Artificial intelligence vs traditional teaching methods on student performance: Effectiveness and challenges
Abstract
The increasing need to achieve better results in students, individualized learning, and scalable educational services has made the argument of artificial intelligence in education versus conventional teaching techniques more intense. Even though traditional teacher-centered training is necessary to ensure social interaction, emotional support, and contextual understanding, the recent developments in AI-based learning, generative AI, intelligent tutoring systems, and adaptive learning platforms have reshaped the educational environment. The proposed PRISMA-based literature review research problem is to examine the comparative efficacy and limitations of artificial intelligence versus conventional teaching procedures on student performance in higher education, K-12 education, and STEM education settings. The systematic review was based on such themes as academic achievement, student engagement, learning analytics, automated assessment, educational chatbots, personalized feedback, and technology-enhanced learning. Recent trends related to ChatGPT, multimodal learning, predictive analytics, AI-based assessment, and human-centered AI were used as the focus of the selection process. The results show that AI-based learning systems tend to be more effective in enhancing academic motivation, self-regulated learning, adaptive learning, and personalized learning results than conventional teaching strategies. Virtual learning environments, intelligent tutoring systems and learner analytics proved especially useful in helping to provide individualized instruction and to spot at-risk students. Nevertheless, the conventional approaches to teaching are more efficient in promoting collaborative learning, emotional intelligence, critical thinking, and teacher-student relationship. Other significant issues associated with ethical AI, algorithmic bias, data privacy, academic integrity, teacher preparedness, and the digital divide are also noted in the review.
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