Güneyli Ahmet, Burgul Nazım Serkan, Dericioğlu Sonay, Cenkova Nazan, Becan Sinem, Şimşek Şeyma Elif, Güneralp Hüseyin
Faculty of Education, European University of Lefke, Lefke 99728, Northern Cyprus, Mersin 10, Türkiye.
Faculty of Sports Sciences, Near East University, Lefkoşa 99138, Northern Cyprus, Mersin 10, Türkiye.
Eur J Investig Health Psychol Educ. 2024 Aug 12;14(8):2358-2376. doi: 10.3390/ejihpe14080156.
This study investigates the level of awareness among teachers regarding the use of artificial intelligence (AI) in education, focusing on whether this awareness varies according to socio-demographic characteristics, access to technology, and specific knowledge and beliefs about AI. Conducted in Northern Cyprus during the 2023-2024 academic year, this study employed a survey model with purposive and snowball sampling methods, involving 164 teachers. Teachers at different levels, namely, primary school, secondary school, high school, and university, were included in this study. The "Artificial Intelligence Awareness Scale", developed by Ferikoğlu and Akgün (2022), was used to measure AI awareness. Data normality was verified through skewness and kurtosis values, allowing for parametric statistical tests such as t-tests, one-way ANOVA, logistic regression, and chi-square analysis. This study explored the distribution of AI use across different school types and educational levels and assessed the impact of sub-dimensions of AI awareness on its application in teaching. Findings revealed no significant influence of teacher demographics (age, gender, education level, type of school, institution level, and monthly income) on AI awareness. However, usage patterns indicated that university lecturers were more likely to incorporate AI in their teaching, followed by primary and high school teachers, with secondary school teachers using it the least. A Multilayer Neural Network Analysis identified practical knowledge as the most critical factor influencing the use of AI in teaching (importance weight of 0.450), followed by beliefs and attitudes (0.298), relatability (0.148), and theoretical knowledge (0.104). These results highlight the importance of practical knowledge for fostering AI integration in educational practices, underscoring significant implications for teacher training and professional development programs.
本研究调查了教师对人工智能在教育中应用的认知水平,重点关注这种认知是否因社会人口特征、技术获取情况以及对人工智能的特定知识和信念而有所不同。该研究于2023 - 2024学年在北塞浦路斯进行,采用了目的抽样和滚雪球抽样方法的调查模型,涉及164名教师。本研究纳入了不同层次的教师,即小学、中学、高中和大学教师。使用了费里科奥卢和阿克坤(2022年)开发的“人工智能认知量表”来衡量人工智能认知。通过偏度和峰度值验证了数据正态性,从而可以进行参数统计检验,如t检验、单因素方差分析、逻辑回归和卡方分析。本研究探讨了人工智能在不同学校类型和教育层次中的使用分布情况,并评估了人工智能认知的子维度对其在教学中应用的影响。研究结果显示,教师的人口统计学特征(年龄、性别、教育水平、学校类型、机构层次和月收入)对人工智能认知没有显著影响。然而,使用模式表明,大学讲师在教学中使用人工智能的可能性更大,其次是小学和高中教师,中学教师使用最少。多层神经网络分析确定实践知识是影响教学中人工智能使用的最关键因素(重要权重为0.450),其次是信念和态度(0.298)、相关性(0.148)和理论知识(0.104)。这些结果凸显了实践知识对于促进人工智能融入教育实践的重要性,强调了对教师培训和专业发展计划的重大影响。