{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/127391"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/127391","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"CodeLens: A generative ai framework for dynamic feedback on SQL semantic errors","abstract":"This Thesis was approved for publication on 2024-12-06 at 16:45.","abstract_html":"This Thesis was approved for publication on 2024-12-06 at 16:45.","abstract_has_math":false,"creators":["Alrabah, Abdulrahman"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Alawini, Abdussalam"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-12-06","date_published":"2024-12-06","updated_at":"2026-07-22T22:25:04Z","subjects":["Generative Ai","Ai","Machine Learning","Sql","Semantic Error","Feedback System"],"languages":["en","eng"],"rights":["Copyright 2024 Abdulrahman AlRabah"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/127391","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Alawini, Abdussalam"]},{"key":"dc:creator","label":"Author","values":["Alrabah, Abdulrahman"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-12-06","2024-12"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Generative Ai","Ai","Machine Learning","Sql","Semantic Error","Feedback System"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Abdulrahman AlRabah"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/127391"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This Thesis was approved for publication on 2024-12-06 at 16:45.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21480 on 2025-03-28 at 14:44:35","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-12-01","The student, Abdulrahman Alrabah, accepted the attached license on 2024-12-03 at 17:20.","The student, Abdulrahman Alrabah, submitted this Thesis for approval on 2024-12-03 at 17:21.","The integration of Generative AI and Machine Learning (ML) technologies in computing education presents a unique opportunity to complement the learning experience for students across different educational levels. This thesis presents an AI-assisted computing framework designed to support engineering students in their learning journey by employing a structured sequence of instructions designed to guide the AI's behavior, fine-tuning techniques and Retrieval Augmented Generation (RAG) models, to deliver helpful feedback tailored to each student’s needs. The framework dynamically adapts to different programming languages by detecting the language used and applying course-specific context through dynamic prompting. Preliminary implementations in courses such as Database Systems have demonstrated the framework’s influence, resulting in a noticeable reduction in SQL problem submissions. This approach acts as an intelligent tutor, providing support to reduce students' frustration, errors, and deepen their understanding of complex engineering problems. The framework’s correctness and effectiveness are evaluated by testing the models on a series of problem sets, with experts assessing and refining the generated responses. Ultimately, this work contributes to the field of Education and Database Systems by showcasing the practical application, adaptability, and effectiveness of AI models in computing education, providing a more supportive learning environment that leads to better outcomes for students tackling computing problems."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["CodeLens: A generative ai framework for dynamic feedback on SQL semantic errors"]}]}],"canonical_facts":{"dc:contributor":["Alawini, Abdussalam"],"dc:creator":["Alrabah, Abdulrahman"],"dc:date":["2024-12-06","2024-12"],"dc:description":["This Thesis was approved for publication on 2024-12-06 at 16:45.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21480 on 2025-03-28 at 14:44:35","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-12-01","The student, Abdulrahman Alrabah, accepted the attached license on 2024-12-03 at 17:20.","The student, Abdulrahman Alrabah, submitted this Thesis for approval on 2024-12-03 at 17:21.","The integration of Generative AI and Machine Learning (ML) technologies in computing education presents a unique opportunity to complement the learning experience for students across different educational levels. 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