Purpose: Challenges in teaching the engineering design process (EDP) at the high-school level, such as promoting good documentation practices, are well-documented. While developments in educational artificial intelligence (AI) systems have the potential to assist in addressing these challenges, the open-ended nature of the EDP leads to challenges that often lack the specificity required for actionable AI development. In addition, conventional educational AI systems (e.g. intelligent tutoring systems) primarily target procedural domain tasks with well-defined outcomes and problem-solving strategies, while the EDP involves open-ended problems and multiple correct solutions, making AI intervention timing and appropriateness complex. Design/methodology/approach: Authors conducted a six-week-long Research through Co-Design (RtCD) process (i.e. a co-design process rooted in Research through Design) with two experienced high-school engineering teachers to co-construct actionable insight in the form of AI intervention points (AI-IPs) in engineering education where an AI system can effectively intervene to support them while highlighting their pedagogical practices. Findings: This paper leveraged the design of task models to iteratively refine our prior understanding of teachers' experiences with teaching the EDP into three AI-IPs related to documentation, ephemeral interactions between teachers and students and disruptive failures that can serve as a focus for intelligent educational system designs. Originality/value: This paper discusses the implications of these AI-IPs for designing educational AI systems to support engineering education as well as the importance of leveraging RtCD methodologies to engage teachers in developing intelligent educational systems that align with their needs and afford them control over computational interventions in their classrooms.