Artificial Intelligence (AI), particularly in the form of intelligent AI agents, is transforming education, industry, and everyday life. These agents extend the capabilities of Large Language Models (LLMs) by integrating planning, decision-making, tool use, and multi-agent collaboration, enabling systems that can reason, adapt, and act in dynamic environments. As such systems become integral to modern workplaces and everyday problem solving, early exposure equips high school students with systems thinking, practical problem-solving skills, and ethical awareness, while preparing them to create applications that address real-world needs. Yet most high school AI programs focus on basic model usage and overlook the skills required to design and deploy agentic systems. Existing resources are largely aimed at university learners and assume substantial programming expertise, creating a significant accessibility gap. To address this need, we present a structured hackathon-based framework for introducing high school students to the design and application of AI agents. The framework combines expert-led lectures on core topics such as agent architectures, prompting strategies, reasoning methods, and tool-use protocols with a guided hackathon in which students collaboratively develop domain-specific agent-based chatbots. We provide a complete suite of instructional materials, step-by-step tutorials, and starter code to support hands-on learning, enabling participants to build functional agents capable of reasoning and interacting with external tools. Our approach bridges the gap between AI literacy and practical deployment while fostering creativity, collaboration, and responsible innovation, and our findings suggest that early engagement with agent-based AI design equips students with both technical proficiency and the mindset to shape the AI-driven future.

It is still challenging to effectively identify rumors due to rapid changes in people's interests and perceptions. To enhance rumor detectors, we first need to better understand which rumors are effective (in terms of bypassing detection) and their characteristics. In this paper, we introduce ARGH, a novel framework to automatically generate rumors using recent advancements in natural language processing, customized to target and generate specific topics. To show the effectiveness of ARGH, we conducted a user study with 212 participants and analyzed how well humans can detect the rumors generated by ARGH, and we also tested its performance against the state-of-the-art rumor detection model PLAN [17]. Surprisingly, the experimental results demonstrate that the generated rumors are significantly harder to identify as rumors than hand-written rumors, degrading the detection accuracy by both humans and machines by 18.87% and 17.62%, respectively. We believe that ARGH will be a useful tool to obtain high quality and evasive rumor datasets quickly, which is often a tedious and time consuming task. Further, our analysis results provide valuable insight into how to characterize evasive rumors and how they can be generated, which will help to enhance the existing rumor detection techniques.