The paper talks about the curriculum development and implementation, as well as the survey feedbacks from both teachers and students. In this paper the authors highlight the importance of creating a curriculum that is both socially relevant and engaging for the students. It's particularly crucial for female students, as there is a lack of diversity in the technology industry and creating a curriculum that recognizes the importance of encouraging and empowering more women to enter the technology industry.
The project used "AI4K12 Big Idea 3 (Learning) progression chart, as it had high school level objectives specifically relating to machine learning algorithms, neural networks, and datasets".
The curriculum takes into account the needs and interests of female students. It focused on socially relevant topics, such as using AI to tackle environmental issues or AI for Criminal Justice, the authors made the subject matter more engaging and relevant to the students. Also, the authors incorporated hands-on projects and case studies to give the students practical experience in using AI. This helped to deepen their understanding of the subject and made the learning process more fun and effective.
The authors also emphasized the importance of creating a community of support for the female students and recognizing the challenges faced by female students in technology-related fields. This is essential, as female students are often underrepresented in technology-related fields and can benefit from a supportive learning environment.
The article is a valuable contribution to the field of AI education, particularly in terms of encouraging and empowering female students. The focus on making the curriculum socially relevant and engaging, as well as incorporating hands-on projects, and the emphasis on creating a supportive learning environment is important and can play a critical role in encouraging more women to enter the technology industry.
In this paper the authors assessed the students learning outcome by both QUALITATIVE and QUANTITATIVE methods. Quantitatively, the authors used pre and post surveys. It is an effective way to measure the understanding of AI concepts within the middle school students. Qualitatively, the authors interviewed students to understand about their perception of their program and how it impacted their learning. "After the Pop- Bots activities, children’s previous experiences with robots still had a powerful hold on their perceptions, emphasizing the importance of early childhood AI education as a means to help next generation AI citizens to form unbiased beliefs about AI technology." This shows students motivation towards the AI technology and how can they use it for their community hence communicating the idea of AI4ALL.
The paper also presented data on students' engagement and participation in the program, which gave the authors a better understanding of the factors that influenced the students' learning outcomes. This information was particularly useful in identifying any challenges that students faced during the program and how they could be addressed in the future.
The combination of qualitative and quantitative methods helped to provide a nuanced understanding of the students' experiences and their knowledge gains from the Pop-Bots program.
I really liked the usage of tool DoodleIt to visually explain working of CNN model with 1 layer of neural network. The tool allows students to create and manipulate their own images, and then see how the CNN processes these images and makes predictions. This approach provides a dynamic and interactive way for students to learn about the intricate concepts behind CNNs. Use of visual, interactive tools like DoodleIt can be an effective way of teaching complex concepts in the field of AI and machine learning.
ChatGPT calculating 8 X 3 = 26 in a math question LOL!!