Higher education assessment practice in the era of generative AI tools
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arXiv
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| Format: | Preprint |
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2024
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| author | Ogunleye, Bayode Zakariyyah, Kudirat Ibilola Ajao, Oluwaseun Olayinka, Olakunle Sharma, Hemlata |
| author_facet | Ogunleye, Bayode Zakariyyah, Kudirat Ibilola Ajao, Oluwaseun Olayinka, Olakunle Sharma, Hemlata |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_01036 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Higher education assessment practice in the era of generative AI tools Ogunleye, Bayode Zakariyyah, Kudirat Ibilola Ajao, Oluwaseun Olayinka, Olakunle Sharma, Hemlata Information Retrieval Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning I.2.7; I.2.10; H.3.3 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. |
| title | Higher education assessment practice in the era of generative AI tools |
| topic | Information Retrieval Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning I.2.7; I.2.10; H.3.3 |
| url | https://arxiv.org/abs/2404.01036 |