Understanding the Effects of GenAI as No-Code Alternative for Teaching Machine Learning Workflows - Strobel, Martin (Proceedings of the AAAI Conference on Artificial Intelligence, 2026)
Teaching machine learning (ML) workflows to non-programmers remains a challenge in introductory AI courses. Traditionally, educators have turned to no-code tools such as KNIME to lower barriers. With the rise of generative AI (GenAI), students can now construct ML pipelines through natural language prompts, potentially offering a new “no-code” pathway. In a polytechnic-wide elective in Singapore, students were given the choice of using either KNIME or a GenAI chatbot for practical exercises and their semester project. Survey responses, informal interviews, and classroom observations revealed that both tools supported conceptual learning, but students’ experiences diverged: KNIME provided predictability and structured guidance, while GenAI offered speed and flexibility yet posed setup challenges and required coding familiarity. Students valued having a choice, though this complicated teaching logistics. Our experience suggests that GenAI can complement—but not yet replace—traditional no-code platforms, and that the design of introductory activities is critical for adoption. We share lessons learned for educators considering GenAI as an alternative in workflow-based ML education.