Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/181098
Title: Transforming assessment with LLM and generative AI: impacts and challenges
Authors: Hao, Jiangang
Keywords: Computer and Information Science
Issue Date: 2024
Source: Hao, J. (2024). Transforming assessment with LLM and generative AI: impacts and challenges. AI for Education Singapore 2024. Nanyang Technological University.
Conference: AI for Education Singapore 2024
Abstract: The remarkable strides in artificial intelligence (AI), exemplified by ChatGPT, have impacted our ways of doing many things. Given its central role in accrediting knowledge and skills, assessment positions itself at the forefront of these impacts. Applying cutting-edge large language models (LLMs) and generative AI to assessment holds great promise in boosting efficiency, mitigating bias, and facilitating customized evaluations. Conversely, these innovations raise significant concerns regarding validity, reliability, transparency, fairness, equity, and test security, necessitating careful thinking when applying them in assessments. In this talk, I will discuss the impacts and implications of LLMs and generative AI on critical dimensions of assessment with example use cases and highlight the challenges that call for a community effort to address.
URI: https://hdl.handle.net/10356/181098
URL: https://www.ntu.edu.sg/mae/ai-education-singapore-2024/activities/keynote-invited-talk#Content_C021_Col00
Schools: School of Mechanical and Aerospace Engineering 
Organisations: NVIDIA 
Rights: © 2024 The Author. Published by Nanyang Technological University. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Conference Papers

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