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Crafting Tomorrow's Evaluations: Assessment Design Strategies in the Era of Generative AI

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arxiv 2405.01805 v1 pith:ERSCA42B submitted 2024-05-03 cs.CY

classification cs.CY
keywords assessmentgenaidesignacademicarticleevaluationintegritychallenges
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GenAI has gained the attention of a myriad of users in almost every profession. Its advancement has had an intense impact on education, significantly disrupting the assessment design and evaluation methodologies. Despite the potential benefits and possibilities of GenAI in the education sector, there are several concerns primarily centred around academic integrity, authenticity, equity of access, assessment evaluation methodology, and feedback. Consequently, academia is encountering challenges in assessment design that are essential to retaining academic integrity in the age of GenAI. In this article, we discuss the challenges, and opportunities that need to be addressed for the assessment design and evaluation. The article also highlights the importance of clear policy about the usage of GenAI in completing assessment tasks, and also in design approaches to ensure academic integrity and subject learning. Additionally, this article also provides assessment categorisation based on the use of GenAI to cultivate knowledge among students and academic professionals. It also provides information on the skills necessary to formulate and articulate problems and evaluate the task, enabling students and academics to effectively utilise GenAI tools.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Improving LLM Safety and Helpfulness using SFT and DPO: A Study on OPT-350M

    cs.CL 2025-09 reject novelty 3.0 of 10

    For OPT-350M on the Anthropic HH-RLHF set, SFT plus DPO gives the highest combined helpfulness/harmlessness score, but not the highest safety score.

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