隨著人工智慧於生活中的應用日益增加，人工智慧教育在K-12逐漸受到國際重視，然而過去人工智慧教學多實施於高等教育，目前較缺乏針對高中生設計的人工智慧教材。人工智慧包括許多抽象概念，以往針對K-12的人工智慧教育多著重在使用現有工具動手操作，而不介紹人工智慧理論，學習者難以瞭解其背後原理。
模擬可以將抽象概念具體化。為了幫助學習者理解人工智慧抽象概念，本研究建置人工智慧主題的視覺化模擬輔助學習平臺，並提出模擬輔助教學步驟：操作觀察、概念探索、概念整合。研究探討所提出之視覺化模擬輔助人工智慧教學對高中生人工智慧素養（包含人工智慧概念與人工智慧演算法實作）、學習態度以及運算思維之影響。為瞭解視覺化模擬輔助人工智慧教學的有效性，本研究透過實證研究比較視覺化模擬輔助教學與傳統教學之差異。研究結果發現：
一、視覺化模擬輔助人工智慧教學對人工智慧素養之影響
實驗組在經過視覺化模擬輔助人工智慧教學後，其人工智慧概念與人工智慧演算法實作皆較控制組佳。顯示出視覺化模擬輔助人工智慧教學比傳統講述式教學更能夠幫助學習者學習人工智慧概念以及人工智慧演算法實作，透過視覺化模擬輔助人工智慧教學可以增進學習者之人工智慧素養。而在訪談中，實驗組學習者表示在視覺化模擬輔助平臺上修改演算法的參數以及實作練習題填答完後給予的即時回饋，皆有助於其對演算法之理解。此外，分析視覺化模擬輔助平臺的使用情形顯示，本研究發展之視覺化模擬輔助教學中，以「概念整合」的引導最能幫助學習者理解人工智慧概念。
二、視覺化模擬輔助人工智慧教學對學習態度之影響
經過本研究之視覺化模擬輔助人工智慧教學後，在「對人工智慧課程的感受」之學習態度面向，統計結果為實驗組顯著較控制組佳。顯示視覺化模擬輔助人工智慧教學比傳統講述式教學更能夠增加學習者學習人工智慧正向的態度，訪談中實驗組多數學習者提到「程式設計」之運用，可見他們將人工智慧概念與實作產生更多連結，更深入瞭解程式設計的意義與重要性。
而在「視覺化模擬輔助人工智慧教學的有效性」面向，根據對實驗組學習者進行的半結構式訪談結果顯示，學習者普遍認為本研究發展之學習步驟具有引導學習之效，因此本研究發展之學習步驟有助於幫助學習。
三、視覺化模擬輔助人工智慧教學對運算思維之影響
實驗組經過視覺化模擬輔助人工智慧教學以及控制組經過傳統講述式教學後，兩組運算思維無顯著差異。代表視覺化模擬輔助人工智慧教學不會影響學習者的運算思維。此結果可能的原因為本研究之實驗時間有限，而運算思維需經過長時間的培養才能有所提升。

With the development of Artificial Intelligence (AI) technologies, AI instruction has become an important issue for K-12 education. However, traditional AI instruction is only for higher education and focuses more on theories. Since AI involves many abstract and complex concepts, it is challenging for k-12 students to understand.
Simulation allows students to interact with abstract concepts by operating complex procedures involved in the concepts with visualization, which leads to a better understanding of the concepts. This research proposed an AI instruction based on visualization and simulation, and developed a simulation learning platform. An empirical study was also conducted to examine the effectiveness of the proposed instruction, in which the experimental group used the simulation learning platform and was guided with the proposed learning stages: operation and observation, concept exploration, and concept integration; the control group was taught by the traditional lecture-based instruction. The research findings are as follows:
1. The effectiveness of simulation-based AI instruction on students’ AI literacy.
The experiment results show that the experimental group performed better in both AI concepts and AI algorithm implementations than the control group. Through modifying the parameters of the algorithms and observing the instant feedback, students could understand better about the AI algorithms. The students were benefited most from the “concept integration” stage because they could apply the learnt concepts and observe the visualized results, which clarified their concepts.
2. The effectiveness of simulation-based AI instruction on students’ learning attitude.
The experimental group had more positive attitude towards AI courses than the control group. This implies that visualization and simulation might enhance students’ learning attitude towards AI. Most of the students in the experiment group mentioned about “programming” in the interview, which implies that they might build better connections between concepts and implementations, and they were more aware of the importance of programming and AI applications. Students also reflected in the interview that the guidance and simulation tools in the instruction helped them grasp the concepts and procedures better.
3. The effectiveness of simulation-based AI instruction on students’ computational thinking.
Although students could understand better about the algorithms and concepts by the simulation-based instruction, there was no significant difference between the experimental and control groups in the aspect of computational thinking. This might be because computational thinking needs to be cultivated for a long time to improve, but the experimental duration was was insufficient in this study.

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