Corporate climate disclosures have come to the fore of measuring environmental responsibility, but worries about greenwashing of exaggeration or parts of the environmental performance of exaggerating or overselling environmental performance remain. This paper fulfills this crucial gap in establishing the validity of such revelations by offering the machine learning method of identifying possible greenwashing. It is probable that the mixed-methods design has been used, where the textual analysis of the composed corporate sustainability reports and supervised learning algorithms trained on labeled examples of misleading statements are supplemented. Through the implementation of natural language processing and classification algorithms, the model will recognise patterns that are suggestive of a lack or even exaggeration of commitment with regard to climate pledges. The findings can be used to illustrate industry-related patterns and important language indications linked to greenwashing.

The AI is transforming how countries learn by providing adaptable, data-driven learning experiences that respond to learners' cognitive states, behavioral patterns, and educational paths. This paper explores the convergence of AI and educational psychology, investigating how an AI-assisted learning system operationalizes recent psychological concepts, such as cognitive load theory, self-regulated learning (SRL), and Vygotsky's Zone of Proximal Development, to maximize the instructional process. Based on recent empirical data from meta-analyses and randomized controlled trials, this research reports that AI-supported learning significantly increases academic performance (cohensions d = 0.68, 95% CI [0.41, 0.95]) compared with traditional learning. Moreover, AI-personalized learning systems reduce additional cognitive load by 34 percent among learners with cognitive deficits. The evidence indicates that AI is useful for learning when it provides purposeful practice and profound thinking that align with learners' cognitive development.

This study maps the intellectual structure and thematic evolution of artificial intelligence (AI) in digital marketing through a bibliometric analysis of 827 Scopus-indexed articles (1986–2026). Utilizing Bibliometrix and VOSviewer, performance analysis and science mapping reveal recent, accelerated growth across interdisciplinary outlets. China, the US, and India lead productivity, while Malaysia, Jordan, Australia, and the UK drive international collaboration. Thematic mapping identifies machine learning for commerce and consumer behavior as a dominant motor theme. In contrast, human-centric social media research remains a specialized niche, and generative AI emerges as a fast-growing frontier. Furthermore, co-citation networks expose a three-pillar intellectual foundation rooted in digital transformation, quantitative methodology, and technology adoption behavior. Ultimately, this long-horizon science map highlights strategic opportunities to integrate emerging generative AI and human-centric approaches with established, analytics-driven marketing frameworks.