Innovative GPT-Powered Adaptive Assessment for Mobile Language Learning: A Path to Real-Time Personalization and Enhanced Engagement

Abstract

The fast development of mobile learning has revolutionized the field of language education as we know it today, but conventional formative assessment practices are still rigid and not adaptive to individual learning trajectories. This research put forth a new GPT-based formative assessment model for mobile-based English language studies that is heir to static evaluation paradigms due to incorporating a real-time, intuitive feedback feature. Different from the rigid classes of rule-based and ML approaches that are dependent on static assessment structures, the suggested GPT model adapts dynamically to inputs from students and gives personalized results based on ongoing language growth. With the incorporation of GPT, mobile learning applications can provide instant assessment and situational recommendations thereby increasing learner involvement and the feat of a language. The model attunement was performed on the English Language Learning Dataset from Kaggle and there showed the impressive precision and effectiveness in personalized evaluations compared to such traditional models as rule-based systems and SVMs. This real-time adaptability does not only create more interactive learning scenarios but also timely Addresses challenges, ensures perpetual growth and long-term retention. Comparative assessment from reference models emphasizes on the advantages of the proposed solution over the comparative precision, recall, and F1-score, which creates a new paradigm for mobile-assisted language learning. This research represents a major step forward in the mobile learning applications with its demonstration of the revolutionizing potential of AI-based personalized assessment in language education. Future studies will try to expand this model into different linguistic scenarios, proving its efficiency in unmodified real-life scenarios.