[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126228-en":3,"doc-seo-126228-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126228,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",8,"Research & Report","ADVANCING ACADEMIC ADVISING WITH KNOWLEDGE GRAPHS - Integrating Machine Learning and LLMs for Personalized Course Planning","Academic advising is essential for student decision-making, performance improvement, and successful navigation of university life, yet rising enrollment challenges traditional advising by limiting scalability and increasing advisor workload. Course offering design also remains complex and error-prone due to multiple stakeholders. This thesis proposes a data-driven automated system to generate personalized academic plans and reduce student dependency on advisors while providing reliable course-demand estimates. The approach uses knowledge graphs for course and prerequisite relationships in phase one and integrates machine learning with LLMs in phase two to deliver policy-compliant recommendations. Results from a UAEU case study show up to 70% similarity with generic plans and about 80% similarity with actual plans followed by graduated students.","The College of Graduate Studies and the College of Information Technology Cordially Invite  \nYou to a  \nMaster Thesis Defense  \nEntitled  \nADVANCING ACADEMIC ADVISING WITH KNOWLEDGE GRAPHS: INTEGRATING MACHINE LEARNING AND LLMS FOR PERSONALIZED COURSE PLANNING  \nby  \nSara Alshamsi  \nStudent ID: 201801167  \nFaculty Advisor  \nProf. Nazar Zaki  \nDepartment of Computer Science and Software Engineering College of Information Technology  \nDate & Venue  \nDay: Thursday, 12 June 2025  \nTime: 11:00 AM – 1:00 PM  \nRoom: E1-1013  \nAbstract  \nAcademic advising plays a critical role in helping students make informed decisions, improve academic performance, and successfully navigate their university journey. However, with increasing university enrollment, traditional advising methods often struggle to scale, leading to student frustration and overburdened advisors. Additionally, designing course offerings that match student demand is a complex and error-prone process involving multiple stakeholders. To address these challenges, this thesis proposes an automated, data-driven system for generating personalized academic plans for students. The primary aim of this thesis is to develop a system that reduces students' dependency on advisors while simultaneously providing accurate estimates of course demand to assist in academic planning for upcoming semesters. The proposed system operates in two phases. In the first phase, Knowledge Graphs (KGs) are used to model relationships between courses, prerequisites, and student progress. In the second phase, Machine Learning (ML) techniques and Large Language Models (LLMs) are integrated to further personalize course recommendations. The system is designed to ensure logical course progression while adhering to university-specific academic policies, with a case study conducted at the United Arab Emirates University (UAEU) . The generated academic plans demonstrate up to 70\\% similarity when compared to the generic degree plans provided by the university and show an average of 80\\% similarity when compared to actual plans followed by graduated students. This work introduces a hybrid system combining Knowledge Graph modeling with Machine Learning personalization for academic advising, offering a scalable and interpretable solution that aligns course planning with student needs and institutional constraints. This thesis addresses the scarcity of research applying Knowledge Graphs for personalized academic planning in universities, bridging the gap between traditional advising practices and automated, data-driven recommendation systems tailored to individual student backgrounds.  \nKeywords: Automated academic advising, KGs, ML, Personalized academic plans, Course demand prediction, LLMs.  \nتتشرف كلية الدراسات العليا وكلية تقنية المعلومات بدعوتكم لحضور  \nمناقشة رسالة الماجستير  \nالعنوان  \nتعزيز الإرشاد الأكاديمي باستخدام الرسوم البيانية المعرفية: دمج تعلم الآلة والنماذج اللغوية الكبيرة لتخطيط المقررات  \nالدراسية بشكل مخصص  \nللطالبة   \nسارة الشامسي  \nالرقم الجامعي201801167:  \nالمشرف  \nد. نزار زكي  \nكلية تقنية المعلومات  \nقسم علوم الحاسوب وهندسة البرمجيات  \nالمكان والزمان  \nيوم الخميس الموافق 12 يونيو 2025  \nم 01:00 – ص 11:00 الساعة  \nالمبنى E1 – الغرفة 1013  \nالملخص  \nتلعب عملية الإرشاد الأكاديمي دورا حيويا في مساعدة الطلاب على اتخاذ قرارات تشكل مستقبلهم الأكاديمي، وتحسين أدائهمالأكاديمي، والتنقل بنجاح في رحلتهم الجامعية. ومع ذلك، ومع تزايد أعداد الطلاب المسجلين في الجامعات، غالبا ما تعجزالطرق التقليدية في الإرشاد الأكاديمي عن التكيف مع هذا النمو، مما يؤدي إلى إحباط الطلاب وزيادة الضغط على المرشدينالأكاديميين. بالإضافة إلى ذلك، يُعد تصميم خطط طرح المقررات بما يتماشى مع طلب الطلاب عملية معقدة وعرضة للأخطاءالبشرية. تهدف هذه الرسالة إلى تطوير نظام مؤتمت يعتمد على البيانات لتوليد خطط أكاديمية شخصية للطلاب، مما يقلل مناعتمادهم على المرشدين الأكاديميين، ويوفر تقديرات دقيقة للطلب المتوقع على المقررات لدعم التخطيط الأكاديمي للفصولالدراسية القادمة. يعمل النظام المقترح على مرحلتين، حيث يتم في المرحلة الأولى استخدام الرسوم","cbCaivNEmxVdusES","https://ap.wps.com/l/cbCaivNEmxVdusES","pdf",909471,6,1,2,"English","en",105,"# Abstract\n## System goal and challenges\n## Two-phase proposed approach\n## Evaluation and results\n## Research contribution","[{\"question\":\"What problems does the thesis address in academic advising and course planning?\",\"answer\":\"It addresses scalability limits of traditional advising as enrollment grows and the complexity and error-proneness of designing course offerings that match student demand.\"},{\"question\":\"How does the proposed system generate personalized academic plans?\",\"answer\":\"It works in two phases: knowledge graphs model course-prerequisite-student progress relationships, then machine learning combined with large language models produces personalized course recommendations.\"},{\"question\":\"How were the results evaluated in the thesis?\",\"answer\":\"A case study at the United Arab Emirates University compares generated plans to generic degree plans and to actual plans followed by graduated students.\"}]","ADVANCING ACADEMIC ADVISING WITH KNOWLEDGE GRAPHS - Integrating Machine Learning and LLMs for Personalized Course Planning | PDF",1785903938,5,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"advancing-academic-advising-with-knowledge-graphs-integrating-machine-learning-and-llms-for-personalized-course-planning","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":22},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/advancing-academic-advising-with-knowledge-graphs-integrating-machine-learning-and-llms-for-personalized-course-planning/126228/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-25","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problems does the thesis address in academic advising and course planning?","Question",{"text":76,"@type":77},"It addresses scalability limits of traditional advising as enrollment grows and the complexity and error-proneness of designing course offerings that match student demand.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed system generate personalized academic plans?",{"text":81,"@type":77},"It works in two phases: knowledge graphs model course-prerequisite-student progress relationships, then machine learning combined with large language models produces personalized course recommendations.",{"name":83,"@type":74,"acceptedAnswer":84},"How were the results evaluated in the thesis?",{"text":85,"@type":77},"A case study at the United Arab Emirates University compares generated plans to generic degree plans and to actual plans followed by graduated students.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,110,114,119,122,127,130,134],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":30,"doc_module":4,"doc_module_name":47,"category_name":107,"show_sort_weight":108,"slug":109},"Comic",60,"comic",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":47,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":47,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":47,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":47,"category_name":136,"show_sort_weight":30,"slug":137},19,"General","general"]