https://ir.lib.ncu.edu.tw/handle/987654321/174 Search the Channel s https://ir.lib.ncu.edu.tw/simple-search https://ir.lib.ncu.edu.tw/handle/987654321/111463 title: 生成式人工智慧輔助合作論證轉化爲英文議論文寫作平台開發與初步評估;Development and Preliminary Evaluation of a Generative AI-Assisted Platform for Transforming Collaborative Argumentation into English Argumentative Writing abstract: 「合作論證學習」是一種以小組互動與論點建構為核心的學習方式，主張學生透過討論、論點交流、證據提出與反駁修正，一步一步共同建構知識與深化理解的過程。這種學習方式有助於提升學生的批判思考能力、問題解決能力與論述表達能力。而其中，英文議論文（argumentative essay）被視為最能體現學生批判思考與論證能力的文類之一。英文議論文不僅要求學生具備基本語言能力，更需要學生能清楚界定立場、組織論點、提出證據支持立場，並回應反方觀點，以展現邏輯推理與批判思維。同時，隨著近年來資訊科技迅速發展，人工智慧已逐步被廣泛應用於各個領域，在教育領域中，以聊天機器人為基礎的學習工具開始被視為能夠協助學生學習、提供即時回饋的學習夥伴。 因此本研究導入人工智慧技術，設計能自動摘要分類小組論點、證據以及引導學生逐步將組內論證成果轉化為完整英語議論文寫作大綱之「AI聊天機器人」，協助學生在寫作前進行論點組織、論證深化與結構化思考，進一步協助學生從合作論證轉向有效的個人英文議論文寫作。 本研究採系統初步評估設計，針對大一學生進行實測。結果顯示，學生對平台之知覺有用性、知覺易用性與使用意願皆持正向看法，並肯定平台在協助英文議論文寫作規劃、論證思考引導與架構釐清方面之成效。研究結果初步顯示，結合 Knowledge Forum 合作論證活動與 AI 寫作引導之平台，具有協助學生整理小組論證內容、形成個人化寫作素材，並支援英文議論文寫作前規劃與架構建構之潛力。;Collaborative Argumentation Learning is a learning approach centered on group interaction and argument construction， in which students collaboratively build knowledge through discussion， evidence sharing， and rebuttal. This approach helps enhance students critical thinking， problem-solving， and argumentative expression abilities. Argumentative essay writing， which emphasizes stance development， evidence support， and logical reasoning， is regarded as an important way to cultivate students critical thinking and argumentation skills. Meanwhile， with the rapid development of artificial intelligence technologies， chatbot-based learning tools have gradually been adopted in education as learning partners that provide immediate feedback and support. Therefore， this study integrates artificial intelligence technology to develop a chatbot that can automatically summarize and categorize group arguments and evidence， while guiding students to transform collaborative discussion outcomes into structured English argumentative essay outlines， thereby supporting students argument organization and writing development. This study adopted a preliminary system evaluation design and conducted a pilot implementation with first-year university students. The results indicated that students held positive perceptions regarding the platform’s perceived usefulness， perceived ease of use， and intention to use. Students also recognized the platform’s effectiveness in supporting argumentative essay planning， guiding argumentative thinking， and organizing writing structures in English argumentative writing. The findings suggest that the platform has the potential to support students in transforming group discussion content into personalized writing materials and in planning the structure of English argumentative essays. <br> Tue, 20 Oct 2026 10:15:03 GMT https://ir.lib.ncu.edu.tw/handle/987654321/111145 title: 博覽群書AI同伴：結合智慧推薦、AI聊書與遊戲化機制;博覽群書AI同伴：結合智慧推薦、AI聊書與遊戲化機制 abstract: 本研究以「明日閱讀習慣暨家校共讀博覽會」認證活動為脈絡，針對學生面對大量書單時不易選書、閱讀內容容易集中於熟悉領域或難度，以及閱讀後整理與回饋不足等問題，設計「書籍地圖」系統。系統整合推薦功能、AI聊書同伴與拼圖任務，分別提供閱讀前選書參考、閱讀中進度與分布視覺化，以及閱讀後反思引導。 本研究採單組前後比較之準實驗設計，並以混合研究法進行分析。研究對象為桃園市某實驗小學參與家校認證活動之33位學生，實驗期程共9週。資料來源包含系統歷程紀錄、閱讀學習動機策略量表前後測與學生半結構式訪談。依資料性質與研究問題不同，本研究分別以推薦配對分析、第一筆與最後一筆聊書比較、完整聊書歷程趨勢分析、上下學期閱讀紀錄比較，以及量表前後測分析，檢視系統導入後學生在選書參考、閱讀反思、閱讀行為與閱讀動機策略上的變化。 研究結果顯示，推薦結果與學生實際閱讀登記具有部分重疊，其中以熱門書籍配對最為明顯，表示推薦功能較適合作為輔助性選書參考。AI聊書同伴透過4F架構，引導學生回顧事實、表達感受、整理發現並連結未來生活；依系統評分結果，學生4F平均分數顯著提升，其中以感受表達與未來連結之提升較為明顯。在拼圖任務機制導入後，本研究觀察到學生有效閱讀登記數量、閱讀廣度與平均閱讀深度皆呈現顯著增加。量表結果顯示，學生自我效能顯著提升，內在價值、自我調節與整體量表未達顯著差異。 整體而言，「書籍地圖」將閱讀認證任務轉化為可視覺化、可追蹤且具回饋性的歷程，並支持學生選書、持續閱讀與閱讀後反思。本研究可作為未來閱讀認證活動推動與AI閱讀輔助系統設計之參考。;This study designed the “Book Map” system for the Tomorrow Reading Habits and Home-School Collaborative Reading Certification Program. The system addresses students’ difficulties in selecting books from large reading lists， their tendency to remain within familiar domains or difficulty levels， and limited post-reading organization and feedback. It integrates recommendation functions， an AI book-talk companion， and puzzle tasks to support book selection， visualize reading progress， and guide post-reading reflection. A single-group pre-post quasi-experimental design with mixed-methods analysis was adopted. Participants were 33 students from an experimental elementary school in Taoyuan City， and the study lasted nine weeks. Data included system logs， pretest and posttest questionnaires， and semi-structured interviews. Analyses included recommendation matching， AI book-talk comparisons and trends， semester-based reading record comparisons， and questionnaire analysis. Results showed that the recommendation functions mainly served as auxiliary book-selection references. Based on system-generated scores， students’ average 4F scores improved significantly， especially in expressing feelings and connecting reading to future life. After puzzle tasks were introduced， valid reading records， reading breadth， and average reading depth increased significantly. Questionnaire results showed significant improvement in self-efficacy， while intrinsic value， self-regulation， and the overall scale showed no significant differences. Overall， the “Book Map” system transformed reading certification tasks into a visualized， trackable， and feedback-oriented process supporting book selection， sustained reading， and post-reading reflection. <br> Tue, 20 Oct 2026 09:00:07 GMT https://ir.lib.ncu.edu.tw/handle/987654321/111141 title: AI 輔助評審面談國小學生閱讀習慣認證之系統設計;Design and Application of an AI-Assisted Pre-Interview Support System for Elementary School Students’ Reading Habit Certification abstract: 本研究以「明日閱讀習慣暨家校閱讀博覽會」之閱讀習慣認證活動為場域，設計AI輔助面談系統，協助國小學生在正式面談前回顧閱讀經驗，並提供評審整理資料及準備問題時所需的參考資訊。學生端以題庫引導對話，再由AI依學生回答進行延伸追問；面談完成後，系統產生摘要與面談追問建議，供評審於正式面談前使用。正式追問與認證判斷仍由評審負責。 研究採系統設計與質性個案分析取向。初始版本實際應用於第一屆活動，共納入9位完成AI面談的學生，其中7位另有正式面談紀錄，並蒐集3位評審的使用回饋。分析資料包括學生AI面談紀錄、AI摘要、面談追問建議、正式面談資料及評審回饋。第一屆活動完成後，再依實際使用與維護過程發現的問題進行系統重構，將原本分開的學生端與評審端功能整合至既有活動平台。 分析結果發現，9位學生皆至少在一項延伸追問中補充前一回答未出現的資訊，但補充內容與程度並不相同。AI摘要能集中整理多輪對話中的閱讀資訊，面談追問建議也可提供部分備題方向；不過，摘要仍可能出現遺漏、過度概括或與原始回答不完全一致的內容，建議問題也可能較為一般。研究結果顯示，AI較適合作為正式面談前的輔助工具，由AI協助提問、整理文字與提供問題方向，評審則依學生閱讀資料及現場回答進行追問與認證判斷。改良版本已完成主要功能重構、資料串接及平台整合，其實際使用情形仍待後續驗證。;This study designed an AI-assisted interview system for an elementary school reading habit certification program. Before the formal interview， the system used a question set and AI-generated follow-up questions to help students recall reading experiences. It then produced summaries and suggested questions for reviewers. Formal questioning and certification decisions remained the reviewers’ responsibility. The study adopted a system design and qualitative case analysis approach. The initial version was used by nine students， seven of whom also had formal interview records. Feedback was collected from three reviewers. The data included AI interview records， summaries， suggested questions， formal interview records， and reviewer feedback. After the first program cycle， the student-side and reviewer-side functions were rebuilt and integrated into the existing platform. All nine students added new information during at least one follow-up exchange， although the content varied. The summaries and suggested questions helped reviewers organize information and prepare questions， but some summaries omitted details or overgeneralized student responses. The findings indicate that AI is more suitable as a pre-interview support tool than as an evaluator. System and data integration have been completed in the revised version， but its practical use still requires further evaluation. <br> Tue, 20 Oct 2026 08:59:45 GMT https://ir.lib.ncu.edu.tw/handle/987654321/111137 title: 家庭AI聊書助理之設計與初步導入：支援親子聊書歷程回顧與家校閱讀資料轉譯;家庭AI聊書助理之設計與初步導入：支援親子聊書歷程回顧與家校閱讀資料轉譯 abstract: 本研究旨在設計並初步導入家庭AI聊書助理，探討其在親子聊書歷程回顧、AI提問建議及家校閱讀資料轉譯中的應用情形。既有學校閱讀活動與師生聊書資料雖逐漸累積，家庭中的親子聊書互動仍較少被具體保存，學校端資料也未必能直接轉化為家庭可接續的對話資源。因此，本研究整合親子聊書錄音、語音轉錄、角色辨識、摘要、CROWD提問方向分析、AI建議問題、歷程回顧與家校資料整合等功能，協助家長保存、理解並延伸親子聊書。 本研究採設計研究法（design-based research， DBR），以質性資料分析為主，問卷、系統操作紀錄與AI輸出檢核為輔。研究包含兩次家庭端系統導入與一次教師觀點訪談，資料來源包括家長前導背景問卷、親子聊書逐字稿、系統紀錄、家長訪談、家校整合內容與教師觀點訪談。研究者先依家庭與研究階段整理使用脈絡，再對照逐字稿、系統紀錄、AI輸出與訪談資料，分析家長如何接觸、理解、參考及轉化系統內容，並整理各階段資料如何支持後續設計調整。 研究結果顯示，系統可將家庭中容易流失的口語互動整理為逐字稿、摘要與提問方向，提供家長回顧互動及尋找後續聊書線索的入口；家長亦不將AI提問建議視為固定腳本，而會依孩子回答、家庭語氣與文本內容進行篩選、縮短、改寫或暫不使用，因此AI較適合作為可選擇與可調整的對話支持。學校端閱讀與聊書資料則需經摘要化、觀察化、脈絡化與行動化整理，才較容易被家長理解，並形成家庭聊天、選書或後續聊書的參考。教師訪談亦補充下一輪家校聊書共備所需的資料內容、角色分工與推動條件；由於本研究未正式實施共備活動，故不推論其成效。本研究指出，家庭閱讀AI工具應協助家長理解、判斷與接續親子互動，而非取代家長或教師；未來研究可延長導入期程、增加參與家庭並納入孩子觀點，以進一步檢視其長期使用條件與實務價值。;This study designed and preliminarily implemented a family AI book-talk assistant to examine its use in reviewing parent–child book-talk， providing AI-generated questions， and translating home–school reading data. Because parent–child book-talk is rarely preserved and school-based data may not readily become usable family resources， the system integrated recording， transcription， speaker identification， summaries， CROWD-based question analysis， AI suggestions， history review， and home–school data integration. The study adopted design-based research (DBR)， using qualitative analysis as the primary approach and questionnaires， system logs， and AI-output evaluation as supplementary data. It included two family implementation stages and one teacher interview stage. Data included parent questionnaires， parent–child book-talk transcripts， system logs， interviews， and home–school integrated content. Data were organized by family and research stage to examine how parents accessed， understood， referred to， and transformed system-generated content. The findings showed that the system transformed easily lost oral interactions into transcripts， summaries， and question-direction records that supported review and later planning. Parents did not treat AI-generated questions as fixed scripts， but selected， shortened， revised， or set them aside according to their children’s responses and conversational styles. AI suggestions were therefore positioned as optional， adjustable conversational support. School-based reading and book-talk data required summarization， observation， contextualization， and action-oriented translation before becoming useful references for family conversation， book selection， or later book-talk. Teacher interviews identified the data content， role distribution， and implementation conditions needed for future home–school book-talk co-planning; because such activities were not formally implemented， no effects were inferred. The study suggests that AI tools for family reading should support parental understanding， judgment， and continued parent–child interaction rather than replace parents or teachers. Future research may extend implementation， involve more families， and include children’s perspectives. <br> Tue, 20 Oct 2026 08:59:09 GMT