When executed well, project-based learning (PBL) engages students' intrinsic motivation, encourages students to learn far beyond a course's limited curriculum, and prepares students to think critically and maturely about the skills and tools at their disposal. However, educators experience mixed results when using PBL in their classrooms: some students thrive with minimal guidance and others flounder. Early evaluation of project proposals could help educators determine which students need more support, yet evaluating project proposals and student aptitude is time-consuming and difficult to scale. In this work, we design, implement, and conduct an initial user study ( n = 36) for a software system that collects project proposals and aptitude information to support educators in determining whether a student is ready to engage with PBL. We find that (1) users perceived the system as helpful for writing project proposals and identifying tools and technologies to learn more about, (2) educator ratings indicate that users with less technical experience in the project topic tend to write lower-quality project proposals, and (3) GPT-4o's ratings show agreement with educator ratings. While the prospect of using LLMs to rate the quality of students' project proposals is promising, its long-term effectiveness strongly hinges on future efforts at characterizing indicators that reliably predict students' success and motivation to learn.

The emergence of generative AI, particularly large language models (LLMs), has opened the door for student-centered and active learning methods like project-based learning (PBL). However, PBL poses practical implementation challenges for educators around project design and management, assessment, and balancing student guidance with student autonomy. The following research documents a co-design process with interdisciplinary K-12 teachers to explore and address the current PBL challenges they face. Through teacher-driven interviews, collaborative workshops, and iterative design of wireframes, we gathered evidence for ways LLMs can support teachers in implementing high-quality PBL pedagogy by automating routine tasks and enhancing personalized learning. Teachers in the study advocated for supporting their professional growth and augmenting their current roles without replacing them. They also identified affordances and challenges around classroom integration, including resource requirements and constraints, ethical concerns, and potential immediate and long-term impacts. Drawing on these, we propose design guidelines for future deployment of LLM tools in PBL.

"From Unseen Needs to Classroom Solutions": Exploring AI Literacy Challenges & Opportunities with Project-Based Learning T oolkit in K-12 Education Hanqi Li * 1, Ruiwei Xiao * 2, Hsuan Nieu 3, Ying-Jui Tseng 2, Guanze Liao 3 1 New Y ork University 2 Carnegie Mellon University 3 Taiwan National Tsing Hua University hl4893@nyu.edu, Abstract As artificial intelligence (AI) becomes increasingly central to various fields, there is a growing need to equip K-12 students with AI literacy skills that extend beyond computer science. This paper explores the integration of a Project-Based Learning (PBL) AI toolkit into diverse subject areas, aimed at helping educators teach AI concepts more effectively. Through interviews and co-design sessions with K-12 teachers, we examined their current AI literacy levels and how these teachers adapt AI tools like the AI Art Lab, AI Music Studio, and AI Chatbot into their course designs. While teachers appreciated the potential of AI tools to foster creativity and critical thinking, they also expressed concerns about the accuracy, trustworthiness, and ethical implications of AI-generated content. Our findings reveal the challenges teachers face, including limited resources, varying student and instructor skill levels, and the need for scalable, adaptable AI tools. This research contributes insights that can inform the development of AI curricula tailored to diverse educational contexts. Introduction As accessible Artificial Intelligence (AI) tools have gained increasing interest among K-12 educators in incorporating AI literacy into their classrooms. K-12 educators recognize the need to teach students about its capabilities and limitations(Ng et al. 2023a). Existing AI education efforts focus on dedicated curricula and professional learning for teachers (Amplo and Butler 2023; Lee and Perret 2022).

This study explores the integration of real-world machine learning (ML) projects using human-computer interfaces (HCI) datasets in college-level courses to enhance both teaching and learning experiences. Employing a comprehensive literature review, course websites analysis, and a detailed case study, the research identifies best practices for incorporating HCI datasets into project-based ML education. Key findings demonstrate increased student engagement, motivation, and skill development through hands-on projects, while instructors benefit from effective tools for teaching complex concepts. The study also addresses challenges such as data complexity and resource allocation, offering recommendations for future improvements. These insights provide a valuable framework for educators aiming to bridge the gap between theoretical knowledge and practical application in ML education.

Inquiry-based learning (actuated by inquiry-based teaching questions) and project-based learning have long been recognized as powerful ways to engage students in meaningful, authentic learning experiences. By empowering students to ask questions, explore ideas, and create their own solutions, these approaches foster curiosity, creativity, and critical thinking skills that can benefit students long after they leave the classroom. As a teacher, I've seen firsthand the transformative impact that project-based learning and inquiry teaching can have on students. But I've also recognized the challenges that come with implementing these approaches effectively. From designing engaging projects to managing student inquiry, there are many factors that can make or break the success of these teaching strategies.

Building and maintaining production-grade ML-enabled components is a complex endeavor that goes beyond the current approach of academic education, focused on the optimization of ML model performance in the lab. In this paper, we present a project-based learning approach to teaching MLOps, focused on the demonstration and experience with emerging practices and tools to automatize the construction of ML-enabled components. We examine the design of a course based on this approach, including laboratory sessions that cover the end-to-end ML component life cycle, from model building to production deployment. Moreover, we report on preliminary results from the first edition of the course. During the present year, an updated version of the same course is being delivered in two independent universities; the related learning outcomes will be evaluated to analyze the effectiveness of project-based learning for this specific subject.

The Artificial Intelligence revolution is here. That might sound like hyperbole. After all, the world looks the same. The revolution didn't arrive with Skynet and robots or with Blade Running cyborgs. A small chat at the bottom right hand corner. If you're imagining Siri or Alexa or even Clippy (Rest in Peace, Clippy), it's so much more than that.

Three years ago, I stood in a high-tech lab at a technical university and stared at the giant flat screen monitor. The professor asked me, "Can you tell me which of these samples was generated via Artificial Intelligence?" At the top, a student had asked a complex question related an application of calculus for calculating fluid flow and heat transfer. Below, a graduate student and an AI chat bot had both answered the question. I studied both answers over and over again. I looked for clues in the syntax. I tried to find the humanity within the the answers. Finally, I shook my head. I couldn't tell the difference. Both options were clear and concise with just a touch of colloquial friendliness while still remaining slightly cold and objective.

Artificial intelligence refers to a computer's ability to perform tasks that are similar to human intelligence. This includes concepts like image detection, pattern recognition, and natural language processing. AI capabilities can be applied to a variety of applications in different industries, such as purchase recommendations or self-drive cars. Artificial Intelligence is a common tool for children today. Virtual assistants like Siri and Alexa, as well as smart devices, are just a few examples. A basic understanding of artificial intelligence will help children to understand these devices and their functions.

Did you know Education Ecosystem is like GitHub on video for developers? It is project-based learning ecosystem that teaches you how to build products using real project examples in fields of AI, programming, data science, design, game development, cryptocurrency and cybersecurity.