Personalized learning has been proving to be useful concept in the learning of a student.Artificial Intelligence (AI) which has revolutionized many aspects of our lives has also been glowingly used in the education sector.One of the fascinating AI technique, the Reinforcement Learning (RL) is considered as the perfect tool to develop personalized solution in the education.RL algorithms have the ability to take into account personal characteristics of each student.This work presents the development of personalized exam scheduler using RL.The intelligent examination scheduler consider several parameters for training such as age, academic year, past education performance, discipline, number of courses, and gap between two exams.The trained RL agent then able to provide examination schedule to a student depending on a student personal record, interests and abilities.The preliminary results are encouraging and more research would bring useful contribution of AI in various aspects of learning process of a student.

Recent research has underscored the pivotal role of soft skills in navigating the complexities of today's workplace dynamics.Soft skills encompass a broad spectrum of attributes, such as effective communication, adept collaboration, nimble adaptability, and profound emotional intelligence, all of which are integral to fostering productive team environments and driving organizational success.Despite their acknowledged importance, quantifying and evaluating soft skills has traditionally been hindered by their inherently subjective nature.However, the emergence of artificial intelligence (AI) technologies has revolutionized the landscape of skill assessment, presenting novel opportunities to address these longstanding challenges.By leveraging AI-powered algorithms, organizations can now analyze vast datasets encompassing various facets of human interaction, enabling a more nuanced and objective evaluation of individuals' soft skill proficiencies.Moreover, AIdriven assessments offer scalability, allowing for the efficient evaluation of large cohorts of employees or candidates.Nonetheless, this intersection of AI and soft skills measurement is not without its obstacles.Ethical considerations surrounding data privacy, algorithmic bias, and the potential for automationinduced job displacement necessitate careful scrutiny and regulation.Furthermore, the dynamic nature of soft skills presents a continuous challenge, as individuals must continually adapt and refine their abilities to meet evolving workplace demands.Despite these challenges, the synergistic relationship between AI and soft skills measurement holds immense promise for the future of talent assessment and development.By embracing AI-driven approaches, organizations can cultivate a workforce equipped with the diverse skill set necessary to thrive in an ever-changing professional landscape.

Abstract We prove first-order definability of the prime subring inside polynomial rings, whose coefficient rings are (commutative unital) reduced and indecomposable. This is achieved by means of a uniform formula in the language of rings with signature $(0,1,+,\cdot )$ . In the characteristic zero case, the claim implies that the full theory is undecidable, for rings of the referred type. This extends a series of results by Raphael Robinson, holding for certain polynomial integral domains, to a more general class.