本研究以「生命智慧代間學習」計畫為案例，探討樂齡代間學習的實踐內涵與AI應用的可能性。採質性個案研究法，訪談三位核心樂齡講師。結果顯示，成功的代間學習源自高齡者的「生產傳承」動機，互動歷程呈現「相互鷹架」的角色流動，並在安親班、教會等場域形成「共享資源庫」的微型實踐社群，促進認知、技能、態度與行為等層面的正向轉變。然現行評量難以客觀呈現動態互動，本研究遂提出以AI為基礎的「計算顯微鏡」分析框架，結合自然語言處理、序列模式探勘與社會網絡分析技術，將質性資料轉化為可量化指標，以提升評估效度。研究最後提出四項結論與三項建議，作為未來代間學習及桌遊卡牌教學創新策略的重要參考。

This study explores the practice of intergenerational learning among older adults and the potential integration of artificial intelligence (AI), using the Wisdom of Life Intergenerational Learning program as a case. Employing a qualitative case study approach, three senior instructors were interviewed. Findings show that successful intergenerational learning arises from older adults’ intrinsic motivation for “productive generativity.” The process features “mutual scaffolding” in role dynamics and the formation of micro practice communities with “shared resource pools” across contexts such as after-school programs and churches, leading to positive cognitive, skill-based, attitudinal, and behavioral growth in both generations. However, current evaluation methods cannot fully capture these dynamic interactions. To address this, the study proposes an AI-based “Computational Microscope” framework that applies natural language processing (NLP), sequential pattern mining (SPM), and social network analysis (SNA) to convert qualitative data into quantifiable indicators and improve assessment validity. The study concludes with four key findings and three recommendations, offering insights for future innovations in intergenerational learning and game-based teaching strategies.