This research investigates the potential and challenges of using artificial intelligence, specifically the ChatGPT-4 model developed by OpenAI, in grading and providing feedback in an educational setting. By comparing the grading of a human lecturer and ChatGPT-4 in an experiment with 105 students, our study found a strong positive correlation between the scores given by both, despite some mismatches. In addition, we observed that ChatGPT-4's feedback was effectively personalized and understandable for students, contributing to their learning experience. While our findings suggest that AI technologies like ChatGPT-4 can significantly speed up the grading process and enhance feedback provision, the implementation of these systems should be thoughtfully considered. With further research and development, AI can potentially become a valuable tool to support teaching and learning in education. https://saiconference.com/FICC
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ChatGPT’s emergence and subsequent evolution as a generative artificial intelligence tool introduces new ways of assisting students with research design. Fostering research skills with undergraduate students presents opportunities and challenges for faculty to aid with drafting research plans, questions for investigation, and methods for conducting the research. While some educators rightfully voice concerns over the ethical aspects of such a tool, this article will draw on my own experiences using ChatGPT 4.0 as a tool in research project supervision. I demonstrate how to prompt ChatGPT to give useful suggestions that can be used as actionable feedback. I also discuss how to instruct students to include ChatGPT in their research methodology when using the tool to refine research questions.
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Research into automatic text simplification aims to promote access to information for all members of society. To facilitate generalizability, simplification research often abstracts away from specific use cases, and targets a prototypical reader and an underspecified content creator. In this paper, we consider a real-world use case – simplification technology for use in Dutch municipalities – and identify the needs of the content creators and the target audiences in this scenario. The stakeholders envision a system that (a) assists the human writer without taking over the task; (b) provides diverse outputs, tailored for specific target audiences; and (c) explains the suggestions that it outputs. These requirements call for technology that is characterized by modularity, explainability, and variability. We argue that these are important research directions that require further exploration
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