Classrooms, yet evidence on their collective impact remains scattered across single‑tool case studies. This systematic review closes that gap by analyzing nineteen empirical investigations published between 2020 and 2025 that deploy learning‑management systems, mobile applications, extended reality (XR) environments and artificial‑intelligence tutors in K‑12 and tertiary science, technology, engineering, and mathematics (STEM) settings. Using a mixed method comparative synthesis, the study maps how these tools converge into integrated communication ecosystems and how their orchestration shapes conceptual understanding, collaboration dynamics and equity of access. The findings show that learning‑management platforms and mobile messaging now anchor almost three quarters of deployments, providing a backbone for scheduling, resource distribution and threaded dialogue that bridges classroom and home. When these platforms are paired with short, scaffolded XR sessions and real‑time, artificial intelligence‑driven feedback loops, learners achieve double‑digit gains in spatial reasoning, problem-solving and motivation while low‑performing students close achievement gaps by up to thirty per cent. The review also uncovers persistent barriers: bandwidth constraints, high hardware costs and limited teacher data literacy temper the transformative potential of these tools, particularly in rural and linguistically diverse contexts. By clustering technologies, pedagogical approaches and methodological choices into a single analytic matrix, the review offers the first holistic blueprint for designing inclusive, data‑rich STEM ecosystems. However, methodological heterogeneity, study durations shorter than one school term and convenience sampling from high‑connectivity regions limit generalizability and call for longitudinal, multi‑site designs and living evidence reviews. Overall, the study argues that communication technologies will reach their full promise only when technical ingenuity is matched by pedagogical wisdom and structural commitment.

This study aims to examine the impact of university students’ motivations for social media use (information seeking, socialization, entertainment, and identity formation) on their intentions to use artificial intelligence (AI)-powered learning tools such as ChatGPT, Gemini, and Copilot. Designed within the framework of the technology acceptance model (TAM), the research addresses the mediating role of perceived utility and the moderating role of digital literacy. The study aims to contribute to the literature by understanding how social media habits evolve into academic technology adoption processes. This research, based on a sample of 370 university students, investigates the relationship between various social media motivations (social connection/FOMO, popularity/identity formation, appearance/impression management, and civic/advocacy) and behavioral intention (BI) to use AI learning tools. Perceived usefulness (PU) is included as a mediating variable in this relationship. Results from regression-based mediation analyses (PROCESS Model 4 equivalent) indicate that social media use motivations significantly predict both PU and BI. The indirect effect through PU was statistically significant (ab = 0.248, SE = 0.062, z = 3.988, p < .001), supporting a partial mediation model. Civic/advocacy motivations demonstrated the strongest relationship with PU and BI among subscales. These findings advance understanding of technology adoption in educational contexts and highlight the role of social media usage patterns in shaping AI tool adoption.