#1161: ‘I Spend All My Energy Preparing’: Balancing AI Automation and Agency for Self-Regulated Learning in SmartFlash

Effective study strategies fail when preparatory tasks consume learning time. While AI educational tools demonstrate efficacy, understanding how they align with self-regulation needs in learners' own study contexts remains limited. We conducted formative design research using an AI flashcard prototype, employing large language models to generate design hypotheses validated through researcher walkthroughs and student sessions. Six students across disciplines completed sessions combining interviews and think-aloud tasks with their materials. Analysis revealed students value automation for addressing overwhelming preparation burden yet require transparent, editable AI outputs to maintain cognitive ownership essential for self-regulation. They conceptualized AI as collaborative partner demanding verifiable reasoning rather than autonomous agent. Metacognitive scaffolding was endorsed when clarifying study direction without constraining choice. Motivational features produced divergent responses. We derive design principles prioritizing editability and transparency, scaffolding metacognition without prescription, and accommodating motivational diversity. Findings identify conditions under which automation supports versus undermines metacognitive development in self-regulated learning.