Harnessing large language models to develop research-based learning assistants for formative feedback

Abstract 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. In this light, we propose the application of Large Language Models (LLMs) to guide students towards problem-solving through formative feedback. We present LEAP, a novel platform that utilizes advanced LLMs, such as GPT-4o. LEAP empowers teachers with the ability to effectively pre-prompt and assign tasks to the LLM, resulting in formative feedback that stimulates students’ cognitive and metacognitive processes, thereby enhancing self-regulated learning. We demonstrate that a systematic prompt design can provide a wide range of types of scaffolds to students. These scaffolds, which are rooted in educational research, include sense-making, elaboration, self-explanation, partial task-solution scaffolds, as well as metacognitive and motivational scaffolds. Through this approach, we emphasize the critical importance of synchronizing educational technological advances with empirical research and theoretical frameworks. This alignment could potentially ensures the positive and effective application of LLMs in the educational landscape.