Abstract
Workflow automation platforms connect applications and services to automate data transfer and multistep processes. Although widely used in engineering research and institutional administration, including engineering institutions, they are rarely integrated into undergraduate engineering curricula, and their educational adoption introduces ethical, technical, and pedagogical risks. This paper proposes a practical framework for developing, deploying, and assessing workflow-automation-enabled learning tools coupled with generative AI, with explicit attention to institutional constraints and learning outcomes. As a conceptual case study, we present it in a second-year chemical engineering course (Heat and Mass Transfer) to support learning of heat conduction. The platform includes instructor-approved assets such as content slides, solved problems, pre-prompts, and a validated question database, through an automation pipeline that issues structured API calls to a generative AI system and returns explanations and worked examples. A subject matter expert (SME) authors and tests prompt modules aligned with learning outcomes mapped to Bloom's taxonomy levels (understand, apply, analyze, evaluate) and aligned with accreditation expectations. Effectiveness metrics are proposed to estimate learning gains, and a tool quality audit and an SME review are used to verify conceptual correctness and computational validity. To assess risks, we apply a likelihood-impact risk matrix and define five categories: data privacy and protection, transparency, pedagogical impact, institutional compliance, and ethical considerations. Preliminary analysis suggests potential concept gains from structured prompt/content integration but indicates that numerical problem reliability depends strongly on question-database coverage. The highest perceived risks relate to privacy and transparency, followed by pedagogical impact, motivating mitigations such as anonymization protocols, restricted data pathways, and human verification throughout the lifecycle.