pith. sign in

arxiv: 2404.01036 · v1 · pith:TMSECH2Anew · submitted 2024-04-01 · 💻 cs.IR · cs.AI· cs.CV· cs.LG

Higher education assessment practice in the era of generative AI tools

classification 💻 cs.IR cs.AIcs.CVcs.LG
keywords toolsassessmentgenaifindingsdatadisciplineseducationgenerative
0
0 comments X
read the original abstract

The higher education (HE) sector benefits every nation's economy and society at large. However, their contributions are challenged by advanced technologies like generative artificial intelligence (GenAI) tools. In this paper, we provide a comprehensive assessment of GenAI tools towards assessment and pedagogic practice and, subsequently, discuss the potential impacts. This study experimented using three assessment instruments from data science, data analytics, and construction management disciplines. Our findings are two-fold: first, the findings revealed that GenAI tools exhibit subject knowledge, problem-solving, analytical, critical thinking, and presentation skills and thus can limit learning when used unethically. Secondly, the design of the assessment of certain disciplines revealed the limitations of the GenAI tools. Based on our findings, we made recommendations on how AI tools can be utilised for teaching and learning in HE.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

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

  1. LLMography: Transforming Human-AI Conversations into Traceability, Oversight, and Auditability Indicators

    cs.HC 2026-06 unverdicted novelty 4.0

    LLMography is a framework for generating KPI reports from human-AI conversations including prompt quality, human direction, AI dependency, and auditability scores, with a preliminary evaluation on 19 student reports s...