GitHub Copilot AI pair programmer: Asset or Liability?

Dakhel, Arghavan Moradi; Majdinasab, Vahid; Nikanjam, Amin; Khomh, Foutse; Desmarais, Michel C.; Jiang, Zhen Ming

doi:10.1016/j.jss.2023.111734

Cited by 86 publications

(7 citation statements)

References 25 publications

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“…Indeed, given how computationally and data-intensive cryoEM and cryoET analyses are, there is a great need for efficient, cross-platform, easy-to-install, well-maintained, well-documented, and user-friendly software for data processing and analysis. Moreover, the availability of AI-based tools for coding and software development (Moradi Dakhel et al, 2023;Wu et al, 2023b) could help in the attainment of such a goal. Moving into the future, increased file format standardization, improved software documentation and maintenance, and adoption of best practices guided by extensive benchmarking (Turoňová et al, 2020) will contribute to tackling post-data collection bottlenecks.…”

Section: Discussion: Opportunities To Further Potentiate Large-scale ...mentioning

confidence: 99%

The advent of preventive high-resolution structural histopathology by artificial-intelligence-powered cryogenic electron tomography

Galaz-Montoya

2024

Preprint

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Advances in cryogenic electron microscopy (cryoEM) single particle analysis have revolutionized structural biology by facilitating the in vitro determination of atomic- and near atomic-resolution structures for fully hydrated macromolecular complexes exhibiting compositional and conformational heterogeneity across a wide range of sizes. Cryogenic electron tomography (cryoET) and subtomogram averaging are rapidly progressing towards delivering similar insights for macromolecular complexes in situ, without requiring tags or harsh biochemical purification. Furthermore, cryoET allows visualizing cell and tissue phenotypes directly at molecular, nanometric resolution without chemical fixation or staining artifacts. This forward-looking review covers recent developments in cryoEM/ET and related technologies such as cryogenic focused ion beam milling scanning electron microscopy and correlative light microscopy, increasingly driven and boosted by artificial intelligence algorithms. Their potential application to emerging concepts is discussed, primarily the prospect of complementing medical histopathology analysis. Machine learning solutions are poised to address current challenges posed by 'big data' in cryoET of tissues, cells, and macromolecules, offering the promise of novel insights into disease processes translatable to the clinic in the near future in the form of improved diagnostics and targeted therapeutics.

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Section: Discussion: Opportunities To Further Potentiate Large-scale ...mentioning

confidence: 99%

The advent of preventive high-resolution structural histopathology by artificial-intelligence-powered cryogenic electron tomography

Galaz-Montoya

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Indeed, given how computationally and data-intensive cryoEM/ET analyses are, there is a great need for efficient, cross-platform, easy-to-install, well-maintained, well-documented, and user-friendly software for data processing and analysis. Moreover, the availability of AI-based tools for coding and software development ( Wu T. et al, 2023 ; Moradi Dakhel et al, 2023 ) could help in the attainment of such a goal. Moving into the future, increased file format standardization, improved software documentation and maintenance, and adoption of best practices guided by extensive benchmarking ( Turoňová et al, 2020 ) will contribute to reducing post-data collection bottlenecks.…”

Section: Discussion: Opportunities To Further Potentiate Large-scale ...mentioning

confidence: 99%

The advent of preventive high-resolution structural histopathology by artificial-intelligence-powered cryogenic electron tomography

Galaz-Montoya

2024

Front. Mol. Biosci.

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Advances in cryogenic electron microscopy (cryoEM) single particle analysis have revolutionized structural biology by facilitating the in vitro determination of atomic- and near-atomic-resolution structures for fully hydrated macromolecular complexes exhibiting compositional and conformational heterogeneity across a wide range of sizes. Cryogenic electron tomography (cryoET) and subtomogram averaging are rapidly progressing toward delivering similar insights for macromolecular complexes in situ, without requiring tags or harsh biochemical purification. Furthermore, cryoET enables the visualization of cellular and tissue phenotypes directly at molecular, nanometric resolution without chemical fixation or staining artifacts. This forward-looking review covers recent developments in cryoEM/ET and related technologies such as cryogenic focused ion beam milling scanning electron microscopy and correlative light microscopy, increasingly enhanced and supported by artificial intelligence algorithms. Their potential application to emerging concepts is discussed, primarily the prospect of complementing medical histopathology analysis. Machine learning solutions are poised to address current challenges posed by “big data” in cryoET of tissues, cells, and macromolecules, offering the promise of enabling novel, quantitative insights into disease processes, which may translate into the clinic and lead to improved diagnostics and targeted therapeutics.

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“…Beyond ChatGPT, there are other options for LLMs, such as CoPilot [41][42][43]. To evaluate whether students have obtained the knowledge and skills related to Programming II fairly, we decided to use paper-based midterm exams for all participants.…”

Section: Paper-based Midtermsmentioning

confidence: 99%

Computer Science Education in ChatGPT Era: Experiences from an Experiment in a Programming Course for Novice Programmers

Kosar,

Ostojić,

Liu

et al. 2024

Mathematics

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The use of large language models with chatbots like ChatGPT has become increasingly popular among students, especially in Computer Science education. However, significant debates exist in the education community on the role of ChatGPT in learning. Therefore, it is critical to understand the potential impact of ChatGPT on the learning, engagement, and overall success of students in classrooms. In this empirical study, we report on a controlled experiment with 182 participants in a first-year undergraduate course on object-oriented programming. Our differential study divided students into two groups, one using ChatGPT and the other not using it for practical programming assignments. The study results showed that the students’ performance is not influenced by ChatGPT usage (no statistical significance between groups with a p-value of 0.730), nor are the grading results of practical assignments (p-value 0.760) and midterm exams (p-value 0.856). Our findings from the controlled experiment suggest that it is safe for novice programmers to use ChatGPT if specific measures and adjustments are adopted in the education process.

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GitHub Copilot AI pair programmer: Asset or Liability?

Cited by 86 publications

References 25 publications

The advent of preventive high-resolution structural histopathology by artificial-intelligence-powered cryogenic electron tomography

The advent of preventive high-resolution structural histopathology by artificial-intelligence-powered cryogenic electron tomography

The advent of preventive high-resolution structural histopathology by artificial-intelligence-powered cryogenic electron tomography

Computer Science Education in ChatGPT Era: Experiences from an Experiment in a Programming Course for Novice Programmers

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