About the Author:
Dr Alvin Chan is the Yvon Pfeifer Professor of Artificial Intelligence & Emerging
Technologies at Cambridge Corporate University (Switzerland), specialising in AI
and educational innovation. He has led teacher training in digital pedagogy and
generative AI, developed AI-powered educational applications, and pioneered the
integration of Multiple Intelligence frameworks. Dr Chan has held academic
leadership roles, serves on editorial boards, and is a peer reviewer for leading journals
in artificial intelligence. His work centres on scalable, inclusive AI solutions for
teaching and learning.
Abstract:
The article explores the transformative potential of generative AI, particularly Large
Language Models (LLMs), in advancing adaptive learning within education. It
highlights how AI-powered tools enable personalised, dynamic learning experiences
by tailoring content and feedback to individual student needs, grounded in established
educational theories such as behaviorism, cognitivism, and constructivism. A key
innovation discussed is “Vibe Coding,” a no-code platform that empowers educators
to create custom AI-driven applications using natural language, reducing reliance on
technical expertise and fostering teacher autonomy. The paper emphasizes the
practical benefits of AI tools like automated lesson planning, adaptive assessments,
and multimedia content creation, which streamline teaching workflows and enhance
student engagement. It also addresses critical ethical and practical challenges,
including algorithmic bias, data privacy, and equitable access, underscoring the need
for robust governance and professional development. Case studies of platforms like
Pico demonstrate the effectiveness of these technologies in real classrooms,
supporting diverse learners and reducing teacher workload. Ultimately, the article
advocates for a collaborative approach among educators, policymakers, and
developers to responsibly integrate generative AI in education, ensuring it promotes
equity, innovation, and improved learning outcomes.
Keywords: generative AI, adaptive learning, large language models (LLMs), no-code
platforms, personalized learning, and educational technology.