{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1990"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1990","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Implicit pen annotation assisted by Large Language Models","abstract":"In our modern society, integrating humans and computer systems has transformed everyday tasks, including reading, annotating, and reviewing documents. Annotating documents is an age-old practice that involves adding markings and notes to engage with the material. Although this process is crucial for understanding texts and facilitating collaborative communication, it has not evolved significantly over the years. Tedious and repetitive workflows in current digital annotation tools suggest missed opportunities for more intelligent, adaptive design. This work introduces AnnotateGPT, a document annotation tool with a digital pen. It leverages a Large Language Model (LLM) (1) to infer the underlying purposes of the user’s annotations and (2) automatically generates annotations with the same purpose throughout the document. AnnotateGPT aims to alleviate the burdens of manual annotation, enabling users to focus on tasks that require critical expertise.","abstract_html":"In our modern society, integrating humans and computer systems has transformed everyday tasks, including reading, annotating, and reviewing documents. Annotating documents is an age-old practice that involves adding markings and notes to engage with the material. Although this process is crucial for understanding texts and facilitating collaborative communication, it has not evolved significantly over the years. Tedious and repetitive workflows in current digital annotation tools suggest missed opportunities for more intelligent, adaptive design. This work introduces AnnotateGPT, a document annotation tool with a digital pen. It leverages a Large Language Model (LLM) (1) to infer the underlying purposes of the user’s annotations and (2) automatically generates annotations with the same purpose throughout the document. AnnotateGPT aims to alleviate the burdens of manual annotation, enabling users to focus on tasks that require critical expertise.","abstract_has_math":false,"creators":["Leung, Benedict"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Science (MSc)","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":["Shimabukuro, Mariana","Collins, Christopher"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-08-01","date_published":"2025-08-01","updated_at":"2026-07-24T05:35:34Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1990","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Shimabukuro, Mariana","Collins, Christopher"]},{"key":"dc:creator","label":"Author","values":["Leung, Benedict"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-09-18T16:48:15Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-09-18T16:48:15Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-08-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MSc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/1990"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In our modern society, integrating humans and computer systems has transformed everyday tasks, including reading, annotating, and reviewing documents. Annotating documents is an age-old practice that involves adding markings and notes to engage with the material. Although this process is crucial for understanding texts and facilitating collaborative communication, it has not evolved significantly over the years. Tedious and repetitive workflows in current digital annotation tools suggest missed opportunities for more intelligent, adaptive design. This work introduces AnnotateGPT, a document annotation tool with a digital pen. It leverages a Large Language Model (LLM) (1) to infer the underlying purposes of the user’s annotations and (2) automatically generates annotations with the same purpose throughout the document. AnnotateGPT aims to alleviate the burdens of manual annotation, enabling users to focus on tasks that require critical expertise."]},{"key":"dc:title","label":"Title","values":["Implicit pen annotation assisted by Large Language Models"]}]}],"canonical_facts":{"dc:contributor.advisor":["Shimabukuro, Mariana","Collins, Christopher"],"dc:creator":["Leung, Benedict"],"dc:date.accessioned":["2025-09-18T16:48:15Z"],"dc:date.available":["2025-09-18T16:48:15Z"],"dc:date.issued":["2025-08-01"],"dc:description.abstract":["In our modern society, integrating humans and computer systems has transformed everyday tasks, including reading, annotating, and reviewing documents. Annotating documents is an age-old practice that involves adding markings and notes to engage with the material. Although this process is crucial for understanding texts and facilitating collaborative communication, it has not evolved significantly over the years. Tedious and repetitive workflows in current digital annotation tools suggest missed opportunities for more intelligent, adaptive design. This work introduces AnnotateGPT, a document annotation tool with a digital pen. It leverages a Large Language Model (LLM) (1) to infer the underlying purposes of the user’s annotations and (2) automatically generates annotations with the same purpose throughout the document. AnnotateGPT aims to alleviate the burdens of manual annotation, enabling users to focus on tasks that require critical expertise."],"dc:identifier.uri":["https://hdl.handle.net/10155/1990"],"dc:language.iso":["en"],"dc:title":["Implicit pen annotation assisted by Large Language Models"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["Master of Science (MSc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:34Z"}