Generative AI in Education: Pedagogical, Theoretical, and Methodological Perspectives
DOI:
https://doi.org/10.46328/ijte.845Keywords:
Artificial Intelligence, AI, ChatGPT, Education, Generative AI, GenAI, LearningAbstract
Recently, ChatGPT, a cutting-edge large language model, has emerged as a powerful Generative Artificial Intelligence (GenAI) tool with the capacity to influence education. ChatGPT provides ample opportunities for learners, researchers, educators, and practitioners to achieve the intended learning outcomes in various disciplines. This special issue examines the diverse applications and implications of GenAI tools including ChatGPT in education, highlighting their potential to enhance teaching and learning across various contexts. Key findings from seventeen studies collected in this special issue demonstrate that GenAI tools can significantly improve educational outcomes by providing personalized feedback, facilitating language learning, and supporting both qualitative and quantitative research methodologies. The findings emphasize GenAI’s capacity to increase learner engagement and motivation, yet also underscore the need for robust ethical guidelines and human oversight due to potential issues with privacy, bias, and accuracy. This special issue also highlights the challenges GenAI faces, such as limitations in contextual understanding and its impact on critical thinking skills. In addition, it provides a foundational framework for exploring effective and responsible GenAI integration, aiming to enrich educational experiences. We conclude that future research should focus on the longitudinal effects of GenAI tools on learning outcomes, developing ethical frameworks for their use, and ensuring their adaptability to diverse learner populations to promote inclusive educational practices.References
Noroozi, O., Soleimani, S., Farrokhnia, M., & Banihashem, S.K. (2024). Generative AI in education: Pedagogical, theoretical, and methodological perspectives. International Journal of Technology in Education (IJTE), 7(3), 373-385. https://doi.org/10.46328/ijte.845
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