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.ļ¼Peng. Z& Yan.W, para.72ļ¼We 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 communicationāits 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 peopleās understanding and perception of artificial intelligence. For example, Western Christianity emphasizes the idea of ānot playing God,ā believing that even AI cannot replace a divine presence. In contrast, East Asian thought focuses more on AIās role in human relationships and communities, aligning with Confucian ren (benevolence) and Buddhismās emphasis on interconnectedness.
Additionally, I examined the concept of ādigital resurrection,ā 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.
Learning Resource Fundamentals
Artificial Intelligence (AI) has revolutionized the way people access and process information. From students to professionals, AI can serve as a powerful tool to enhance learning efficiency, expand knowledge, and develop critical thinking skills. However, to maximize AIās potential as a learning assistant, users need structured guidance on how to ask effective questions, critically evaluate AI-generated content, and apply AI tools to their personalized learning journeys. This learning resource is designed to equip learners of all ages with strategies to effectively use AI for self-directed learning, enabling them to explore topics of interest in an engaging and efficient manner.
The course follows a cognitivist approach, helping learners develop structured inquiry skills while interacting with AI. By leveraging AIās capabilities, users will refine their questioning techniques, analyze AI-generated information, and apply insights to real-world scenarios. This will be achieved through a combination of blog-style instructional content, interactive activities, and formative assessments to reinforce understanding and improve AI-based learning outcomes.
Learning Environment & Target Audience
This learning resource is designed for learners of all ages who want to explore how to learn efficiently with AI tools. The course is delivered in English, but multilingual subtitles ensure accessibility for non-native speakers, removing language barriers and making AI learning globally accessible.
The course is structured to accommodate a wide range of learners, including:
– Students (middle school, high school, and university) who wish to use AI for academic research, study assistance, and skill development.
– Professionals and lifelong learners looking to explore new fields, expand their knowledge base, or improve productivity using AI tools.
– Individuals with limited access to formal education who want to leverage AI as a personal tutor for self-improvement.
This online learning resource is available to anyone with access to a computer or mobile device. It provides on-demand, flexible learning, allowing learners to complete activities at their own pace. Users can access assessments to test their AI learning strategies, refine their questioning techniques, and track their improvement over time.
By removing barriers to effective AI learning, this resource ensures that learners of all backgrounds can harness AIās power for personalized, self-directed education while building essential critical thinking and inquiry skills for the future.
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.
Interactivity & Technology
In our activities, we will first teach the composition of the high-efficiency question system by watching videos and summarizing the main points. Then we will give students the opportunity to compare. We will set a theme and limit the number of questions, so that students can try to obtain relevant information by using the techniques they usually ask AI. Then, we can obtain information by using video and question structure of our teaching in the same limited number of times, and finally compare the efficiency of information collection.
We use PowerPoint and video tools to teach at events, and use things like ChatGPT or DeepSeek to make students feel the difference in productivity.
Learning Outcomes
By the end of this course, students will develop a foundational understanding of AI and learn to integrate it effectively into their learning processes. They will be able to explain key AI concepts, including machine learning and natural language processing, and distinguish between AI-generated and human-generated content. Students will acquire skills to ask structured AI queries, critically evaluate AI-generated responses, and apply AI tools for research, summarization, and brainstorming while maintaining academic integrity. They will also explore AIās role in personalized learning by adjusting AI settings to fit their individual preferences, using AI for progress tracking, and streamlining study tasks like summarizing notes or generating flashcards. Additionally, they will develop awareness of AIās ethical implications, including misinformation risks, data privacy concerns, and responsible AI usage. Through project-based learning, students will create a self-learning plan incorporating AI tools, engage in AI-assisted discussions, and collaborate on AI-enhanced presentations. By mastering these outcomes, students will leverage AI as an active learning assistant, enhancing efficiency, adaptability, and critical thinking in an AI-driven world.
Inclusion Review
- Evaluate the language used in learning content to ensure it is accessible to learners from different linguistic backgrounds, avoiding complex terminology or cultural biases.Also, try to use easy-to-understand methods when explaining professional terms. We will ensure that any personal opinions expressed in the language are explained individually, preventing them from influencing the readerās independent judgment.
- Pay attention to the examples and case studies in the resources, ensuring they incorporate diverse cultural perspectives. Different cultural backgrounds may lead to varying interpretations of issues, highlighting the necessity of analyzing these topics from multiple perspectives.
- Ensure that discussions and activity designs take into account different cognitive styles of learners, allowing them to participate and understand the issues and content fairly.
Accessibility Review
- Ensure that all video resources provide subtitles, making it easier for readers of any language to understand the content. More importantly, this will aid individuals with hearing impairments in comprehending the material.
- Check whether learning materials provide text alternatives, allowing those with reading difficulties to use assistive tools to access information. This will help these individuals better understand the content.
- Focus on the availability of text-to-speech functionality and screen reader compatibility to ensure that visually impaired learners can access resources smoothly.
- Assess the user-friendliness of the interface, ensuring accessibility for individuals with mobility impairments, such as through the use of voice commands. This will help improve their ability to read and watch learning materials more effectively.
Interaction design
To enhance students’ understanding of how to effectively ask AI questions, I selected a YouTube video that introduces interaction strategies for AI models like ChatGPT and explains how to construct clear, specific, and useful AI prompts. Although the video itself is not interactive, students can actively engage in multiple ways, such as recording effective and ineffective AI prompts, experimenting with different query structures in ChatGPT, and analyzing AI-generated responses to identify biases, ambiguities, or misinformation. This learning approach encourages students to optimize AI interactions through practice rather than passively receiving information. To deepen engagement, I have designed a series of post-video activities, including AI prompt optimization exercises where students refine basic AI questions to improve clarity and contextual accuracy; AI query comparison tasks where students analyze responses to different questioning strategies and evaluate the most effective approach; and real-world AI application discussions where students explore how effective AI interactions can enhance productivity in academic or professional settings. These activities not only improve technical AI interaction skills but also foster critical thinking and precision in communication.
To ensure effective feedback, students will engage in peer review by evaluating each other’s AI queries and providing suggestions for improvement, while instructors will also offer feedback on query logic and structure. Additionally, students will use a self-assessment rubric to evaluate their questioning skills and assess the quality of AI responses. This activity balances feasibility and effectiveness, making it suitable for both small classroom discussions and large-scale online courses. Students can reduce instructor workload through self-assessment while benefiting from an interactive learning approach that allows them to iteratively refine their AI querying techniques. To ensure inclusivity, I will provide subtitles and transcripts for students with hearing impairments or those who are non-native English speakers, along with multiple engagement modes such as text-based exercises, audio reflections, and video demonstrations to accommodate different learning styles. Furthermore, alternative AI platforms like Google Bard and Claude will be incorporated, enabling students to compare responses from different AI models and gain a more comprehensive understanding of AI interactions.
Assessment Plan
To facilitate learners’ exploration, experimentation, and active engagement with concepts while preparing 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 (20%)
Students will design a self-learning plan based on the question logic taught in class and the knowledge they wish to gain from AI. They will create a structured set of questions to enhance their independent learning and critical thinking skills. Each week, students will be required to research a broad topic of their interest and use their understanding to design their own question sets.
Lab Work (20%)
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) (20%)
Based on previous assignments, students will write a 300-400 word reflection, analyzing their learning process, challenges encountered, and strategies for improvement, thereby enhancing their metacognitive skills.
Presentation (40%)
At the end of the semester, students will work in groups of 4-5 to create a PowerPoint presentation. Each member will showcase their best-designed question from the semester, explaining its underlying logic. Additionally, they will share effective techniques for asking AI questions and key considerations for improving AI interactions.
Final Evaluation Criteria
Students who achieve a total score of 65% or higher will be considered proficient in AI learning strategies. This module directly evaluates studentsā ability to formulate effective AI questions, analyze AI responses, and apply AI knowledge to real-world learning tasks.
Reference
Jo, H., & Park, D.-H. (2024). āEffects of ChatGPTās AI capabilities and human-like traits on spreading information in work environments.ā Scientific Reports, 14(1), 7806. https://doi.org/10.1038/s41598-024-57977-0
Peng, Z., & Wan, Y. (2024). Human vs. AI: Exploring studentsā preferences between human and AI TA and the effect of social anxiety and problem complexity. Education and Information Technologies, 29(1), 1217ā1246.
https://doi.org/10.1007/s10639-023-12374-4
Su, J. (2023, August 1). Master the perfect ChatGPT prompt formula (in just 8 minutes)! YouTube.
Project plan:
Yunyang Ma:Interactivity & Technology; Submission
Xinghan Wang: Inclusion & Accessibility
Fan Xiong: Ensure the logical structure of the content; References & Proofreading
Yingjie Zhang: Content & Structure
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