隨著現代網頁對於快速且流暢的使用體驗需求不斷提升，前端開發技術越來越多樣複雜，然而，現有文獻缺乏一套系統化的前端技術評估指引，導致開發者在選擇適合的解法時面臨困難，需耗費大量時間與心力。本研究旨在透過一套結構化的樣式系統，提供前端技術的系統化指引，以協助開發者更有效率的選用合適的解決方案。研究聚焦於三個核心領域：「操作 DOM」、「繪製」與「Hydration」，共建立 11 份樣式。為了評估這些樣式，作者引入生成式 AI（ChatGPT），搭配多面向的評量規準，這份評量規準根據過去研究中品質好的樣式所具備的屬性所建立，形塑出一套標準化且有效的評估架構。本研究進行兩輪樣式評估實驗，邀請具前端經驗的開發者與 ChatGPT 參與。第一輪中，人類評估者與 ChatGPT 皆依照評量規準，各自對樣式進行評分與回饋。第二輪則將人類回饋提供給 ChatGPT，觀察其在獲得更多語境資訊後回饋內容的變化。結果顯示，ChatGPT 在語境理解與結構推理方面有所進步，但在補充技術細節與準確解讀使用者需求方面，仍明顯落後於人類專家。本論文提出一套可應用於前端開發的實用樣式系統，並探討導入生成式 AI 進行樣式協作評估的可行性與侷限性。研究成果為未來推進前端繪製樣式的實務發展奠定了基礎，並為設計 AI 輔助評估流程提供了參考。

As modern web applications increasingly demand fast and seamless user experiences, frontend technologies have become more diverse and complex. However, the existing literature lacks a systematic guideline for evaluating frontend technologies, making it difficult and time-consuming for developers to select appropriate solutions. This thesis aims to provide a systematic guideline for frontend technologies through a structured system of patterns, helping developers more efficiently select appropriate solutions. Focusing on three core areas, namely, DOM manipulation, rendering, and hydration, this thesis introduces a total of 11 patterns. To evaluate these patterns, the author incorporates generative AI (ChatGPT) alongside multi-dimensional rubrics. These rubrics are based on attributes derived from high-quality patterns in prior research, forming a standardized and effective assessment framework. The author conducted a two-round evaluation process involving experienced frontend developers and generative AI (ChatGPT). In the first round, both human evaluators and ChatGPT independently assessed the patterns using the defined rubrics. In the second round, human feedback was provided to ChatGPT to examine how its responses evolved with additional context. The results indicate that the AI demonstrated improvements in contextual understanding and structural reasoning. However, it continued to lag behind human experts in supplying detailed technical insights and accurately interpreting user needs. This thesis presents a practical system of patterns for frontend development and explores the feasibility and limitations of incorporating generative AI into collaborative pattern assessment. The findings lay the groundwork for advancing rendering pattern practices and inform the design of future AI-assisted evaluation workflows.

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