隨著大型語言模型技術的快速發展，高等教育的教與學正被重塑：教師可用其協助備課、回饋與分析，學生則以對話方式獲得解題、除錯與創作支援。然而，近期跨國調查顯示，大多數學生雖頻繁使用，但是高等教育機構仍缺乏完善的指導方針與倫理規範下，導致學生缺乏足夠的素養與學術誠信意識。並且，人工智慧的發展已逐漸從單純問答工具轉向嵌入工作流的代理人。在此脈絡下，如何將人工智慧轉化為一種可教導、可練習且可評量的關鍵素養，並建構能支援長週期專案學習的系統化鷹架，已成為解決上述治理落差、提升教學品質，並確保技術應用能有效對齊教育目標的關鍵課題。
儘管人工智慧在教育應用具高度潛力，但專業課程導入仍面臨三項關鍵缺口：通用模型缺乏課程對齊之領域知識而易產生幻覺或淺薄回應、欠缺能結合自我調節學習與專案導向學習的主動式代理人以支撐長週期任務、以及缺乏同時兼顧高階思維、協作歷程與學習成效的統整性評估架構。基於此，本研究以高等教育資訊科技專題課程為場域並依倫理規範完成知情同意，提出可複製的「PDCA知識蒸餾-主動代理-統整評估」框架，透過三項互補實驗驗證：實驗一以PDCA為核心進行知識蒸餾與開源模型領域微調並由專家從流暢性、連貫性、正確性與有用性評估模型品質；實驗二以準實驗比較領域模型與通用模型(ChatGPT)對學生高階思維與學習成效之差異；實驗三以實驗設計建構基於領域模型之專案開發代理人，檢驗其在長週期課程中對高階思維、專案任務與時程管理及整體學習表現之影響。
在模型建置與基礎成效方面，研究結果顯示，透過PDCA循環進行知識蒸餾與微調之領域模型，在專家評估(實驗一)的正確性與有用性構面上顯著優於通用模型，且在流暢性上維持穩定水準。在真實教學場域的驗證(實驗二)發現，使用領域模型之學生在批判性思考與自我調節學習的表現上，顯著優於使用通用模型之學生，且其問題解決能力在課程後期呈現顯著差異。在進階的人機協作與代理人效益(實驗三)方面，實驗結果指出，具備規劃與追蹤功能的專案開發代理人，能顯著提升學生的專案時程管理能力，顯示主動式的節點提醒與進度追蹤能有效輔助長週期任務的執行，但是代理人組在專案成績與創造力表現上增益不顯著，該結果顯示，深層認知的增益或是原創性的突破仍須人機協作機制的互補。
在理論貢獻上，本研究以階梯式設計累積雙軌證據：先以量化指標與專家評估檢核模型品質，再以準實驗驗證課室成效，並以對照設計檢測代理人對歷程治理與專案表現之影響，形成可在真實高教場域再現的研究程序。基於三階段證據鏈，本研究提出三項貢獻：第一，建構具實證效能的教育領域知識模型架構，將異質資料治理、PDCA迭代式知識蒸餾與多維度對齊評估整合為可驗證循環。第二，闡明「領域知識對齊×任務適配」所形成之知識邊界機制，指出其能降低發散雜訊並釋放學生認知資源，促進高階思維發展。第三，建構以五大核心特性為基礎、並以六項人機協作設計準則為核心的教育代理人理論框架，界定代理人由流程治理走向品質治理的必要條件，補足通用代理架構在教育治理面向之不足。
在實務應用上，本研究連結技術開發與教學現場，提出可複製的系統與全歷程實施指引。針對系統開發者，本研究提供App Inventor課程語域之資料轉化、PDCA微調與混合雲部署流程，以對話介面串接後端服務，提升在教室情境的可用性與可部署性。針對Agent設計者，實驗結果顯示僅具規劃與追蹤功能的代理人可強化時程管理，但難以穩定帶動專案品質與創造力，據此建議將Rubric與品質檢核邏輯內嵌為代理人內在機制，以補足流程效率與品質達標之落差。針對AI教育者，本研究彙整並優化「課前把關-課中鷹架-專案治理-評量稽核」四階段指引：封閉式評量維持界線以掌握個體基礎，開放式專案允許AI介入但須提交可回溯證據鏈，以支援稽核與反思。研究限制仍包括單一課程與單學期場域、非隨機分派與資源限制，且代理人尚難直接評測作品品質，後續需跨課程與跨場域驗證其可移植性。

With the rapid advancement of large language model (LLM) technologies, teaching and learning in higher education are being reshaped: instructors can leverage LLMs for lesson preparation, feedback, and analysis, while students can engage in dialogic interactions to obtain support for problem solving, debugging, and creation. However, recent cross-national surveys indicate a persistent governance gap: although most students use generative AI frequently, many higher education institutions still lack comprehensive guidelines and ethical regulations, leaving students insufficiently equipped with AI literacy and academic integrity awareness. Meanwhile, AI has been evolving from standalone question-answering tools toward agents embedded in workflows. Within this context, a central challenge is to transform AI into a teachable, practiceable, and assessable core competency, and to build systematic scaffolding that can support long-cycle project-based learning. Addressing this challenge is critical for mitigating the governance gap, improving instructional quality, and ensuring that technological adoption aligns with educational objectives.
Despite the considerable potential of AI in education, implementation in professional courses continues to face three key gaps: (1) general-purpose models lack curriculum-aligned domain knowledge and are therefore prone to hallucinations or superficial responses; (2) there is a shortage of proactive agents that integrate self-regulated learning (SRL) and project-based learning (PBL) to support long-cycle tasks; and (3) an integrative evaluation framework that simultaneously captures higher-order thinking, collaborative processes, and learning outcomes remains underdeveloped. Accordingly, this study was conducted in an information-technology capstone course in higher education with informed consent obtained under ethical procedures. We propose a replicable framework—“PDCA-based knowledge distillation, proactive agency, and integrative evaluation”—and validate it through three complementary experiments: Experiment 1 applies a PDCA (Plan–Do–Check–Act)-centered distillation and domain fine-tuning process using an open-source model and evaluates model quality via expert review across fluency, coherence, correctness, and usefulness; Experiment 2 adopts a quasi-experimental design to compare a domain model with a general-purpose model (ChatGPT) in terms of students’ higher-order thinking and learning achievement; and Experiment 3 employs an experimental design to build a project-development agent grounded in the domain model and to examine its effects on higher-order thinking, project task and schedule management, and overall learning performance in a long-cycle course.
Regarding model construction and baseline effectiveness, results show that the domain model produced through PDCA-driven knowledge distillation and fine-tuning significantly outperforms the general-purpose model on correctness and usefulness in expert evaluations (Experiment 1), while maintaining stable fluency. In authentic classroom validation (Experiment 2), students using the domain model perform significantly better than those using the general-purpose model in critical thinking and SRL, and their problem-solving ability exhibits significant differences in the later stages of the course. For advanced human-AI collaboration and agent effectiveness (Experiment 3), findings indicate that the project-development agent with planning and tracking functions significantly improves students’ schedule management capability, suggesting that proactive milestone prompting and progress monitoring can effectively support the execution of long-cycle tasks. However, the agent group shows no significant gains in project grades or creativity, implying that improvements in deep cognition and breakthroughs in originality still require complementary human-AI collaboration mechanisms.
In terms of theoretical contributions, this study accumulates dual-track evidence through a staged design: model quality is first verified through quantitative indicators and expert evaluation, classroom effects are then validated via a quasi-experiment, and agent impacts on process governance and project performance are finally examined through a controlled comparison, yielding a replicable research procedure in authentic higher education settings. Based on this three-stage evidence chain, the study makes three contributions. First, it constructs an empirically grounded educational domain-knowledge model architecture that integrates heterogeneous data governance, PDCA-based iterative knowledge distillation, and multi-dimensional alignment evaluation into a verifiable cycle. Second, it explicates a knowledge-boundary mechanism formed by “domain alignment × task fit,” arguing that it reduces divergent noise and releases learners’ cognitive resources to facilitate higher-order thinking development. Third, it advances a theoretical framework for educational agents grounded in five core characteristics and operationalized through six human–AI collaboration design principles, specifying the necessary conditions for agents to move from process governance to quality governance and addressing limitations of general-purpose agent architectures in educational governance.
In practical terms, this study bridges technical development and instructional practice by delivering replicable systems and end-to-end implementation guidance. For system developers, we provide procedures for data transformation in the App Inventor course discourse, PDCA-based fine-tuning, and hybrid-cloud deployment, connecting a conversational interface to backend services to improve classroom usability and deployability. For agent designers, results indicate that agents equipped only with planning and tracking functions can strengthen schedule management but may not reliably improve project quality or creativity; accordingly, we recommend embedding rubrics and quality-checking logic as internal mechanisms to close the gap between process efficiency and quality attainment. For AI educators, we refine a four-phase guideline—pre-class validation, in-class scaffolding, project governance, and assessment auditing—maintaining clear boundaries for closed-book assessments to capture individual foundations while allowing AI involvement in open-ended projects contingent on submission of a traceable evidence chain to support auditing and reflection. Limitations include the single-course, single-semester setting, potential confounds due to non-random assignment and resource constraints, and the agent’s current inability to directly evaluate artifact quality; future work should conduct cross-course and cross-context validation to assess transferability.

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