隨著人工智慧（Artificial Intelligence，AI）技術持續進步，大型語言模型、生成式AI技術與視覺辨識系統已逐漸被導入教育情境，協助學生在複雜的學習任務中獲得即時輔助。AI輔助學習雖能提供個人化建議和語意回饋，但其缺乏對實體任務的感知能力，使得學習者在真實操作中依然面臨許多挑戰。目前大多數AI介入程式教學仍侷限於邏輯和語法層面的支援，對於需要實作與抽象邏輯並重的物聯網程式設計課程，仍無法主動提供充足且情境化的引導。學生在此類課程中常需同時進行程式撰寫、電子元件辨識與電路組裝，對初學者而言，不僅認知負荷高，也容易因錯誤頻繁而降低學習動機與自信心。過往研究大多聚焦於學習成效的提升，卻較少探討AI如何實際影響學生的行為歷程與策略發展。學習行為不只是結果的過程，更反映了學生面對挑戰時的思考邏輯、自我調整能力與動機狀態，若能加以分析與理解，將有助於打造更具適應性與引導性的AI學習系統。因此，本研究建置兩種AI支援學習平台，其中實驗組使用具備即時視覺辨識功能與生成式AI對話模組的智慧電子元件辨識學習系統（IERLS），對照組則僅使用文字型AI輔助系統（IELS）。本研究以18週的IoT專題課程為實驗場域，招募具初階程式經驗的大學生參與，並透過行為序列編碼與轉移分析，結合學習成績、認知負荷問卷與三階段ARCS動機評量等工具，全面探討不同AI系統對學習歷程與學習心理的影響。
研究結果顯示，IERLS系統不僅有效提升學生學習成效，亦能顯著降低外在認知負荷，並促進學生在意義建構與任務策略上的投入。其學習行為展現出更高比例的規劃性與目標導向行為。在課程中後期，實驗組學生的注意力、信心與任務相關性感知皆持續上升，動機維持優於對照組，顯示視覺AI所提供的即時回饋與情境提示機制，能夠強化學習者的自我效能與正向循環。本研究不僅驗證結合視覺辨識與語言生成AI之系統能有效支援具實作性質的課程，也強調分析學習行為歷程對理解AI教學效果的重要性。進一步而言，IERLS所展現的學習支持潛力也顯示，未來可擴展應用至非資訊科系學生、跨領域學習者，或對硬體技術不熟悉之群體，作為平衡技能落差與促進教育平權的實用工具。本研究成果不僅為AI教學設計與學習分析研究提供實證支持，也回應了教育永續性的實踐需求，對促進AI於教學現場之普及與深化具有長遠意義。

As artificial intelligence (AI) continues to evolve, generative language models and visual recognition systems have become increasingly integrated into education to support complex learning tasks. However, most AI tools in programming education remain limited to syntax- and logic-level assistance, lacking contextual awareness for hands-on tasks such as component assembly and physical debugging. This gap is especially evident in Internet of Things (IoT) programming, where students must simultaneously write code, recognize components, and build circuits tasks that pose high cognitive demands and may reduce learners’ motivation and confidence.
This study implemented two AI-supported learning systems: the Intelligent Electronic Components Recognition Learning System (IERLS), which combines real-time visual recognition capabilities with generative AI (GAI) based instructional support, and Intelligent Electronic Learning System (IELS), a text-only AI support platform. Conducted over an 18-week IoT course, the study involved university students with basic programming knowledge. Learning behavior was analyzed using log-based sequential analysis, and learning outcomes were assessed through performance scores, ARCS-based motivation scales, and cognitive load questionnaires. Results revealed that the IERLS group outperformed the control group in learning achievement, reported lower extraneous cognitive load, and showed higher engagement in meaningful, goal-directed learning behaviors. Visual AI support facilitated efficient task sequences involving identification, inquiry, revision, and testing, thereby reducing repetitive trial-and-error behaviors. Furthermore, students using IERLS exhibited sustained gains in attention, confidence, and task relevance, demonstrating enhanced motivation and self-regulation.
This study highlights the value of combining generative and visual AI in hands-on programming education. It also suggests the potential for expanding such systems to interdisciplinary learners, supporting educational equity, and promoting sustainable, adaptive learning environments through behavior-driven, multimodal AI support.

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