Modes and models of learning and instruction have shown a significant shift from yesterday's conventional learning and teaching given this era's current educational and social contexts. Learners are no longer learning and communicating with human-generated, computed, and mediated-or traditional-learning and instructional practices, paving the way for machine-facilitated communication, learning, and teaching tools. Learning and instruction, communication and information exchange, as well as gathering, coding, analyzing, and synthesizing data have proven to be in need of even more innovative technology-moderated tools. Applications of Machine Learning and Artificial Intelligence in Education focuses on the parameters of remote learning, machine learning, deep learning, and artificial intelligence under 21st-century learning and instructional contexts. Covering topics such as data coding and social networking technology, it is ideal for learners with an interest in the deep learning discipline, educators, educational technologists, instructional designers, and data evaluators, as well as special interest groups (SGIs) in the discipline.

Creating technology-integrated learning environments for adolescent and adult language learners remains a challenge to educators in the field. Thoroughly examining and optimizing the experience of these students is imperative to the success of language learning classrooms. Technology-Assisted ESL Acquisition and Development for Nontraditional Learners provides innovative insights into the advancements in communication technologies and their applications in language learning. The content within this publication covers emerging research on instructional design, teacher cognition, and professional development. It is a vital reference source for educators, academics, administrators, and researchers seeking coverage centered on the implementation of technology-based language learning systems.

Modes and models of learning and instruction have shown a significant shift from yesterday's conventional learning and teaching given this era's current educational and social contexts. Learners are no longer learning and communicating with human-generated, computed, and mediated-or traditional-learning and instructional practices, paving the way for machine-facilitated communication, learning, and teaching tools. Learning and instruction, communication and information exchange, as well as gathering, coding, analyzing, and synthesizing data have proven to be in need of even more innovative technology-moderated tools. Applications of Machine Learning and Artificial Intelligence in Education focuses on the parameters of remote learning, machine learning, deep learning, and artificial intelligence under 21st-century learning and instructional contexts. Covering topics such as data coding and social networking technology, it is ideal for learners with an interest in the deep learning discipline, educators, educational technologists, instructional designers, and data evaluators, as well as special interest groups (SGIs) in the discipline.

Creating technology-integrated learning environments for adolescent and adult language learners remains a challenge to educators in the field. Thoroughly examining and optimizing the experience of these students is imperative to the success of language learning classrooms. Technology-Assisted ESL Acquisition and Development for Nontraditional Learners provides innovative insights into the advancements in communication technologies and their applications in language learning. The content within this publication covers emerging research on instructional design, teacher cognition, and professional development. It is a vital reference source for educators, academics, administrators, and researchers seeking coverage centered on the implementation of technology-based language learning systems.