本研究旨在探討生成式AI融入高中數學教學對學生「自主學習素養及態度」與「數學學習成效」之實質影響。研究採取量質融合之混合研究設計，以新竹市某公立高中高二兩個常態編班班級為研究對象，其中實驗組學生接受為期三週的生成式AI輔助數學教學介入，以一週為分界分為初、中、後三期，控制組學生則接受常規傳統講授教學。量化工具包含「自我導向學習準備度量表」與數學前後測試卷，資料分析採獨立樣本t檢定、MANCOVA與Hayes之PROCESS Model 1調節效應分析；質性資料則透過《生成式AI之輔助學習經驗學習單》進行主題分析，以實踐量質資料之三角交叉驗證。
研究結果顯示：
一、在自我導向學習準備度方面，生成式AI之融入能顯著提升學生的整體準備度及其「自我管理」、「渴望學習」與「自我控制」三個核心向度。質性文本亦證實，去中心化的AI課堂能驅動學生主動調控學習步調，且工具之非評判性互動特質能建立低風險心理安全網，有效消解學生的數學焦慮。
二、在數學學習成效方面，量化分析證實學生的「數學前測起點能力」具備顯著的調節作用。生成式AI對低起點能力學生展現出「補償教育效果」，能有效提供微型鷹架以填補傳統大班制之認知斷層；然而，對於高起點能力學生，其邊際效益呈現收斂趨勢，且質性分析警示其可能產生「溫水依賴」之深層認知鈍化現象。
三、在課堂生態重塑上，學生在介入初期需經歷提示詞（Prompt）發問困境之調適期，而中後期偶發之「AI幻覺」與計算偏誤，反倒成為培養學生批判性查核與元認知反思之隱性課程。

The purpose of this study was to investigate the substantial impact of integrating generative AI into senior high school mathematics instruction on students' "self-directed learning competencies and attitudes" and "mathematics learning achievement." A mixed-methods research design combining quantitative and qualitative approaches was employed, selecting two parallel tenth-grade classes from a public senior high school in Hsinchu City as the research participants. The experimental group received a several-week generative AI-assisted mathematics instructional intervention, while the control group received conventional lecture-based instruction. Quantitative instruments included the "Self-Directed Learning Readiness Scale" and mathematics pre- and post-tests. Data were analyzed using independent samples t-tests, MANCOVA, and Hayes' PROCESS macro for moderation analysis. Qualitative data collected via the "Generative AI-Assisted Learning Experience Worksheet" underwent thematic analysis to achieve triangulation between quantitative and qualitative sources.
The findings indicate that:
(1) Regarding self-directed learning readiness, the integration of generative AI significantly enhanced students' overall readiness and its three core dimensions: self-management, desire for learning, and self-control. Qualitative data further confirmed that the decentralized AI classroom empowered students to autonomously regulate their learning pace, and the non-judgmental interactive nature of the tool constructed a low-risk psychological safety net, effectively mitigating mathematics anxiety.
(2) In terms of mathematics learning achievement, quantitative analysis verified that students' initial mathematics competency (pre-test scores) exerted a significant moderating effect. Generative AI demonstrated a powerful "compensatory educational effect" for students with lower initial competencies, effectively providing micro-scaffolding to bridge cognitive gaps inherent in traditional large-group instruction. However, for students with higher initial competencies, its marginal benefits exhibited a converging trend, and qualitative insights warned of a "warm-water dependence" effect that potentially causes deep cognitive passivation.
(3) Concerning classroom ecology reshaping, students initially experienced an adaptation period marked by a prompt-phrasing dilemma. In contrast, occasional "AI hallucinations" and calculation errors during the middle and later stages unexpectedly served as a hidden curriculum, cultivating critical verification and metacognitive reflection.

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