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Pages:
2 pages/≈550 words
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Other
Subject:
IT & Computer Science
Type:
Research Paper
Language:
English (U.S.)
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Date:
Total cost:
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Topic:

AI Generated Websites and Applications

Research Paper Instructions:

Research at least 2 peer-reviewed papers published within the last 5 years with the topic of AI Generated Websites and Applications. This topic would investigate the advantages and disadvantages of using AI to create web applications and mobile applications. It will look into the effect it has on jobs in the field, as well as the ethical questions and issues raised by businesses and employees using AI to help them with their projects and products. Do not keep it very broad, narrow it down to a particular sub-topic or problem that is addressed by recent research papers. You can use Google scholar for search. There is an option on the left side of the page to narrow down your search by selecting the certain time frame. For example, you can choose 2020 to current date. You may also use non-peer-reviewed technical articles found on the web as supplementary material, but you must use a minimum of 2 peer-reviewed papers.
2. In selecting the articles, look for articles which address similar or comparable problems/topics. You will have to summarize the major contributions of the papers and it is usually helpful to include a contrast between the approaches taken in each. You may wish to look at a few different papers, read the abstract, introduction, and conclusion sections. Then you can just skip through the method section to get an overall idea about each paper that you are considering. Then carefully make your selection of those which you wish to focus on. Make sure to select ones which have higher citations. Include the citation index for each paper referenced.
3. To indicate what papers you have looked at, prepare a report document in which you summarize the following:
i. The titles, authors, date, and URLs of the papers you focused on, and for each of the peer-reviewed indicate the forums of publication and their citation numbers (number of times they have cited in other publications).
ii. The problem addressed by the papers
iii. The technical approach taken in each paper (in summary of highlights),
iv. Your assessment of the contributions of each of the papers (preferably in a comparative context). This is a very important part in which you will demonstrate your deeper understanding of the contributions of the papers. You should articulate what is the "new" knowledge or methodology that each paper contributes and offer your (justified) opinion of its significance (how does it advance our knowledge, why is it significant, why is the methodology better, did it offer a well defined solution to a real problem, did it open new avenues to addressing a significant problem, etc.). What do we learn from this research that is useful in the career of a computer scientist?
v. Compare/contrast the contributions of these papers. How do they complement each other or how are the approaches different? Make sure to explain what you have learned that may be useful in your intended career.
Note: Your report should demonstrate that you understand the problem and the contributions of the papers; not that you were merely able to rephrase some excerpts without really understanding how the paper's contributions address a problem or what "new" solutions they offer.

Research Paper Sample Content Preview:


Generative AI for Web Content and Web Design
Student's Name
Institutional Affiliation
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Date
Studies reviewed
CodeBERT: A Pre-Trained Model for Programming and Natural Languages by Feng et al. (2020), was published in 2020 in the proceedings of the 28th International Joint Conference on Artificial Intelligence. It introduces a pre-trained model called CodeBERT that can learn from both natural languages and programming languages. “Neural Sketch Learning for Conditional Program Generation” by Murali et al. (2018) was issued in 2018 in the proceedings of the 6th International Conference on Learning Representations. It proposes a novel technique called neural sketch learning that can generate programs from natural language specifications.
Problems addressed
The papers focus on how to use AI to generate code and user interfaces from natural language specifications, which is a challenging task in generative AI. Generating code and user interfaces requires understanding the semantic meaning of the natural language specifications, mapping them to syntactic and semantic rules of the programming languages, and synthesizing executable and functional code and user interfaces that match the specifications and are visually appealing.
The technical approach taken in each paper

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