Xiangping Cui

Institute of Higher Education, Lanzhou University, China // cuixp@lzu.edu.cn

Jiangming Qian

Institute of Higher Education, Lanzhou University, China // qjm52413@zju.edu.cn

Juan Xu

School of Humanities, Communication University of China, China // xujuan@cuc.edu.cn

Shiping Li

Institute of Higher Education, Lanzhou University, China // shipli@lzu.edu.cn

Jun Shen

School of Computing and Information Technology, University of Wollongong, Australia // jshen@uow.edu.au

ABSTRACT: 

Digitalization of textbooks constitutes a pivotal aspect of the digital transformation of education, playing an indispensable role in the development of a modern education system. Currently, the construction of digital textbooks in higher education lacks a shareable and reusable model. Crowdsourcing is an innovative concept that leverages collective wisdom to improve the quality of resources, transcending organizational boundaries and facilitating the flow of knowledge. This study adopts the design-based research paradigm and comprehensively applies a variety of research methods to construct and implement a crowdsourcing-based construction model for digital textbooks in higher education. The model includes a knowledge subsystem, a process subsystem, and an organizational subsystem, covering crowdsourcing participants such as professionals, ordinary participants and artificial intelligence. The digital textbook developed based on this framework has demonstrated significant user satisfaction in terms of content, teaching, technical design, evaluation, opportunities for deep learning, accessibility, and security. It is expected that the construction model of the digital textbooks in high education built in this study can further enrich the research outcomes of digital textbooks, and provide a model reference for digital textbook construction in higher education.

Keywords: 

Digital textbooks, Crowdsourcing, Generative artificial intelligence, Model construction

Ali Derakhshan

Department of English Language and Literature, Faculty of Humanities and Social Sciences, Golestan University, Gorgan, Iran // a.derakhshan@gu.ac.ir

Di Zou

Department of English, Head, Centre for English and Additional Languages, Lingnan University, 8 Castle Peak Road, Tuen Mun, New Territories, Hong Kong // dizou@ln.edu.hk

Saeed Khazaie

Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran // saeed.khazaie@gmail.com

ABSTRACT: 

Recent advancements in Artificial Intelligence-powered robots have introduced a variety of educational technologies aimed at accommodating standard varieties of languages. However, the adaptiveness of the knowledge bases of these robots in establishing adaptive language learning at universities remains underexplored. This quasi-experimental study involved 1041 students from 13 academic disciplines with diverse first languages. Utilizing a parallel design, while students in the control group learned medical English skills through non-adaptive AI-powered robots, students in the experimental group learned the same skills through adaptive AI-powered robots. We assessed the effectiveness of learning medical English through Artificial Intelligence-powered robots with(out) adaptive knowledge bases, employing Objective Structured Video Exams for formative assessment. Additionally, the participants shared their perceptions of Artificial Intelligence-powered robots in medical English education through semi-structured interviews. Quantitative findings indicated that students using adaptive Artificial Intelligence-powered robots showed a greater sociolinguistic competence in medical English compared to those learning with non-adaptive robots. The qualitative findings suggested that the students in disciplines requiring direct interaction with patients and healthcare staff expressed a more positive perception of learning medical English through Artificial Intelligence-powered robots. While both groups acknowledged the value of the course, those engaging with adaptive systems demonstrated a great appreciation for the learning process and outcomes. Based on these results, we offer recommendations for researchers on effectively integrating Artificial Intelligence-powered robots into university language education, highlighting the potential for enhanced learning experiences through adaptive technologies.

Keywords: 

Adaptive learning, Artificial intelligence, Medical English, Robots, Sociolinguistic competence