Pragya Sharma

Introduction: Artificial Intelligence (AI) is reshaping education by facilitating adaptive, personalized, and data-informed learning experiences. Through intelligent tutoring systems, predictive analytics, and AI-powered feedback mechanisms, educators can tailor instruction to meet diverse learner needs while enhancing efficiency and engagement across both traditional and digital learning environments.Objective: this study aims to examine the effective use of artificial intelligence (AI) in education for promoting personalized learning, while upholding key ethical standards. Method: A short communication methodology was employed to synthesize recent developments in AI applications in education. Relevant literature published between 2018 and 2025 was identified through searches of databases including PubMed, Scopus, ResearchGate, and Google Scholar. Studies were selected based on criteria related to the use of AI in personalized learning, assessment, instructional design, and administrative automation. Data were thematically analyzed to evaluate the benefits, implementation strategies, and challenges associated with AI integration.Results: AI-driven tools were found to significantly enhance educational outcomes by enabling proactive (e.g., curriculum design, predictive enrollment analysis) and reactive (e.g., real-time tutoring, automated grading) engagement. Platforms like adaptive learning systems, intelligent tutoring tools, and AI-driven assessment software have enabled personalized learning experiences and eased administrative tasks. However, significant challenges persist, including concerns over data privacy, algorithmic bias, digital literacy gaps, and unequal access to technological infrastructure.Conclusion: AI technologies hold substantial promise in creating more personalized, inclusive, and effective educational ecosystems. By streamlining teaching processes and supporting learner-centered models, AI enhances both pedagogical impact and academic performance. However, ethical concerns and infrastructural barriers must be addressed to ensure responsible and sustainable adoption. Future efforts should focus on aligning AI development with equity, transparency, and accessibility in education.

Plants growing in temperate regions are able to survive freezing temperatures from −5° to −30°C, depending on the species, through a process known as cold acclimation. In the last decade much work has been done on the molecular mechanisms of low temperature (LT) signal transduction and cold acclimation. Mutant studies and microarray analyses have revealed C-Repeat binding factor (CBF) -dependent and -independent signaling pathways in plants. Experimental evidence suggests the existence of ‘potential LT sensors’ but as yet there is no direct proof. A number of signal transducers such as various kinases/phosphatases have been demonstrated but the signal transduction pathways have not been elucidated. An understanding of the molecular basis of the signaling process, however, is of potential practical application. Designing new strategies to improve cold tolerance in crop varieties could increase the plant productivity and also expand the area under cultivation. BioEssays 27:1048–1059, 2005. © 2005 Wiley Periodicals, Inc.