Overview of the Learning Resource
Brief Definition of the Teaching Topic
Artificial Intelligence (AI) refers to technological systems that simulate human intelligence through algorithms and data, enabling machines to perform tasks such as learning, reasoning, and understanding language (Jo & Park, 2024). It has profoundly changed how people access information, analyze problems, and develop solutions. AI is widely applied across education, healthcare, and business sectors, and its potential as a learning assistant is increasingly recognized (Peng & Wan, 2024).
This learning resource is designed to help learners understand the advantages and risks of AI in a rational and critical way. It guides them to master effective AI interaction strategies and apply AI tools efficiently in future learning scenarios.
Target Learners
Our primary target learners are university students and unemployed individuals. We believe that effective use of AI will become a core skill in future workplaces. In order to proactively adapt to the rising trend of AI-powered work environments, it is both timely and necessary for learners to acquire relevant knowledge and capabilities in advance.
Chosen Learning Design Approach and Rationale
This course adopts a combination of Project-Based Learning and Inquiry-Based Learning.
Through project-oriented tasks such as creating self-learning plans, designing AI questioning structures, and delivering group presentations, students are encouraged to explore real-world applications of AI.
By practicing, reflecting, and comparing different AI responses, learners develop critical thinking and inquiry skills.
This blended approach aligns well with the nature of AI learning, which involves continuous experimentation, optimization, and knowledge transfer. Therefore, the integration of these two approaches is ideal for achieving the learning objectives.
Use of Universal Design for Learning (UDL) and CAST Principles to Promote Inclusive Design
To ensure equitable access and participation for all types of learners, this course incorporates the Universal Design for Learning (UDL) framework and the CAST principles:
Multiple Means of Representation
All video resources include English subtitles and provide text alternatives for individuals with hearing impairments or language barriers.
Audio playback and screen reader support are also provided to accommodate visually impaired learners.
Multiple Means of Expression
Learners can present their work in various forms, including written reflections, oral presentations, PowerPoint slides, or video recordings.
Peer feedback and self-assessment rubrics are used to support different learning styles and cognitive preferences.
Multiple Means of Engagement
A variety of activities such as video-based learning, AI query experiments, group discussions, and final presentations are designed to promote active engagement.
Cultural diversity is considered in content and assessment design to ensure that students from different backgrounds can understand and discuss ethical and social issues related to AI from their own perspectives.
Technology Tools and Rationale for Selection
To enhance interactivity and accessibility, we have selected the following technology tools:
Generative AI platforms such as ChatGPT and DeepSeek, allowing learners to practice question formulation and response refinement;
YouTube videos to provide structured instruction on how to formulate effective AI prompts, with subtitles for learners from various language backgrounds;
WordPress for access to resources and Q&A engagement;
PowerPoint for group presentations, supporting clear and visually enhanced communication.
These tools align with the habits and preferences of modern learners, while also providing essential support for completing project-based learning tasks.
Unit 1: Understanding AI & Its Role in Learning
Objective:
To help students grasp the fundamental principles and functions of AI, and understand the role and potential of AI in the learning process.
Content:
What is AI? What is machine learning and natural language processing?
Artificial Intelligence (AI) refers to technologies that allow computers to simulate human intelligence by “learning,” “reasoning,” and performing complex tasks. Machine learning, a core branch of AI, enables systems to improve performance by identifying patterns in data rather than relying on fixed instructions. Natural Language Processing (NLP) is the key technology that enables computers to understand and generate human language, allowing us to interact with AI tools like ChatGPT using natural language. Mastering these basic concepts helps learners understand the logic behind how AI supports learning and productivity.
Common AI Tools for Learning
Many AI tools are widely used in academic and research contexts. For example, ChatGPT can assist with writing, Q&A, and summarization; Deepseek offers web-connected, in-depth responses to complex questions; Gemini is deeply integrated with Google tools and has strong multimodal capabilities. Understanding the unique features of each tool allows learners to choose the AI assistant that best fits their personal learning needs.
What Can and Can’t AI Do?
AI excels at processing large amounts of information and generating structured content, such as writing summaries, organizing notes, rewriting text, or answering basic questions. However, it lacks human emotion, critical thinking, and true creativity, and cannot always provide accurate answers. AI doesn’t “understand” what it says — its responses are based on patterns in training data, which means it may generate misinformation. Knowing both the strengths and limitations of AI helps learners use it wisely as a support tool rather than relying on it for key decisions.
AI-Supported Learning Case Studies
In real educational contexts, both students and teachers have successfully integrated AI into their learning processes. For instance, one university student used ChatGPT to better understand philosophical texts by repeatedly asking and comparing explanations, deepening their grasp of complex concepts. A high school teacher had students design science experiments with AI support and follow up with group discussions, enhancing critical thinking. Other learners used AI to plan personalized study paths, helping them study more efficiently for exams or writing tasks. These examples highlight the vast potential of AI to deepen understanding, foster autonomy, and optimize resource management.
Assessment of Learning Outcomes:
Assignments
Short Answer Questions:
What is the definition of AI?
What is machine learning?
What is natural language processing?
Multiple Choice Questions:
What is AI not good at?
a) Processing large amounts of data
b) Repetitive tasks
c) Thinking independently like a human
d) Organizing information
Answer: c
What is one limitation of AI?
a) Having human emotion
b) Critical thinking
c) Creativity
d) Being free from misinformation
Answer: d
How can AI best support learning?
a) Letting AI generate all answers
b) Modifying your own answers after AI’s generation
c) Using AI to summarize and reinforce learning content
d) Relying fully on AI information regardless of its accuracy
Answer: c
Lab Work
Question: How do you plan to use AI to improve your learning efficiency in a specific course or skill?
In this lab, students will develop a personalized question and reasoning chain that they submit to AI for answers. The focus is on fostering their logical thinking and identifying the optimal questioning strategy.
Discussion
Students will discuss their lab topics and share the reasoning chain they used to ask AI questions. Through peer exchange, they’ll identify flaws and improve their approach.
Unit2:（Effective and Critical Use of AI Tools）
Objectives:
1.Master the basic skills and strategies for proposing high-quality prompts (Prompt Engineering).
2.Understand the impact of different questioning methods on AI output.
3.Have the ability to identify errors or biased information in AI-generated content.
4.Master the use of critical thinking tools (such as CRAAP Test) to evaluate the quality of AI output.
Core topics and knowledge points:
1.Prompt Engineering
Definition:
Prompt is the “instruction” or “way of asking questions” for users to interact with AI. High-quality prompts can effectively guide AI to output accurate and in-depth content.
Key components:
Role setting (Role): Let AI answer in a specific identity (such as: you are a student…)
Task description (Task): Clearly define the type of task you want AI to perform (writing, analysis, enumeration, etc.)
Context (Context): Provide necessary background information to help AI understand the task
Format requirements (Format): Tell AI the output format (such as table, paragraph, list, etc.)
2.Three common questioning styles and their characteristics:
- Imperative
Example: Write an article about climate change.
Output characteristics: The content is direct, but may lack depth.
- Guiding
Example:What are the potential impacts of climate change? Please give examples .
Output characteristics:More open, encourage multi-angle thinking
- Role setting
Example:You are an environmental scientist, please explain the impact of climate change from a professional perspective.
Output characteristics:Improve professionalism and tone accuracy
3. Analysis of common AI output problems
- Hallucination
AI “fabricates” non-existent content or false references.
Cause: lack of training data or over-generalization of the model.
Example: fabricating the title of a non-existent reference book.
- Oversimplification
Answering complex questions too simply or incompletely.
Common when insufficient context or restrictions are set.
- Data bias
Comes from imbalanced or existing biases in training data.
For example: gender stereotypes, cultural bias, etc.
- Missing or misunderstanding context
AI cannot truly “understand” the context and may misinterpret the intent.
4. Critical Appraisal Tool: CRAAP Test
Used to determine whether information is trustworthy and suitable for citation.
- Currency
Core Issues: Is the information updated? Is it suitable for the current situation?
Application: Check the publication date and data year.
- Relevance
Core Issues: Is the information relevant to the problem or topic?
Application: See if it revolves around the core of the problem.
- Authority
Core Issues: Is the author/source reliable?
Application: Does it cite authoritative institutions/experts.
- Accuracy
Core Issues: Is the content wrong or unverified?
Application: Does it support the data and have no logical loopholes.
- Purpose
Core Issues: Is the author’s intention neutral or biased?
Application: Check for promotional/misleading tendencies.
Assessment of Learning Outcomes:
Multiple Choice Questions:
- Which of the following prompts is most likely to generate an accurate and professional AI response?
A. Write an article about artificial intelligence.
B. Can you tell me about artificial intelligence?
C. Please introduce artificial intelligence in less than 1000 words. D. You are a tech journalist. Write a 500-word article that explains the definition and development of artificial intelligence in simple language.
Correct Answer: D
- Which questioning style is most likely to encourage the AI to provide deeper, multi-angle analysis?
A. Command-style prompt B. Guided prompt C. Yes/No question D. Example-only prompt
Correct Answer: B
- When an AI generates content that seems real but is actually false or made-up, this is called:
A. Bias
B. Simplification
C. Hallucination
D. Redundancy
Correct Answer: C
- In the CRAAP Test, what does “Accuracy” focus on?
A. Whether the information is current
B. Whether the information is relevant to the topic
C. Whether the information is correct and supported by evidence
D. Whether the author is credible
Correct Answer: C
- What is the main purpose of optimizing a prompt when using an AI tool?
A. To receive higher-quality, more relevant responses
B. To make the input longer
C. To reduce the AI’s response time D. To avoid getting rejected answers from the AI
Correct Answer: A
Personalized & Self-Regulated Learning with AI
Objective:
Teach students how to use artificial intelligence to support their personal learning, including developing study plans, organizing review strategies, and tracking learning goals, in order to enhance their cognitive abilities and self-reflection skills
Content:
- Using AI to Create Personalized Study Plans
Learn how to use AI to create effective study plans tailored to your personal interests, schedule, and areas of weakness, in order to improve learning efficiency and manage your time wisely.
- Enhancing Learning with AI-Powered Tools
Explore how AI can help organize your mistakes, summarize notes, and generate personalized practice questions to deepen understanding and enhance review.
- Developing Critical Awareness and Ethical AI Use
Reflect on the actual benefits of using AI in your learning (what has truly helped, and what hasn’t), while also understanding responsible usage guidelines such as avoiding the upload of personal information or original assignment questions.
Learning Outcome Assessment
- Students can use AI to generate a weekly study plan, carry it out, and then write a reflection. They will analyze whether the AI-generated plan was effective.
- Set learning goals and use AI tools to help achieve them.
- At the end of the course, students will give a personal presentation based on their experiences throughout the semester, showcasing “How I Learned Effectively with AI” this can be a presentation, blog, or poster.
Summative Assessment
- Use formative assessments such as practice exercises and reflective writing to encourage students to experiment, make mistakes, and make adjustments.
- Summative assessment: Students demonstrate their ability to effectively use AI for learning.
- Providing self-assessment checklists along with teacher feedback can better support students in improving their skills.
Q/A
1. What is the main purpose of using AI to create personalized study plans?
A. To avoid having to attend school
B. To delegate all learning responsibilities to AI
C. To improve learning efficiency by aligning with personal interests, schedule, and areas of weakness
D. To randomly generate study topics without guidance
2. What is the core of developing critical awareness and ethical AI use?
A. Letting AI make all your learning decisions
B. Reflecting on which AI tools are truly effective and using them responsibly
C. Using AI to copy answers from the internet
D. Sharing your learning progress on social media to get feedback
3. Which of the following is NOT a recommended use of AI in this unit?
A. Organizing mistakes and summarizing notes
B. Generating personalized practice questions
C. Uploading original assignment questions for AI to solve
D. All of the above
References（unit 1-）
Jo, H., & Park, D.-H. (2024). “Effects of ChatGPT’s AI capabilities and human-like traits on spreading information in work environments.” Scientific Reports, 14(1), 7806. https://doi.org/10.1038/s41598-024-57977-0
Peng, Z., & Wan, Y. (2024). Human vs. AI: Exploring students’ preferences between human and AI TA and the effect of social anxiety and problem complexity. Education and Information Technologies, 29(1), 1217–1246. https://doi.org/10.1007/s10639-023-12374-4
Su, J. (2023, Novermber 14). Google’s AI Course for Beginners (in 10 minutes)!
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