The Future Classroom: Integrating AI and Social Media for Adaptive Learning

Abstract

This study investigated the impact of integrating artificial intelligence (AI) and social media into classroom instruction to enhance adaptive learning, engagement, and academic performance. A quasi-experimental design was employed with 120 undergraduate students divided into control and experimental groups. The experimental group received instruction through AI-based adaptive platforms and collaborative social media tools, while the control group experienced conventional teaching methods. Data were collected through pre- and post-tests, engagement surveys, and observational checklists, then analysed using SPSS to compare group performance, engagement trends, and correlations between digital activity and academic outcomes. Results of the analysis revealed that the experimental group showed a significantly higher improvement in post-test scores (p < 0.01), with emotional and cognitive engagement increasing more than behavioural engagement. Qualitative feedback highlighted students' appreciation for real-time AI feedback and peer learning networks, though some reported initial technological challenges. The AI system's adaptive algorithms successfully identified knowledge gaps in 78% of learners, enabling targeted interventions. Interestingly, social media integration reduced classroom anxiety by 32% according to self-reported surveys. Correlation analysis indicated a strong positive relationship (r = 0.82) between engagement levels and academic performance. Weekly academic progression showed a steeper upward trajectory in the experimental group. Social media platforms such as Discord and Microsoft Teams were particularly effective in fostering peer collaboration, with 85% of students reporting improved communication skills. The study concluded that AI and social media, when integrated thoughtfully, could promote personalized, engaging, and collaborative learning environments. However, the findings also underscored the need to address concerns related to data privacy, overreliance on AI, and digital equity, particularly for students from low-income backgrounds who showed 15% less engagement with the technologies. Future research should explore longitudinal impacts and scalability across diverse educational settings, including vocational training and STEM disciplines where AI tutoring systems may yield different outcomes.

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