Exploring Student Perception on Gen AI Adoption in Higher Education: A Descriptive Study
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| Format: | Preprint |
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2026
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| author | Singh, Harpreet Singh, Jaspreet Singh, Satwant Singh, Rupinder Shahid, Shamim Ibne Hassan, Mohammad Najaran, Tayarani |
| author_facet | Singh, Harpreet Singh, Jaspreet Singh, Satwant Singh, Rupinder Shahid, Shamim Ibne Hassan, Mohammad Najaran, Tayarani |
| contents | The rapid proliferation of Generative Artificial Intelligence (GenAI) is reshaping pedagogical practices and assessment models in higher education. While institutional and educator perspectives on GenAI integration are increasingly documented, the student perspective remains comparatively underexplored. This study examines how students perceive, use, and evaluate GenAI within their academic practices, focusing on usage patterns, perceived benefits, and expectations for institutional support. Data were collected through a questionnaire administered to 436 postgraduate Computer Science students at the University of Hertfordshire and analysed using descriptive methods. The findings reveal a Confidence-Competence Paradox: although more than 60% of students report high familiarity with tools such as ChatGPT, daily academic use remains limited and confidence in effective application is only moderate. Students primarily employ GenAI for cognitive scaffolding tasks, including concept clarification and brainstorming, rather than fully automated content generation. At the same time, respondents express concerns regarding data privacy, reliability of AI-generated information, and the potential erosion of critical thinking skills. The results also indicate strong student support for integrating AI literacy into curricula and programme Knowledge, Skills, and Behaviours (KSBs). Overall, the study suggests that universities should move beyond a policing approach to GenAI and adopt a pedagogical framework that emphasises AI literacy, ethical guidance, and equitable access to AI tools. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_27777 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Exploring Student Perception on Gen AI Adoption in Higher Education: A Descriptive Study Singh, Harpreet Singh, Jaspreet Singh, Satwant Singh, Rupinder Shahid, Shamim Ibne Hassan, Mohammad Najaran, Tayarani Computers and Society Computer Vision and Pattern Recognition The rapid proliferation of Generative Artificial Intelligence (GenAI) is reshaping pedagogical practices and assessment models in higher education. While institutional and educator perspectives on GenAI integration are increasingly documented, the student perspective remains comparatively underexplored. This study examines how students perceive, use, and evaluate GenAI within their academic practices, focusing on usage patterns, perceived benefits, and expectations for institutional support. Data were collected through a questionnaire administered to 436 postgraduate Computer Science students at the University of Hertfordshire and analysed using descriptive methods. The findings reveal a Confidence-Competence Paradox: although more than 60% of students report high familiarity with tools such as ChatGPT, daily academic use remains limited and confidence in effective application is only moderate. Students primarily employ GenAI for cognitive scaffolding tasks, including concept clarification and brainstorming, rather than fully automated content generation. At the same time, respondents express concerns regarding data privacy, reliability of AI-generated information, and the potential erosion of critical thinking skills. The results also indicate strong student support for integrating AI literacy into curricula and programme Knowledge, Skills, and Behaviours (KSBs). Overall, the study suggests that universities should move beyond a policing approach to GenAI and adopt a pedagogical framework that emphasises AI literacy, ethical guidance, and equitable access to AI tools. |
| title | Exploring Student Perception on Gen AI Adoption in Higher Education: A Descriptive Study |
| topic | Computers and Society Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.27777 |