EDCI 335

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:

1. 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

2. 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

3. 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

4. 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

5. 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)!

 YouTube. https://youtu.be/Yq0QkCxoTHM?si=C3tB0GiOjricXOto

Peer review post#1 https://suzuran.opened.ca/peer-review-of-post-1/
Original source post#1 https://learntech.opened.ca/post-1/
Peer review post#2 https://suzuran.opened.ca/post-2/
Original source post#2 https://ttaruc.opened.ca/blog-ii-direct-instruction/
Peer review post#3 https://suzuran.opened.ca/feed-back-about-post-3/
Original source post#3 https://maweika.opened.ca/post-3/
Peer review Assignment#2 https://suzuran.opened.ca/feedback-of-assignment-2/
Original source Assignment#2 https://healthandwellness.opened.ca/

Post 3

We are similar in our basic ideas, but I found that in the last question, we did not associate inspiration with our blueprint design. Perhaps adding a section on how to use the part of preventing problems in the blueprint can better reflect your understanding of inclusive design.

Direct Instruction and Cooperative Learning are perhaps the two most commonly encountered learning methods at Uvic, and they can also be effectively utilized in our topic.

The main idea of Direct Instruction is for a lecturer to lead the explanation, demonstration, and clarification of students’ confusion. This passive learning is sometimes not the most high-quality way of learning, as students do not receive knowledge out of interest, but rather learn information provided by others. But students can clearly know what they should do when receiving guidance and learn useful content in a short period of time. In our topic, teaching students how to accurately search for keywords can quickly help them understand how to achieve high-precision AI conversations.

Cooperative Learning, on the other hand, allows students to explore and share experiences together to make up for their shortcomings in a certain topic, thus achieving comprehensive learning. This mutual assistance experience can increase students’ impression of the knowledge they have learned. In our learning, let students explore together the learning outcomes that different search methods can achieve, and finally summarize the most suitable and efficient learning method about AI for themselves.

In summary, both Direct Instruction and Cooperative Learning are suitable learning methods for our topic. By combining Direct Instruction and Cooperative Learning, we can learn how to efficiently ask AI for the information we need while finding a suitable way for ourselves.

In Therese Taruc’s article, I really enjoyed the understanding of Direct Instruction and its application. I think using Direct Instruction is definitely the most suitable way to learn about Alzheimer’s disease, a disease that is so difficult to understand its principles so far. Because only by summarizing the experience of predecessors can new and useful conclusions be attempted.

Blog Post #2 – “Direct Instruction”

In Melody Hung’s article, I also found that the encouraging nature of Cooperative Learning, along with the use of communication and critical thinking, can have the same positive impact on our different learning topics. I think we can also combine Direct Instruction with their topics to gain a more comprehensive understanding.

Blog 2

Brief introduction

AI is an epoch-making landmark, people have diverse views on it. Some believe it is a powerful tool that can significantly improve work efficiency, while others see it as a “devil” that can increase wealth inequality and cause many people to lose their jobs. What we want to achieve is to enable students to rationally, dialectically, and objectively view the benefits and harms brought by AI after a series of experiences and reflections. Therefore, we will start researching AI. We will start with an objective analysis of AI from some literature, such as chatbots and their significant contributions to the behavior and perception of office workers (Jo. H&Do Hyung. P, para. 61), as well as considering the existence of ChatGPT from the perspective of students and its impact on students’ response quality, communication ability, service attitude, psychological safety, and response time.（Peng. Z& Yan.W, para.72）We will focus on designing a self-learning plan, submitting relevant and efficient questions, reflecting on articles, and conducting group presentations. Finally, we can judge the success or failure of learning from multiple perspectives.

Misconceptions about artificial intelligence

Misconception One: AI has emotions and thinks like humans and it has the ability to operate autonomously

Many people believe that AI possesses human-like consciousness or understanding, but this is a significant misconception. Theoretically, artificial intelligence is generated through data and algorithms and does not have emotions like humans. In reality, current AI is primarily based on statistical computation. Even the most advanced AI systems merely generate responses based on data and algorithms and do not possess true self-awareness or understanding. AI cannot truly engage in emotional communication—its responses are flat.

Furthermore, many people mistakenly believe that AI can learn and evolve on its own without human supervision. However, most AI systems still rely on manually curated datasets and require engineers to continuously adjust and optimize their algorithms. AI decisions are often influenced by data quality, training methods, and human-defined objectives, making it impossible for AI to operate entirely autonomously. Even the most advanced AI today only predicts and generates responses based on data and algorithms, rather than truly understanding or possessing self-awareness.

Misconception Two: All AI systems are the same

In my previous research, I explored how different cultures and religions shape people’s understanding and perception of artificial intelligence. For example, Western Christianity emphasizes the idea of “not playing God,” believing that even AI cannot replace a divine presence. In contrast, East Asian thought focuses more on AI’s role in human relationships and communities, aligning with Confucian ren (benevolence) and Buddhism’s emphasis on interconnectedness.

Additionally, I examined the concept of “digital resurrection,” which allows people to maintain a certain level of connection with the deceased through AI technology. While this technology may provide psychological comfort, its ability to truly help loved ones overcome grief remains an ethical concern. These cultural perspectives have led me to realize that AI is not just a technological tool but also a reflection of the values and belief systems of different societies. This also illustrates that responses to AI vary across different cultural contexts.

Most interesting part 

As society becomes increasingly reliant on artificial intelligence, the reason for developing these learning resources is that the widespread application of AI makes understanding its fundamental principles and technologies more important. Through these resources, I hope to help students not only grasp how AI works but also learn how to use AI effectively to face future challenges. I am particularly interested in the impact of AI on society and how education can help students critically understand and apply this technology, which is also an exciting new field.

Big Idea

The big Idea of this course is to give students a general understanding of how artificial intelligence works and what knowledge related to the specific technical principles of artificial intelligence students should master by the end of the course. Several important chapters will be introduced below.

Machine Learning: Machine learning is the foundation of artificial intelligence, and understanding machine learning is the foundation of this course. The relevant knowledge of machine learning is interdisciplinary, covering probability theory, statistics, etc. The core purpose is to use computers as tools to truly strive to simulate human learning methods in real time. Students need to have a very clear definition of machine learning by the end of this chapter.

Deep learning: Deep learning is a subfield of machine learning. This chapter is an extension of the “machine learning” chapter. It studies the structure and training methods of neural networks. Its core idea is to simulate the neuron structure of the human brain to process complex data. As a small chapter, students only need to have a general understanding of machine deep learning, mainly to understand how deep learning can improve AI efficiency.

Natural language processing: The purpose of natural language processing is to enable computers to understand and generate human language, including word segmentation, part-of-speech tagging, syntactic analysis, semantic analysis, etc. This is to enable machines to understand and generate human language and achieve natural human-computer interaction. In this chapter, students need to understand how to use AI more efficiently after learning. For example, what are the key tasks of AI, how to make precise requirements for AI, etc.

Learning Outcomes

By the end of the course, students should have a general understanding of how AI works. Then, by understanding how AI works, they should understand how to use it correctly and effectively, and even understand how to customize AI for individuals.

Assessment Plan

To facilitate learners’ exploration, experimentation, and active engagement with concepts while preparing them for assessment, I have designed a series of interactive learning activities. These activities not only encourage students to think critically and apply their knowledge but also ensure they can demonstrate their learning outcomes through various forms of evaluation.

1. Assignments:
   Students will design a self-learning plan based on the knowledge they want to gain from AI and create a set of structured questions to enhance their independent learning and critical thinking skills.
2. Labs Work:
   The instructor will provide lab topics, and students will be required to submit a well-structured set of questions related to the topic, fostering their research and logical reasoning abilities.
3. Participation (Discussion):
   Based on previous assignments, students will write a reflection piece, analyzing their learning process, challenges encountered, and strategies for improvement, thereby enhancing their metacognitive skills.
4. Presentation:
   In groups of 4-5, students will create a PowerPoint presentation, where each member will showcase their best-designed question from the semester, explaining the underlying logic. Additionally, they will share effective techniques for asking AI questions and key considerations for improving AI interactions.
5. Quiz：We will quiz three times before, during and after study, and make a curve according to the percentage of progress and decline, so as to get a more objective result. Meanwhile, four of us will score each quiz individually, and refer to the score given by ai to improve the accuracy of the score.

Reference list:

Jo, Hyeon, and Do-Hyung Park. “Effects of ChatGPT’s AI Capabilities and Human-like Traits on Spreading Information in Work Environments.” Scientific Reports, vol. 14, no. 1, 2024, pp. 7806–7806, https://doi.org/10.1038/s41598-024-57977-0.

Peng, Ziqing, and Yan Wan. “Human vs. AI: Exploring Students’ Preferences between Human and AI TA and the Effect of Social Anxiety and Problem Complexity.” Education and Information Technologies, vol. 29, no. 1, 2024, pp. 1217–46, https://doi.org/10.1007/s10639-023-12374-4.

Project plan:

Yunyang Ma:Theme Description

Xinghan Wang: Misunderstanding and interest

Fan Xiong: Big Idea and Learning Outcomes

Yingjie Zhang: Assessment Plan

Post #1

I like your analysis of your teaching style, and I believe your judgment is accurate. As a student, I believe that constructing real problems to explain mathematics is a particularly effective method. Although it may take more time to visualize mathematical concepts, it undoubtedly leaves a deeper impression on students. I can always remember the scene when I was studying the Pythagorean theorem of trigonometric functions, where the teacher poured water from a square formed by two right angled sides into a square formed by the hypotenuse, filling it perfectly. It has been proven that this method of driving multiple parts of the brain to work together seems to deepen my impression of concepts.