Early Adoption of Generative Artificial Intelligence in Computing Education: Emergent Student Use Cases and Perspectives in 2023

• URL: http://arxiv.org/abs/2411.11166v1
• Date: Sun, 17 Nov 2024 20:17:47 GMT
• Title: Early Adoption of Generative Artificial Intelligence in Computing Education: Emergent Student Use Cases and Perspectives in 2023
• Authors: C. Estelle Smith, Kylee Shiekh, Hayden Cooreman, Sharfi Rahman, Yifei Zhu, Md Kamrul Siam, Michael Ivanitskiy, Ahmed M. Ahmed, Michael Hallinan, Alexander Grisak, Gabe Fierro,
• Abstract summary: There is limited prior research on computing students' use and perceptions of GenAI. We surveyed all computer science majors in a small engineering-focused R1 university. We discuss the impact of our findings on the emerging conversation around GenAI and education.
• Score: 38.83649319653387
• License:
• Abstract: Because of the rapid development and increasing public availability of Generative Artificial Intelligence (GenAI) models and tools, educational institutions and educators must immediately reckon with the impact of students using GenAI. There is limited prior research on computing students' use and perceptions of GenAI. In anticipation of future advances and evolutions of GenAI, we capture a snapshot of student attitudes towards and uses of yet emerging GenAI, in a period of time before university policies had reacted to these technologies. We surveyed all computer science majors in a small engineering-focused R1 university in order to: (1) capture a baseline assessment of how GenAI has been immediately adopted by aspiring computer scientists; (2) describe computing students' GenAI-related needs and concerns for their education and careers; and (3) discuss GenAI influences on CS pedagogy, curriculum, culture, and policy. We present an exploratory qualitative analysis of this data and discuss the impact of our findings on the emerging conversation around GenAI and education.

Related papers

• Engineering Educators' Perspectives on the Impact of Generative AI in Higher Education [4.06279597585806]
  This study reports findings from a survey of engineering educators on their use of and perspectives toward generative AI. We asked them about their use of and comfort with GenAI, their overall perspectives on GenAI, the challenges and potential harms of using it for teaching, learning, and research, and examined whether their approach to using and integrating GenAI in their classroom influenced their experiences with GenAI and perceptions of it.
  arXiv  Detail & Related papers  (2025-02-01T21:29:53Z)
• From Automation to Cognition: Redefining the Roles of Educators and Generative AI in Computing Education [2.0628700367476203]
  Generative Artificial Intelligence (GenAI) offers opportunities to revolutionise teaching and learning in Computing Education (CE) However, educators have expressed concerns that students may over-rely on GenAI and use these tools to generate solutions without engaging in the learning process. This paper describes our experiences with using GenAI in CS-focused educational settings and the changes we have implemented accordingly in our teaching.
  arXiv  Detail & Related papers  (2024-12-16T03:36:25Z)
• Dimensions of Generative AI Evaluation Design [51.541816010127256]
  We propose a set of general dimensions that capture critical choices involved in GenAI evaluation design. These dimensions include the evaluation setting, the task type, the input source, the interaction style, the duration, the metric type, and the scoring method.
  arXiv  Detail & Related papers  (2024-11-19T18:25:30Z)
• Generative Artificial Intelligence Meets Synthetic Aperture Radar: A Survey [49.29751866761522]
  This paper aims to investigate the intersection of GenAI and SAR. First, we illustrate the common data generation-based applications in SAR field. Then, an overview of the latest GenAI models is systematically reviewed. Finally, the corresponding applications in SAR domain are also included.
  arXiv  Detail & Related papers  (2024-11-05T03:06:00Z)
• "I Am the One and Only, Your Cyber BFF": Understanding the Impact of GenAI Requires Understanding the Impact of Anthropomorphic AI [55.99010491370177]
  We argue that we cannot thoroughly map the social impacts of generative AI without mapping the social impacts of anthropomorphic AI. anthropomorphic AI systems are increasingly prone to generating outputs that are perceived to be human-like.
  arXiv  Detail & Related papers  (2024-10-11T04:57:41Z)
• Model-based Maintenance and Evolution with GenAI: A Look into the Future [47.93555901495955]
  We argue that Generative Artificial Intelligence (GenAI) can be used as a means to address the limitations of Model-Based Engineering (MBM&E) We propose that GenAI can be used in MBM&E for: reducing engineers' learning curve, maximizing efficiency with recommendations, or serving as a reasoning tool to understand domain problems.
  arXiv  Detail & Related papers  (2024-07-09T23:13:26Z)
• Understanding Student and Academic Staff Perceptions of AI Use in Assessment and Feedback [0.0]
  The rise of Artificial Intelligence (AI) and Generative Artificial Intelligence (GenAI) in higher education necessitates assessment reform. This study addresses a critical gap by exploring student and academic staff experiences with AI and GenAI tools. An online survey collected data from 35 academic staff and 282 students across two universities in Vietnam and one in Singapore.
  arXiv  Detail & Related papers  (2024-06-22T10:25:01Z)
• Computing in the Life Sciences: From Early Algorithms to Modern AI [45.74830585715129]
  This paper highlights key milestones and technological advancements through the historical development of computing in the life sciences. The discussion includes the inception of computational models for biological processes, the advent of bioinformatics tools, and the integration of AI/ML in modern life sciences research. Attention is given to AI-enabled tools used in the life sciences, such as scientific large language models and bio-AI tools, examining their capabilities, limitations, and impact to biological risk.
  arXiv  Detail & Related papers  (2024-06-17T21:36:52Z)
• Innovating Computer Programming Pedagogy: The AI-Lab Framework for Generative AI Adoption [0.0]
  We introduce "AI-Lab," a framework for guiding students in effectively leveraging GenAI within core programming courses. By identifying and rectifying GenAI's errors, students enrich their learning process. For educators, AI-Lab provides mechanisms to explore students' perceptions of GenAI's role in their learning experience.
  arXiv  Detail & Related papers  (2023-08-23T17:20:37Z)
• The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and Millennial Generation teachers? [0.0]
  Gen Z students were generally optimistic about the potential benefits of generative AI (GenAI) Gen X and Gen Y teachers expressed heightened concerns about overreliance, ethical and pedagogical implications.
  arXiv  Detail & Related papers  (2023-05-04T14:42:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.