Eric C. K. Cheng - Artificial Intelligence in Education: Emerging Technologies, Models and Applications: Proceedings of 2021 2nd International Conference on Artificial Intelligence in Education Technology

Rapid developments in artificial intelligence (AI) and AIs appalling potential in educational use have drawn significant attention from the education community in recent years. Many theoretical and practical questions concerning AI in education (AIED) are raised for educators entering this uncharted territory, and issues on AIs technical, pedagogical, administrative and socio-cultural implications are being debated.

This book is a compendium of selected research papers presented at the 2nd International Conference on Artificial Intelligence in Education Technology (AIET 2021)held in Wuhan, China, on July 24, 2021. AIET establishes a platform for education researchers in AI to present research, exchange innovative ideas, propose new models and demonstrate advanced methodologies and novel systems.

This timely publication is well-aligned with UNESCOs Beijing Consensus on Artificial Intelligence and Education. It is committed to exploring how best to prepare our students and harness emerging technologies for achieving the Education 2030 Agenda as we move towards an era in which AI is transforming many aspects of our lives. Providing broad coverage of recent technology-driven advances and addressing several learner-centric themes, the book is an informative and valuable resource for researchers, practitioners, education leaders and policy-makers who are involved or interested in AI and education.

The 26 papers in this book are divided into five main parts(1) An Overview of AI in Education, (2) AI Technologies and Innovations, (3) Teaching and Assessment across Curricula in the Age of AI, and (4) Ethical, Socio-cultural and Administrative Issues in Education of an AI Era. The book provides a comprehensive picture of the current status, emerging trends, innovations, technologies, applications, challenges and opportunities of current AIED research through these sections.

In chapter , Tianchong Wang and Eric C. K. Cheng conduct a scoping review of research studies on AIED published over the last two decades (20012021). A wide range of empirical studies are collected in the selected databases related to the field, and they are analysed with content analysis and categorical meta-trends analysis. Three distinctive AIED research directions, namely Learning from AI, Learning about AI, and Learning with AI, are identified. By depicting the AIED research directions, this chapter serves as a blueprint for AIED researchers to position their up-and-coming AIED studies for the next decade.

In chapter , Zhao Yaru, Li Chaoqian, Tian Xinyu, Chen Yunhong and Li Shuming present their analysis of research progress in the field of educational technology in China. Using the CNKI database as the primary source, visual and statistical analysis was conducted with 6,740 CSSCI journal articles published from 2015 to 2020. As a result, key research trends, gaps and possible future directions for Chinas educational technology research are revealed.

In chapter , Shuting Li and Lu Han introduce a two-stage Named Entity Recognition (NER) method, a significant subtask for Natural Language Processing (NLP). By exploiting the NER approaches to recognise the entities about papers occurring in Mathematical Contest in Modelling (MCM)the most influential international mathematical modelling competitionthe authors provide some reference for the study of modelling methods in the future.

In chapter , Jos Medardo Tapia-Tllez, Aurelio Lpez-Lpez and Samuel Gonzlez-Lpez present a computational-linguistic analysis, evaluation and feedback for academic documents based on three comprehensibility measures: Connectivity, Dispersion, and Comprehensibility Burden. Such NLP measures are explored and validated. Visualisation feedback during the assessment process is proposed and illustrated, which may be applied to the comprehensibility assessment as it understands and resolves detected language deficiencies.