Generative AI Assistants in Software Development Education: A vision for integrating Generative AI into educational practice, not instinctively defending against it
March 24, 2023 Β· Declared Dead Β· π IEEE Software
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Christopher Bull, Ahmed Kharrufa
arXiv ID
2303.13936
Category
cs.SE: Software Engineering
Cross-listed
cs.AI,
cs.HC
Citations
44
Venue
IEEE Software
Last Checked
4 months ago
Abstract
The software development industry is amid another disruptive paradigm change - adopting the use of generative AI (GAI) assistants for programming. Whilst AI is already used in various areas of software engineering, GAI technologies, such as GitHub Copilot and ChatGPT, have ignited peoples' imaginations (and fears). It is unclear how the industry will adapt, but the move to integrate these technologies by large software companies, such as Microsoft (GitHub, Bing) and Google (Bard), is a clear indication of intent and direction. We performed exploratory interviews with industry professionals to understand current practice and challenges, which we incorporate into our vision of a future of software development education and make some pedagogical recommendations.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted