Evaluating the Effectiveness of Generative AI in Improving Student Engagement and Learning Outcomes in Virtual Classrooms
Keywords:
systematic synthesis, secondary data, virtual classroom, learning outcomes, student engagementAbstract
This paper looks at how well generative artificial intelligence, especially large language models and AI content creators, can improve student participation and learning results in online classrooms. It uses existing data from systematic reviews, meta-analyses, policy reports, and studies published between 2020 and 2025. The study brings together evidence on how much students learn, how they feel about learning (like interest and engagement), and possible problems like depending too much on AI or cheating. The research method involves carefully looking at high-quality reviews and studies, pulling out summaries of how much each study found, and analyzing common ways AI helps learning, such as personalizing content, giving feedback, and supporting students. The results show that using AI in a smart way, with help from teachers, can lead to better learning and more engagement, but using it without thinking can make students less active in thinking and learning. The paper also talks about what schools and teachers should do, along with the study's limits and suggestions for future research.
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