In the era of artificial intelligence (AI), the field of educational testing faces significant challenges, particularly in test development and scoring in adaptive assessment, two key innovations include automated item generation (AIG) and automated scoring (AS).  Only recently that generative AI has facilitated the development of complex test items on a large scale. We introduce “the item factory”, for managing large-scale test development including automation of item generation, quality review, quality assurance, and crowdsourcing techniques in adaptive testing. We present an overview of the latest natural language processing (NLP) techniques and large language models for AIG, alongside psychometric principles and practices for test development. We discuss the application of engineering principles in designing efficient item production processes (Luecht, 2008; Dede et al, 2018; von Davier, 2017).  As AS becomes an integral part of the assessment landscape due to their advantages in reporting time, cost, objectivity, consistency, transparency, and feedback. We aim to demystify AS and provide a comprehensive understanding of its workings. We offer an overview of the design, development, evaluation, and quality control of automated scoring systems, along with practical advice and considerations for practitioners on the applications of these systems into formative and summative assessments (Yan, Rupp, & Foltz, 2020). We will share the most recent AI applications in educational learning and assessment based on our upcoming volume AI for Measurement in Educational Learning and Assessment (von Davier and Yan, 2026).