CHALLENGES OF USING PIXLR EXPRESS AI IMAGE GENERATOR AMONG RESOURCE-CONSTRAINED RURAL PRIMARY SCHOOL ENGLISH TEACHERS
DOI:
https://doi.org/10.35631/IJEPC.1163019Keywords:
Artificial Intelligence in Education, Pixlr Express AI, Rural Education, Teacher Challenges, TPACK FrameworkAbstract
This study explores the challenges faced by resource-constrained rural primary school English teachers in using the Pixlr Express AI Image Generator for instructional purposes. Grounded in the process-oriented framework (before, during, and after teaching) and informed by the Technological, Pedagogical, and Content Knowledge (TPACK) model, the study adopts a qualitative case study design to provide an in-depth understanding of AI integration in a low-enrolment rural school context in Malaysia. Data were collected through semi-structured interviews with four non-specialist English teachers and analysed using thematic analysis. The findings reveal four major interrelated challenges across all instructional phases: limited digital infrastructure, lack of professional development training, time constraints, and insufficient teaching qualifications. Before teaching, unstable internet connectivity, lack of AI-related training, and heavy workloads restricted teachers’ ability to prepare effective teaching aids. During teaching, non-English major teachers struggled to generate accurate prompts, leading to less relevant AI-generated content, while time limitations hindered real-time instructional adaptation. After teaching, the lack of training and content knowledge limited the use of AI tools for remedial instruction and assessment, further compounded by persistent connectivity issues. The study concludes that although Pixlr Express has the potential to enhance visual pedagogy and support differentiated learning, its practical implementation in rural low-enrolment schools remains constrained by systemic and contextual barriers. The findings highlight a critical imbalance in TPACK competencies, particularly in technological and pedagogical domains. To address these issues, the study recommends targeted professional development, improved digital infrastructure, workload management strategies, and policy interventions to support non-specialist teachers. Overall, this research contributes to rural education literature by providing empirical insights into the realities of AI integration in under-resourced educational settings.
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