software), and explain that AI can analyze the waveform to recognize the content of the sound.
Pre-class Preparation Reminder (3 mins): Remind students to bring the dialect recording assigned last week, which will be used in the next ICT class.
2. ICT Course (60 minutes): Sound Principle (AI Programming to Display Recording Waveforms)
Objective: Let students master the basic method of AI programming to collect audio signals, and display the waveform of the recording, so as to deepen the understanding of sound principles.
Process:
Software Introduction (10 mins): Introduce programming software (e.g., Python with PyAudio, Scratch AI extension), demonstrate how to install and open the software, and explain the basic interface.
Programming Guidance (20 mins): Teach students to write simple AI programs: 1. Call the microphone to collect audio (recording for 5 seconds); 2. Convert the audio signal into a waveform; 3. Display the waveform on the screen. Explain the key code (e.g., audio sampling rate, waveform drawing function).
Hands-on Practice (25 mins): Students practice programming: Record their own voice (or the dialect recording brought), and display the waveform. Teachers guide students to adjust parameters (e.g., recording time, waveform color) and solve programming errors.
Summary (5 mins): Summarize the key points of audio signal collection and waveform display, and explain that this is the basis for AI to recognize speech.
3. STEAM Course (60 minutes): Voice Sensor and Voice Synthesizer
Objective: Help students recognize voice sensors and voice synthesizers, master their connection method with the control board, and understand their working principle.
Process:
Component Introduction (10 mins): Introduce voice sensors (function: collect sound signals and convert them into electrical signals) and voice synthesizers (function: convert electrical signals into sound). Show the appearance and interface of the components.
Working Principle & Wiring Demonstration (15 mins): Explain the working principle of the two components, then demonstrate how to connect them to the control board (correctly connect the power supply, signal interface), and test whether they work normally (e.g., the voice sensor collects sound and the voice synthesizer makes a prompt sound).
Hands-on Practice (30 mins): Students connect the voice sensor and voice synthesizer to the control board independently, test the function, and record the phenomenon. Teachers walk around to guide and solve connection problems.
Summary & Preview (5 mins): Summarize the key points of component connection and function, preview the next class's content (robot shape production), and ask students to think about the shape design of the dialect assistant robot.
Week 3: Automatic Speech Recognition (ASR)
1. AI General Course (40 minutes total: 20min + 20min)
Part 1 (20 minutes): Introduction to Automatic Speech Recognition (ASR)
Objective: Help students understand the definition, working principle and application scenarios of ASR, and lay the foundation for subsequent dialect recognition practice.
Process:
Warm-up (3 mins): Play a short audio of dialect and standard Mandarin, ask students "Can you understand the dialect? How can we let the computer understand it?" to lead into ASR.
Lecture (12 mins): Define ASR (the technology that converts human speech into text), explain its working principle (audio collection → preprocessing → feature extraction → model recognition → text output), and introduce common application scenarios (voice input, voice assistants, real-time translation).
Discussion (5 mins): Guide students to discuss "What difficulties may there be in dialect recognition compared with standard Mandarin recognition?" (e.g., different dialect accents, few data samples).
Part 2 (20 minutes): ASR Technology and Project Progress
Objective: Let students understand the development of ASR technology, and combine it with Project 1 to clarify the dialect recognition task of this week.
Process:
Technology Development (10 mins): Introduce the development of ASR technology (from traditional methods to deep learning-based methods), and explain why modern ASR can better recognize dialects (large-scale data training, adaptive models).
Project 1 Progress Arrangement (7 mins): Clarify this week's task: Use AI tools to realize dialect recognition, convert the dialect recording into text. Review the dialect recording brought by students, and confirm the dialect types.
Task Preview (3 mins): Preview the ICT class content, ask students to be familiar with the dialect recording again, and prepare for the hands-on practice of dialect recognition.
2. ICT Course (60 minutes): Speech Recognition (AI Recognizes Dialects)
Objective: Let students master the method of using AI speech recognition tools to recognize dialects, convert dialect audio into text, and understand the application of ASR technology in practical projects.
Process:
Tool Introduction (10 mins): Introduce AI dialect recognition tools (e.g., Baidu AI Speech Recognition, Tencent Cloud Speech Recognition), demonstrate how to register, create an application, and call the recognition interface.
Operation Guidance (15 mins): Teach students to upload the dialect recording (previously prepared) to the AI tool, set the dialect type, and perform speech recognition. Explain how to adjust the recognition accuracy (e.g., upload clear audio, select the correct dialect category).
Hands-on Practice (30 mins): Students upload their own dialect recordings, perform AI recognition, check the recognition results, and modify the incorrect parts. Teachers guide students to solve problems (e.g., low recognition accuracy, audio upload failure).
Summary & Record (5 mins): Invite students to share their recognition results, summarize the factors affecting dialect recognition accuracy, and ask students to record the recognition results for subsequent project use.
3. STEAM Course (60 minutes): Robot Appearance Production
Objective: Let students design and produce the appearance of the dialect assistant robot, combine the control board, voice sensor and other components, and improve hands-on production ability and creativity.
Process:
Design Sharing (10 mins): Ask students to share their robot appearance design ideas (e.g., shape, color, size), and the teacher comments and puts forward suggestions (e.g., ensure the components can be installed, the shape is practical and beautiful).
Material Introduction & Production Guidance (15 mins): Introduce production materials (e.g., cardboard, plastic bottles, tape, scissors), demonstrate the basic production steps: 1. Make the robot shell; 2. Reserve positions for the control board, voice sensor and voice synthesizer; 3. Fix the components.
Hands-on Production (30 mins): Students produce the robot appearance independently, install the components (control board, voice sensor, voice synthesizer) into the shell, and adjust the position to ensure the components work normally. Teachers walk around to guide and help students solve production problems.
Progress Check (5 mins): Check the production progress of each student, remind students to complete the basic appearance production, and prepare for the next class's code integration.
Week 4: Text-to-Speech (TTS) Synthesis
1. AI General Course (40 minutes total: 20min + 20min)
Part 1 (20 minutes): Introduction to Text-to-Speech (TTS) Synthesis
Objective: Help students understand the definition, working principle and application scenarios of TTS synthesis, and establish the connection between TTS and ASR.
Process:
Warm-up (3 mins): Play the text-to-speech audio of standard Mandarin and dialect, ask students "How is this audio generated? Is it different from the audio we recorded?" to lead into TTS.
Lecture (12 mins): Define TTS (the technology that converts text into human-like speech), explain its working principle (text preprocessing → phonetic conversion → speech synthesis → audio output), and introduce common application scenarios (text reading, voice broadcasting, intelligent robots).
Interactive Activity (5 mins): Let students input a short text into a simple TTS tool, listen to the generated audio, and adjust the speech speed and tone, then share their feelings.
Part 2 (20 minutes): TTS and Project Integration
Objective: Let students understand the application of TTS in Project 1, clarify the dialect synthesis task of this week, and sort out the overall progress of the project.
Process:
TTS and Project Connection (10 mins): Explain how to apply TTS in the dialect assistant robot: Convert the text (dialect recognition result or preset reply) into dialect speech through TTS, so that the robot can respond in dialect.
Project 1 Overall Progress (7 mins): Sort out the project progress: Week 1 (topic determination), Week 2 (component preparation), Week 3 (dialect recognition), Week 4 (dialect synthesis and code integration). Clarify this week's core task: Realize dialect synthesis and integrate all functions.
Project Display Preview (3 mins): Remind students that the project will be displayed at the end of this week, and ask them to prepare for the display (e.g., sort out the robot functions, practice the operation steps).
2. ICT Course (60 minutes): Speech Synthesis (AI Synthesizes Dialects)
Objective: Let students master the method of using AI TTS tools to synthesize dialect speech, convert text into dialect audio, and complete the dialect synthesis part of Project 1.
Process:
Tool Introduction (10 mins): Introduce AI dialect TTS tools (e.g., Baidu AI TTS, Alibaba Cloud TTS), demonstrate how to call the tool, select the dialect type, and set parameters (speech speed, tone, volume).
Operation Guidance (15 mins): Teach students to input the text (dialect recognition result or preset reply) into the TTS tool, select the corresponding dialect, generate dialect audio, and save the audio. Explain how to adjust the audio effect to make it more natural.
Hands-on Practice (30 mins): Students input their own dialect text (e.g., the recognition result of last week's recording, preset greeting sentences), generate dialect audio, and test the effect. Teachers guide students to solve problems (e.g., incorrect dialect pronunciation, audio distortion).
Summary & Preparation (5 mins): Summarize the key points of dialect TTS synthesis, ask students to save the generated dialect audio, and prepare for the next class's code integration.
3. STEAM Course (60 minutes): Code Integration and Application
Objective: Let students integrate the previous functions (dialect recognition, dialect synthesis) into the robot through code, realize the complete function of the dialect assistant robot, and complete the project display.
Process:
Code Integration Guidance (15 mins): Review the previous programming content (audio collection, waveform display, dialect recognition, dialect synthesis), demonstrate how to integrate the code: 1. Call the voice sensor to collect dialect audio; 2. Use AI to recognize dialect and convert it into text; 3. Use AI to synthesize dialect text into audio; 4. Play the synthesized audio through the voice synthesizer.
Hands-on Code Integration (30 mins): Students integrate the code independently, test the robot's function (collect dialect → recognize → synthesize → play), and debug the code to solve problems (e.g., the robot does not respond, the audio playback is abnormal). Teachers walk around to guide and provide help.
Project Display (10 mins): Invite students to display their dialect assistant robots in turn, demonstrate the functions, and introduce their production process and experience. Other students and teachers comment and give suggestions.
Summary & Conclusion (5 mins): Summarize the whole course content, praise the students' performance, and encourage students to continue exploring the application of AI in daily life.