Evolution and Applications of Codeless AI Model Generation Tools for AI-Driven Education

Abstract

Codeless or no-code AI model generation tools have evolved from Automated Machine Learning (AutoML) to no-code AI platforms, significantly lowering the barrier to AI development. Traditionally, building AI models required extensive programming skills and domain expertise. However, the emergence of AutoML facilitated automated training and optimization, enabling non-experts to leverage machine learning through platforms such as Google AutoML and Microsoft Azure ML. Furthermore, advancements in drag-and-drop code-free AI tools, including Google Vertex AI, IBM Watson AutoAI, and DataRobot, have made AI model development more intuitive. The integration of generative AI has further streamlined model creation, allowing AI models to be built and optimized using natural language inputs, as demonstrated by tools such as ChatGPT API, OpenAI Codex, and Google Bard. Code-free AI tools have broad applications across various industries, enterprises, startups, SMEs, the public sector, and education. Notably, in the education sector, these tools enable educators, students, and administrators to integrate AI-driven solutions without requiring programming expertise, thereby enhancing accessibility and innovation. Despite their advantages, codeless AI tools face challenges related to limited customization for complex AI models, data quality and ethical concerns, and the high costs of cloud-based AI solutions. Future advancements in generative AI are expected to expand the capabilities of these tools, while more user-friendly interfaces will drive broader adoption among non-technical users. Additionally, enhanced AI regulations and ethical frameworks will contribute to greater reliability and transparency in AI models. As code-free AI model generation tools continue to evolve, they are expected to play a crucial role in automating AI development and enhancing accessibility. Their widespread adoption across academic fields, startups, SMEs, and the public sector will contribute to making AI development more inclusive and efficient, fostering innovation across various domains.

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