<p>Emerging capabilities of generative artificial intelligence (GenAI) offer significant potential to support productive failure (PF)-based learning, which engages adult learners (ALs) in exploring problems before instruction and learning from their initial attempts. However, the effective use of AI to support multifaceted areas of PF-based learning, including problem generation, exploration, consolidation, and knowledge assembly, is limited. Furthermore, AI design principles to support PF-based learning remain under-researched. This study, therefore, aims to investigate ALs’ perceptions of AI applications in enhancing PF-based learning and to explore the essential design principles of AI systems for PF-based learning. To achieve these aims, the study conducted focus group interviews facilitated by scenarios of AI application storyboards and paper prototypes developed by 35 graduate students from two different universities and countries who participated in a collaborative online international learning project. The study findings show that ALs perceive various types of AI to support problem exploration without indirect instruction, solution generation through activating and differentiating prior knowledge, collaborative problem-solving, comparison and contrast, knowledge reorganization, and knowledge transfer across different PF-based learning phases. Furthermore, the study identifies five key AI design principles for PF-based learning, including human-AI collaboration (complementary roles; interpretability; human-in-the-loop; feedback loops), usability (accessibility and inclusivity; error handling and ambiguity resolution; visual cues of AI usage, controllability in AI systems, data interoperability; seamless AI integration within existing technology stack); reflective design (correction mechanisms; follow-up questions; context-aware prompts; error tracking and prevention; continuous learning loops), and emotional design (AI personality; AI conversation design).</p>