This Sage case study explores how early childhood educators developed computational thinking skills through a culture-based educational design model amid the challenges of online learning during a global pandemic. The research highlights a structured approach using e

This article presents CryptoAgent, a novel agentic AI system designed to enhance human-computer interaction in the domain of financial security. The system integrates advanced encryption protocols with adaptive learning mechanisms to ensure secure, responsive, and context-aware interactions between users and AI-driven financial systems. By merging natural language processing with real-time threat detection, CryptoAgent exemplifies a next-generation approach to cybersecurity—prioritizing both user autonomy and system intelligence. The work contributes to emerging scholarship on agentic AI systems, particularly in high-stakes environments where trust, transparency, and adaptiveness are paramount.

This study presents a cross-national quantitative analysis of how university students in the United States and Bangladesh interact with Large Language Models (LLMs). Based on an online survey of 318 students, results show that LLMs enhance access to information, improve writing, and boost academic performance. However, concerns about overreliance, ethical risks, and critical thinking persist. Guided by the AI Literacy Framework, Expectancy-Value Theory, and Biggs' 3P Model, the study finds that motivational beliefs and technical competencies shape LLM engagement. Significant correlations were found between LLM use and perceived literacy benefits (r = .59, p < .001) and optimism (r = .41, p < .001). ANOVA results showed more frequent use among U.S. students (F = 7.92, p = .005) and STEM majors (F = 18.11, p < .001). Findings support the development of ethical, inclusive, and pedagogically sound frameworks for integrating LLMs in higher education.