  Effectively supporting students in mastering all facets of self-regulated
learning is a central aim of teachers and educational researchers. Prior
research could demonstrate that formative feedback is an effective way to
support students during self-regulated learning (SRL). However, for formative
feedback to be effective, it needs to be tailored to the learners, requiring
information about their learning progress. In this work, we introduce LEAP, a
novel platform that utilizes advanced large language models (LLMs), such as
ChatGPT, to provide formative feedback to students. LEAP empowers teachers with
the ability to effectively pre-prompt and assign tasks to the LLM, thereby
stimulating students' cognitive and metacognitive processes and promoting
self-regulated learning. We demonstrate that a systematic prompt design based
on theoretical principles can provide a wide range of types of scaffolds to
students, including sense-making, elaboration, self-explanation, partial
task-solution scaffolds, as well as metacognitive and motivational scaffolds.
In this way, we emphasize the critical importance of synchronizing educational
technological advances with empirical research and theoretical frameworks.
