Dr. Marwa Fathi Fotouh Hafour is an Associate Professor of Teaching English as a Foreign Language (TEFL) at the Faculty of Education, Tanta University, Egypt, and an Assistant Professor at Dhofar University, Oman. She earned her PhD in TEFL Curriculum and Instruction from Tanta University, where her research focused on cloud-based collaborative writing to enhance English language learning. She has also served as a visiting faculty member at Florida State University and Michigan State University, USA. Dr. Hafour's research interests include English language teaching, curriculum and instruction, educational technology, collaborative learning, teacher education, and language assessment. She has actively contributed to international research collaborations, academic quality assurance, and university ranking initiatives. Recognized for her academic excellence, she is the first faculty member in an Egyptian College of Education to be promoted through the Excellence Track.

AI-driven Literacy Training: Modeling Preservice and Inservice Teachers’ Acceptance and Adoption of IMALL via TAM Adaptation AbstractThe rapid integration of Artificial Intelligence (AI) into educational environments has necessitated a re-evaluation of teacher preparedness and perceptions, particularly within Intelligent Mobile-Assisted Language Learning (IMALL) frameworks. Within the Technology Acceptance Model (TAM) framework, this study investigates the differential perceived acceptance constructs and the determinants of AI technology adoption among preservice (N= 61) and inservice (N= 56) EFL teachers following a targeted AI literacy training. In a mixed-methods study, utilizing linear mixed effects and multiple linear regression models, the four perceived acceptance constructs: Ease of use, usefulness, personal interest, and pedagogical attitudes were differentially analyzed. Inservice teachers’ better perceptual acceptance of AI suggests that professional classroom experience and desire for workload reduction may moderate teachers’ perceptual frameworks regarding AI utilization. The counter-intuitive significant differences between personal interest and attitudes imply that teachers may be interested in the appealing technical features and speed of the tool but still have skeptical attitudes towards its systematic integration, due to possible loss of human agency. Moreover, personal interest and usefulness were found to be key predictors of the of AI adoption, surpassing technical convenience as well as attitude factors. This suggests that interest in the functional utility of AI will outweigh the worries related to complexity of user interface. These findings imply that, to foster sustained AI integration, it is crucial to stress the utility aspect rather than technical simplicity in developing AI literacy training programs on the use of AI technology. Together, these findings confirm the adaptability of TAM to the IMALL environment.