Differentiated Formative Feedback Through Parameterized ChatGPT: Effects on Knowledge Construction, Engagement, and Higher-Order Thinking in STEM Education

Abstract

STEM education increasingly emphasizes interdisciplinary integration and self-directed learning, yet students with different achievement levels often face unequal challenges when navigating complex cross-domain content. High-achieving students may lack sufficient cognitive stimulation, whereas low-achieving students may struggle with overwhelming complexity. Standard ChatGPT feedback lacks adaptability to these diverse learner capabilities, leaving some students unchallenged and others overloaded. Addressing these disparities requires timely and differentiated instructional support. This study proposes AdjustGPT, a system that calibrates ChatGPT’s generative parameters (temperature, max_tokens, frequency_penalty, presence_penalty, and top_p) according to learners’ achievement levels to deliver differentiated formative feedback. Grounded in formative feedback theory and cognitive load principles, AdjustGPT provides high-achieving students with exploratory, multi-perspective feedback that fosters critical thinking and innovation, while offering low-achieving students clear, structured guidance that reduces cognitive load and strengthens foundational understanding. A quasi-experimental study with 62 university students in a STEM course showed that, within this exploratory sample, AdjustGPT was associated with significant gains in knowledge construction, cognitive and emotional engagement, and higher-order thinking outcomes, including critical thinking and problem-solving. These findings provide initial evidence that parameterized ChatGPT systems can support adaptive formative feedback and differentiated AI-assisted teaching in STEM education. Larger-scale studies are needed to establish robustness and generalizability.