ChatGPT-Based Learning And Reading Assistant: Initial Report

Abstract

We introduce “C-LARA”, a complete reimplementation of the Learning And Reading Assistant (LARA) which puts ChatGPT-4 in the centre. ChatGPT-4 is used both as a software component, to create and annotate text, and as a software engineer, to implement the platform itself. We de- scribe how ChatGPT-4 can at runtime write and annotate short stories suitable for intermediate level language classes, producing high quality multimedia output which for many languages is usable after only minor editing. We then sketch the development process, where ChatGPT-4, in its software engineer role, has written about 90% of the new platform’s code, working in close collaboration with one of the human authors. We show how the AI is able to discuss the code with both technical and non-technical project members. In conclusion, we briefly discuss the significance of this case study for language technology and software development in general. Appendices give examples of processing flow, samples of annotated text produced by the plat- form, an outline of C-LARA’s software architecture, a full list of platform functionalities, and transcripts of various discussions about C-LARA between ChatGPT-4 and the human collabo- rators.

We introduce “C-LARA”, a complete reimplementation of the Learning And Reading Assistant (LARA) which puts ChatGPT-4 in the centre. ChatGPT-4 is used both as a software component, to create and annotate text, and as a software engineer, to implement the platform itself. We de- scribe how ChatGPT-4 can at runtime write and annotate short stories suitable for intermediate level language classes, producing high quality multimedia output which for many languages is usable after only minor editing. We then sketch the development process, where ChatGPT-4, in its software engineer role, has written about 90% of the new platform’s code, working in close collaboration with one of the human authors. We show how the AI is able to discuss the code with both technical and non-technical project members. In conclusion, we briefly discuss the significance of this case study for language technology and software development in general. Appendices give examples of processing flow, samples of annotated text produced by the plat- form, an outline of C-LARA’s software architecture, a full list of platform functionalities, and transcripts of various discussions about C-LARA between ChatGPT-4 and the human collabo- rators.