Personalised Feedback Framework for Online Education Programmes Using Generative AI

• URL: http://arxiv.org/abs/2410.11904v1
• Date: Mon, 14 Oct 2024 22:35:40 GMT
• Title: Personalised Feedback Framework for Online Education Programmes Using Generative AI
• Authors: Ievgeniia Kuzminykh, Tareita Nawaz, Shihao Shenzhang, Bogdan Ghita, Jeffery Raphael, Hannan Xiao,
• Abstract summary: This paper presents an alternative feedback framework which extends the capabilities of ChatGPT by integrating embeddings. As part of the study, we proposed and developed a proof of concept solution, achieving an efficacy rate of 90% and 100% for open-ended and multiple-choice questions.
• Score: 0.0
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• Abstract: AI tools, particularly large language modules, have recently proven their effectiveness within learning management systems and online education programmes. As feedback continues to play a crucial role in learning and assessment in schools, educators must carefully customise the use of AI tools in order to optimally support students in their learning journey. Efforts to improve educational feedback systems have seen numerous attempts reflected in the research studies but mostly have been focusing on qualitatively benchmarking AI feedback against human-generated feedback. This paper presents an exploration of an alternative feedback framework which extends the capabilities of ChatGPT by integrating embeddings, enabling a more nuanced understanding of educational materials and facilitating topic-targeted feedback for quiz-based assessments. As part of the study, we proposed and developed a proof of concept solution, achieving an efficacy rate of 90% and 100% for open-ended and multiple-choice questions, respectively. The results showed that our framework not only surpasses expectations but also rivals human narratives, highlighting the potential of AI in revolutionising educational feedback mechanisms.

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