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.
Month: February 2025
I believe that the best way to meet the needs of learners varies for different students, direct instruction，inquiry-based learning，cooperative learning，experiential learning，open pedagogies The effectiveness of these different teaching methods undoubtedly varies among different students. Then we will try to achieve a ‘three-dimensional learning’ by focusing on one approach and incorporating other different methods. We will focus on demonstrating the correct and efficient inquiry methods to implant a rough concept of how to efficiently inquire about AI, and then have students explore the most suitable inquiry methods for themselves through extensive learning.
If there are online learning issues such as those caused by influenza virus in teaching, it is not a problem for our topic. We only need to use online meeting software such as Zoom to easily carry out our teaching work online.
In the presentation section, students may encounter situations where they don’t know how to explain their use of the problem or are too shy to express themselves. So during teaching, we will ask some classroom questions and praise the parts that students did right to ease their nervousness about the upcoming public speech.
According to our current plan, we have used multiple methods to test students’ mastery of knowledge, rather than relying solely on exams as a monotonous way to gain a one-sided understanding of their learning situation. I believe this can provide students with more opportunities for success from various aspects and also let them know their shortcomings.
For our learning blueprint, I think we may be more like GPS. Because our design focuses on guiding students towards success from multiple perspectives, much like how GPS analyzes the most suitable path from multiple paths to provide to users. I firmly believe that under our blueprint, students can achieve success, big or small, in this field
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.
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.
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.
- 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. - 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. - 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. - 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. - 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
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