This cross-sectional study investigates how preservice teachers in the Global South engage with Generative Artificial Intelligence across academic and instructional tasks while navigating infrastructural barriers such as limited internet access and high data costs. The study surveyed 167 preservice teachers from four teacher education institutions in Ghana. Descriptive statistics and inferential analyses, including multiple and ordinal logistic regressions, were used to examine patterns of GenAI use. Findings show that preservice teachers rely on GenAI as a learning companion for locating reading materials, accessing detailed content explanations, and identifying practical examples. They also use GenAI as a teaching assistant for tasks related to lesson preparation, including generating instructional resources, identifying assessment strategies, and developing lesson objectives. Usage patterns indicate that students in their third and fourth years have significantly higher frequencies of GenAI use compared to those in earlier years. Gender was not a significant predictor of GenAI adoption, in contrast to class level and age. Participants reported positive attitudes toward GenAI, noting that it supports autonomous learning and reduces dependence on peers and instructors for routine academic and teaching activities. However, challenges such as high data costs, occasional inaccuracies in GenAI outputs, and concerns about academic dishonesty were identified as factors that limit more frequent use. The study recommends the integration of GenAI literacy in teacher education programs, with a focus on ethical and responsible AI use to support equitable adoption in the Global South.

翻译：本横断面研究探讨了全球南方地区的职前教师如何在面临网络接入受限、数据成本高昂等基础设施障碍的情况下，将生成式人工智能应用于学术与教学任务。研究对加纳四所教师教育机构的167名职前教师进行了问卷调查。通过描述性统计与推断分析（包括多元及有序逻辑回归），本研究系统考察了生成式人工智能的使用模式。研究发现：职前教师将生成式人工智能视为学习伙伴，用于查找阅读材料、获取详细内容解释及寻找实践案例；同时将其作为教学助手，辅助完成教案准备相关工作，包括生成教学资源、确定评估策略及设计教学目标。使用模式分析表明，三、四年级学生的生成式人工智能使用频率显著高于低年级学生。性别对生成式人工智能的采纳未呈现显著预测作用，而年级与年龄则具有显著影响。参与者对生成式人工智能持积极态度，认为其支持自主学习，并降低了对同伴与教师在常规学术及教学活动中依赖。然而，高昂的数据成本、生成式人工智能输出偶现的不准确性，以及对学术不端行为的担忧，被确认为限制其更频繁使用的关键因素。研究建议将生成式人工智能素养纳入教师教育课程体系，重点关注人工智能使用的伦理与责任规范，以促进全球南方地区的公平采纳。