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          <dc:identifier>https://hdl.handle.net/2286/R.2.N.200683</dc:identifier>
                  <dc:rights>http://rightsstatements.org/vocab/InC/1.0/</dc:rights>
          <dc:rights>http://creativecommons.org/licenses/by-nc-sa/4.0</dc:rights>
                  <dc:date>2025-05</dc:date>
                  <dc:format>28 pages</dc:format>
                  <dc:contributor>Feng, Gregory</dc:contributor>
          <dc:contributor>Singh, Archit</dc:contributor>
          <dc:contributor>Chen, Yinong</dc:contributor>
          <dc:contributor>De Luca, Gennaro</dc:contributor>
          <dc:contributor>Barrett, The Honors College</dc:contributor>
          <dc:contributor>Computer Science and Engineering Program</dc:contributor>
                  <dc:description>In this project, my partner Archit and I looked to explore the future of no-code/low-code development. Throughout this project, we initially researched low-code/no-code development environments before settling on Microsoft Power Platform and Google Cloud to implement our capstone project, a retrieval augmented generation large language model teaching assistant that uses course-specific content pulled directly from Canvas and fed into the model. At the end of this project, we were able to fully replicate our capstone project&#039;s base frontend and the majority of our project&#039;s backend within two weeks, with a functional chat screen, two-factor authentication using a verification code, and a built-in user statistics report as compared to our capstone project, which took a whole semester to create.</dc:description>
                  <dc:subject>Microsoft Power Platform</dc:subject>
          <dc:subject>Low Code Development</dc:subject>
          <dc:subject>Artificial Intelligence</dc:subject>
          <dc:subject>Application Development</dc:subject>
                  <dc:title>Analysis on the Impact of Modern AI-Assisted Low-Code Software Development Techniques</dc:title></oai_dc:dc></metadata></record></GetRecord></OAI-PMH>
