# Higher education assessment practice in the era of generative AI tools

arXiv: https://arxiv.org/abs/2404.01036 · HTML: https://mlsanity.com/paper/2404.01036/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 1 references that are in the map.
- First posted on arXiv 1 Apr 2024.

## Built on by (most cited first, 5 of 5)

- [A Systematic Review of Generative AI for Teaching and Learning Practice](https://mlsanity.com/paper/2406.09520/) (2024)
- [Comprehensive AI Assessment Framework: Enhancing Educational Evaluation with Ethical AI Integration](https://mlsanity.com/paper/2407.16887/) (2024)
- [Generative AI in Education: Student Skills and Lecturer Roles](https://mlsanity.com/paper/2504.19673/) (2025)
- [Exploring Student Perception on Gen AI Adoption in Higher Education: A Descriptive Study](https://mlsanity.com/paper/2603.27777/) (2026)
- [LLMography: Transforming Human-AI Conversations into Traceability, Oversight, and Auditability Indicators](https://mlsanity.com/paper/2606.29437/) (2026)

## Abstract (arXiv)

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.