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Author: Gokcearslan, SahinHas files: false

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    Citation - WoS: 17
    Citation - Scopus: 19
    Emotion Regulation, E-Learning Readiness, Technology Usage Status, In-Class Smartphone Cyberloafing, and Smartphone Addiction in the Time of Covid-19 Pandemic
    (Wiley, 2023) Gokcearslan, Sahin; Durak, Hatice Yildiz; Esiyok, Elif; Yildiz Durak, Hatice
    BackgroundThe COVID-19 pandemic has spread quickly, e-learning became compulsory and disseminated throughout the world. During the pandemic, smartphones are frequently used to access e-learning content, but connecting to technological tools increased the risk of cyberloafing during e-courses. Currently, there are a limited number of studies on how e-learning will evolve under compulsory conditions. ObjectivesThis study aimed to investigate the relationship between emotion regulation, e-learning readiness, technology usage status (TUS), in-class smartphone cyberloafing, and smartphone addiction (SA) of the students during the pandemic. MethodsIn total 1294 students participated in this study. A research model was tested by structural equation modelling. Results and ConclusionThe findings of this study indicated that there is a relationship between TUS and SA. Emotion regulation was related to SA. E-learning readiness levels can help to explain cyberloafing. This study presents a conceptual model of the variables that affect cyberloafing in the context of the e-learning environment.
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    Citation - WoS: 3
    Citation - Scopus: 3
    Understanding the Intention To Use Artificial Intelligence Chatbots in Education: the Role of Individual Innovativeness and AI Trust Among University Students
    (Springernature, 2025) Gokcearslan, Sahin; Esiyok, Elif; Kucukergin, Kemal Gurkan
    AI chatbots, which use artificial intelligence and are growing in popularity offer interactive learning environments. In this current study, we used the Technology Acceptance Model (TAM) for the acceptance of AI chatbots in the educational environment. The expanded model included the variables of AI chatbot trust and individual innovativeness. A total of 306 university students participated in the research. According to the Partial Least Squares Structural Equation Modeling (PLS-SEM) results, the model explained 61% of the variance in intention to use AI chatbots for educational purposes. This study shows that AI trust and individual innovativeness offer deeper insights into the research model. Based on these findings, practical recommendations include providing supportive activities to improve ease of use and usefulness, encouraging innovation among less innovative students, and enhancing chatbot design with more humanistic and pedagogical features to build trust and engagement.