We present conceptual foundations for artificial intelligence, expert systems, and knowledge engineering and management and discuss high quality in education. Following, we discuss the basics of our vision and prospective about higher education (HE): the battle for the future with digital transformation (DT). Next, we present our central Chapter 4 on DT: our dual model of knowledge and data, as befits a HE institution. Below, we present a succinct outline of our architectural model. The pillars of the architecture are funding, research, entrepreneurship and social projection (Chapter 8); recognizing from the start that knowledge has its ethos in the university; these pillars correspond to: Productive ecosystem of the DT DT that enhances knowledge and innovation in the universities for the habilitation of the digital capabilities A new economy that requires the U transformation as social projection. New DT human talent required by the new knowledge and intelligence industry. The student hyper-personalization by competences and skills is required. Ten Views of Our DT Model, given its complex process that takes place in long ways, a process required for the successful survival of an organization into the IV Industrial Revolution, with the final purpose of being very competitive, productive and of high quality (HQ). The DT views, namely: 1-The DT Ecosystem. 2-The structural vision or the pillars of the DT mentioned. 3-The DT strategic map, showing scenarios, actors and vision-mission. 4-The architectural components for DT: 8 architectures were developed and implemented, applying some intelligent constructs that we have developed and documented in the last 10 years at FESSANJOSE (U. San Jose), leading DT in postsecondary education (PSEd). 5-Digital 360 Degrees architecture of DT Academy and Administration LOCUS: this architecture is the digital portfolio that implies the organization of the subsystems to obtain better and/or new functionalities based on knowledge to obtain an intelligent behavior. The DT multilayer-architecture approach is a system of systems (SoS) one, which ensures compliance with government policies, norms and standards, in a highly complex social institution with digital assets; this approach describes the subsystems at a higher level, where a system is composed, and with the protocols by which the subsystems communicate. It provides a 360 Degrees business vision map and a planning framework for commercial and technological changes. 6-The computational-mathematical perspective of DT, identifying endogenous and exogenous variables and their interrelations. 7-The synthesis, the Matrix of End-Means (EMM) that summarizes in DT: Where the HE is. In addition, where can the HE go? 8-The MIR Matrix, which describes DT Objectives, impacts-indicators and results. 9-The dynamic model of the DT system, based on computational intelligence, representing the system information control of all the components to achieve the completion of the DT. The intelligent management information system (iMIS) for PSEd, shows the dynamics of DT, integrating several multilevel system hybrid architectures, as a space to respond to the solution of the HE problems, tending to the desired competitiveness, specifically pointing out the way that these modern technologies can be included for their adaptation and evolution in PSEd in post-modernity, making governability, and teaching and student productivity compatible with educational high quality, the purpose of DT. The interface Results of the iMIS includes: high quality metrics, digitization rate progress, indicators (an special appendix on KPI were included) and values of management, desertion, answers, and plans. The Input Interface includes data, information and knowledge acquisition, where the attributes, parameters.

Knowledge processing requires the discipline of engineering knowledge in order to solve complex problems normally requiring a high level of expertise, currently used in management information systems (MIS) domains, with artificial and computational intelligence (ACI), intelligent and deductive databases, expert systems (ES), decision support systems, geographic knowledge and information systems, business intelligence (BI), software architectures and others. Knowledge engineering has its own mathematics and body of knowledge; strongly concerning cognitive science. This book includes important conceptual definitions, tools, principles, and applications of knowledge engineering.