Leadership plays a central role in employee retention. As demonstrated by Thakur et al. (2019) leadership refers to the ability to inspire and influence employees and colleagues. With rapid globalization and fierce competitive pressures, firms are increasingly looking at maximizing the potential of their followers to ensure that they have a competitive edge. Udovita (2020) revealed that leaders can play a paramount role in shaping the attitudes and behavior of their followers, thereby propelling them to their full potential. Given the increasing knowledge and information intensity, labor has evolved to become the key strategic competitive weapon in business. Perhaps the most important task of a leader is to maintain a team of motivated and capable followers that can continue to deliver results.

This chapter proposes a hands-on approach to AI education for children aged 6-12, focusing on building simple AI projects without the need for coding, a barrier I've seen firsthand in my work with technology integration. Leveraging my experience in system design, I aim to create activities that allow young learners to interact with AI concepts through tangible, code-free methods. For instance, children could design a basic AI-driven sorting game using physical objects like colored blocks, where they define rules for an AI to follow, such as grouping by color or size, using pre-built tools like Google Teachable Machine's image recognition features. Another activity might involve creating an AI-assisted storyboard, where children input ideas into a drag-and-drop interface to generate a narrative, then refine it based on their creative vision. These projects are grounded in experiential learning principles, ensuring children actively engage with AI processes while bypassing technical hurdles.

Multimodal misinformation detection has advanced from text-only classification toward joint processing of language, imagery, and social metadata. Yet much of the literature still treats adaptation as an internal fusion problem rather than a systems-level routing problem, leaving open the question of when multimodal escalation is warranted and how expert usage can be reduced without materially weakening predictive quality. This paper introduces MemANS, an event-driven, in-memory Agentic Name Search architecture for multimodal misinformation detection. MemANS treats inference as a resolution process over named experts: a default text–metadata resolver handles ordinary cases, while a specialist is invoked only when cross-modal disagreement and confidence conditions jointly indicate that additional reasoning is justified. The empirical study is conducted on a balanced, fully image-available Fakeddit benchmark comprising 5400 training instances, 840 validation instances, and 840 test instances across six misinformation classes. On the final test set, MemANS achieves 0.6182 macro-F1, 0.6167 accuracy, 0.5405 MCC, and 0.8726 weighted one-vs-rest AUC, clearly surpassing the always-on fusion baseline (0.5732 macro-F1) with a statistically supported gain of 0.0454 (p < 0.001) while using only 1.7202 average experts versus 2.0000. The results indicate that Agentic Name Search is a viable and practically meaningful multimedia-systems abstraction for adaptive multimodal reasoning.