Insights from the Frontline: GenAI Utilization Among Software Engineering Students
- URL: http://arxiv.org/abs/2412.15624v1
- Date: Fri, 20 Dec 2024 07:30:51 GMT
- Title: Insights from the Frontline: GenAI Utilization Among Software Engineering Students
- Authors: Rudrajit Choudhuri, Ambareesh Ramakrishnan, Amreeta Chatterjee, Bianca Trinkenreich, Igor Steinmacher, Marco Gerosa, Anita Sarma,
- Abstract summary: Generative AI (genAI) tools have become ubiquitous in software engineering (SE)
We explore the academic experiences of using genAI tools to complement SE learning and implementations.
We uncover the contexts where these tools are helpful and where they pose challenges, along with examining why these challenges arise and how they impact students.
- Score: 19.31786879151898
- License:
- Abstract: Generative AI (genAI) tools (e.g., ChatGPT, Copilot) have become ubiquitous in software engineering (SE). As SE educators, it behooves us to understand the consequences of genAI usage among SE students and to create a holistic view of where these tools can be successfully used. Through 16 reflective interviews with SE students, we explored their academic experiences of using genAI tools to complement SE learning and implementations. We uncover the contexts where these tools are helpful and where they pose challenges, along with examining why these challenges arise and how they impact students. We validated our findings through member checking and triangulation with instructors. Our findings provide practical considerations of where and why genAI should (not) be used in the context of supporting SE students.
Related papers
- LLMs Integration in Software Engineering Team Projects: Roles, Impact, and a Pedagogical Design Space for AI Tools in Computing Education [7.058964784190549]
This work takes a pedagogical lens to explore the implications of generative AI (GenAI) models and tools, such as ChatGPT and GitHub Copilot.
Our results address a particular gap in understanding the role and implications of GenAI on teamwork, team-efficacy, and team dynamics.
arXiv Detail & Related papers (2024-10-30T14:43:33Z) - Misconceptions, Pragmatism, and Value Tensions: Evaluating Students' Understanding and Perception of Generative AI for Education [0.11704154007740832]
Students are early adopters of the technology, utilizing it in atypical ways.
Students were asked to describe 1) their understanding of GenAI; 2) their use of GenAI; 3) their opinions on the benefits, downsides, and ethical issues pertaining to its use in education.
arXiv Detail & Related papers (2024-10-29T17:41:06Z) - How Novice Programmers Use and Experience ChatGPT when Solving Programming Exercises in an Introductory Course [0.0]
This research paper contributes to the computing education research community's understanding of Generative AI (GenAI) in the context of introductory programming.
This study is guided by the following research questions:.
What do students report on their use pattern of ChatGPT in the context of introductory programming exercises?
How do students perceive ChatGPT in the context of introductory programming exercises?
arXiv Detail & Related papers (2024-07-30T12:55:42Z) - 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) - Large Language Models Meet User Interfaces: The Case of Provisioning Feedback [6.626949691937476]
We present a framework for incorporating GenAI into educational tools and demonstrate its application in our tool, Feedback Copilot.
This work charts a course for the future of GenAI in education.
arXiv Detail & Related papers (2024-04-17T05:05:05Z) - Identifying and Mitigating the Security Risks of Generative AI [179.2384121957896]
This paper reports the findings of a workshop held at Google on the dual-use dilemma posed by GenAI.
GenAI can be used just as well by attackers to generate new attacks and increase the velocity and efficacy of existing attacks.
We discuss short-term and long-term goals for the community on this topic.
arXiv Detail & Related papers (2023-08-28T18:51:09Z) - 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) - LLM-based Interaction for Content Generation: A Case Study on the
Perception of Employees in an IT department [85.1523466539595]
This paper presents a questionnaire survey to identify the intention to use generative tools by employees of an IT company.
Our results indicate a rather average acceptability of generative tools, although the more useful the tool is perceived to be, the higher the intention seems to be.
Our analyses suggest that the frequency of use of generative tools is likely to be a key factor in understanding how employees perceive these tools in the context of their work.
arXiv Detail & Related papers (2023-04-18T15:35:43Z) - Seamful XAI: Operationalizing Seamful Design in Explainable AI [59.89011292395202]
Mistakes in AI systems are inevitable, arising from both technical limitations and sociotechnical gaps.
We propose that seamful design can foster AI explainability by revealing sociotechnical and infrastructural mismatches.
We explore this process with 43 AI practitioners and real end-users.
arXiv Detail & Related papers (2022-11-12T21:54:05Z) - Investigating Explainability of Generative AI for Code through
Scenario-based Design [44.44517254181818]
generative AI (GenAI) technologies are maturing and being applied to application domains such as software engineering.
We conduct 9 workshops with 43 software engineers in which real examples from state-of-the-art generative AI models were used to elicit users' explainability needs.
Our work explores explainability needs for GenAI for code and demonstrates how human-centered approaches can drive the technical development of XAI in novel domains.
arXiv Detail & Related papers (2022-02-10T08:52:39Z) - AI Explainability 360: Impact and Design [120.95633114160688]
In 2019, we created AI Explainability 360 (Arya et al. 2020), an open source software toolkit featuring ten diverse and state-of-the-art explainability methods.
This paper examines the impact of the toolkit with several case studies, statistics, and community feedback.
The paper also describes the flexible design of the toolkit, examples of its use, and the significant educational material and documentation available to its users.
arXiv Detail & Related papers (2021-09-24T19:17:09Z)
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.