As artificial intelligence (AI) becomes increasingly embedded in early and primary education through apps, virtual assistants, and adaptive learning tools, fostering critical thinking in children (ages 3–11) is vital for responsible interaction with AI. This chapter, “Developing Critical Thinking: Helping Children Question AI Outputs,” explores ways to help young learners evaluate AI-generated content with curiosity and discernment. It introduces AI in the context of children's daily lives and cognitive development, covering bias, reliability, and errors. The chapter offers age-appropriate teaching strategies, classroom activities such as analyzing AI stories, and discussions on fairness. It highlights collaboration with parents and communities to support learning beyond school. By nurturing critical thinking as a foundation of AI literacy, this chapter aims to prepare children to engage with AI ethically, thoughtfully, and confidently in an increasingly digital world.

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.

The chapter will define employee engagement by focusing on its practical implications for organizations. Employee engagement will be explored as employees' commitment and enthusiasm toward their work and organization. It will highlight the key components distinguishing engagement from job satisfaction, such as emotional investment and willingness to go beyond essential responsibilities. The chapter will also clarify why engagement is crucial for organizational success. High engagement will improve productivity, customer satisfaction and lower turnover rates. By examining these aspects, the chapter will demonstrate how engaged employees contribute directly to achieving business objectives.