Designing a chatbot to improve English reading comprehension among Emirati students

Instructional technology is a modest technology over the years, which has helped improve the teaching and learning process in many ways. This transition from conventional instructional media to resourceful AI and technological students has significantly changed instructional approaches. The basic objective o this research is to enhance learning with the help of AI-powered text adaptation, application of game design elements, and vocabulary support. Using the Successive Approximation Model (SAM), the research will follow design, pilot, feedback, revision, and evaluation as steps to enhance the tool's effectiveness in addressing the challenges students and educators face. The research primarily intends to fill the gaps above by helping Emirati students in public schools enhance their understanding of reading comprehension, develop their vocabulary, compose various reading resources, and interact more effectively with traditional forms of instruction. A few of the technologies that are primarily required in this tool creation include: AI and Natural Language Processing (NLP), Machine Learning Algorithms, and Cloud-Based Learning Management System (LMS) Integration. the study will use a mixed method to establish the level of difference in comprehension among the participants influenced by the chatbot. This entails the use of both census and survey data collection techniques. The quantitative data will be collected through questionnaires, while the qualitative data will be obtained from interviews. By so doing, the researchers are in a unique position to combine quantitative results with the users' overall subjective impression of the chatbot and its efficiency. The readiness assessment reveals technological and organisational readiness for the AI-powered reading chatbot for student learning, with favourable technology readiness parameters and perception to embrace innovations by the teachers.