Exploring the Feasibility and Efficacy of ChatGPT3 for Personalized Feedback in Teaching

Abstract

This study explores the feasibility of using AI technology, specifically ChatGPT-3, to provide reliable, meaningful, and personalized feedback. Specifically, the study explores the benefits and limitations of using AI-based feedback in language learning; the pedagogical frameworks that underpin the effective use of AI-based feedback; the reliability of ChatGPT-3’s feedback; and the potential implications of AI integration in language instruction. A review of existing literature identifies key themes and findings related to AI-based teaching practices. The study found that social cognitive theory (SCT) supports the potential use of AI chatbots in the learning process as AI can provide students with instant guidance and support that fosters personalized, independent learning experiences. Similarly, Krashen’s second language acquisition theory (SLA) was found to support the hypothesis that AI use can enhance student learning by creating meaningful interaction in the target language wherein learners engage in genuine communication rather than focusing solely on linguistic form. To determine the reliability of AI-generated feedback, an analysis was performed on student writing. First, two rubrics were created by ChatGPT-3; AI then graded the papers, and the results were compared with human graded results using the same rubrics. The study concludes that e-Learning arning certainly has great potential; besides providing timely, personalized learning support, AI feedback can increase student motivation and foster learning independence. Not surprisingly, though, several caveats exist. It was found that ChatGPT-3 is prone to error and hallucination in providing student feedback, especially when presented with longer texts. To avoid this, rubrics must be carefully constructed, and teacher oversight is still very much required. This study will help educators transition to the new era of AI-assisted e-Learning by helping them make informed decisions about how to provide useful AI feedback that is underpinned by sound pedagogical principles.