AI-driven education platforms have made some progress in personalisation, yet most remain constrained to static adaptation--predefined quizzes, uniform pacing, or generic feedback--limiting their ability to respond to learners' evolving understanding. This shortfall highlights the need for systems that are both context-aware and adaptive in real time. We introduce PAL (Personal Adaptive Learner), an AI-powered platform that transforms lecture videos into interactive learning experiences. PAL continuously analyzes multimodal lecture content and dynamically engages learners through questions of varying difficulty, adjusting to their responses as the lesson unfolds. At the end of a session, PAL generates a personalized summary that reinforces key concepts while tailoring examples to the learner's interests. By uniting multimodal content analysis with adaptive decision-making, PAL contributes a novel framework for responsive digital learning. Our work demonstrates how AI can move beyond static personalization toward real-time, individualized support, addressing a core challenge in AI-enabled education.

翻译：人工智能驱动的教育平台在个性化方面已取得一定进展，但大多数仍局限于静态适应——预设的测验、统一的学习节奏或泛化的反馈——这限制了其应对学习者不断变化的理解能力的能力。这一不足凸显了对既能感知上下文又能实时适应的系统的需求。我们提出PAL（个性化自适应学习器），这是一个由人工智能驱动的平台，可将讲座视频转化为互动学习体验。PAL持续分析多模态讲座内容，并通过提出不同难度的问题动态地吸引学习者，随着课程的展开根据他们的回答进行调整。在课程结束时，PAL会生成个性化总结，强化关键概念，同时根据学习者的兴趣定制示例。通过将多模态内容分析与自适应决策相结合，PAL为响应式数字学习提供了一种新颖的框架。我们的工作展示了人工智能如何超越静态个性化，走向实时、个性化支持，从而解决人工智能赋能教育中的一个核心挑战。