[Paper Review] Generative AI: Implications and Applications for Education

Anastasia Olga, Anastasia Olga Tzirides+11 more|arXiv (Cornell University)|May 12, 2023

Artificial Intelligence in Healthcare and EducationMedicine60 citations

TL;DR

The paper analyzes how a chat-based large language model (C-LLM) can be applied to AI review and assessment of complex student work, and discusses intrinsic limits and future educational applications.

ABSTRACT

The launch of ChatGPT in November 2022 precipitated a panic among some educators while prompting qualified enthusiasm from others. Under the umbrella term Generative AI, ChatGPT is an example of a range of technologies for the delivery of computer-generated text, image, and other digitized media. This paper examines the implications for education of one generative AI technology, chatbots responding from large language models, or C-LLM. It reports on an application of a C-LLM to AI review and assessment of complex student work. In a concluding discussion, the paper explores the intrinsic limits of generative AI, bound as it is to language corpora and their textual representation through binary notation. Within these limits, we suggest the range of emerging and potential applications of Generative AI in education.

Motivation & Objective

• Motivate exploration of Generative AI in education following ChatGPT's emergence in 2022.
• Examine a C-LLM-based application for AI review and assessment of complex student work.
• Discuss intrinsic limits of Generative AI tied to language corpora and binary representation.
• Identify emerging and potential educational applications within the constraints of current technology.

Proposed method

• Review and analysis of a C-LLM-based approach to AI review and assessment of complex student work.
• Critical discussion of the capabilities and limitations of language-based AI in education.
• qualitative exploration of the range of applications and future directions for Generative AI in educational settings.

Experimental results

Research questions

• RQ1 How can a C-LLM be applied to AI review and assessment of complex student work?
• RQ2 What intrinsic limits do Generative AI systems face due to language corpora and binary representation?
• RQ3 What emerging and potential applications of Generative AI in education can be identified within these limits?

Key findings

• Demonstrates an application of a C-LLM to AI review and assessment of complex student work.
• Identifies intrinsic limits of Generative AI rooted in language data and binary notation.
• Outlines a range of emerging and potential educational applications of Generative AI.
• Provides a discussion of the educational implications and cautionary considerations for deployment.