The impact of GenAI-enabled coding hints on students' programming performance and cognitive load in an SRL-based Python course

Abstract

Programming education often imposes a high cognitive burden on novice programmers, requiring them to master syntax, logic, and problem-solving while simultaneously managing debugging tasks. Prior knowledge is a critical factor influencing programming learning performance. A lack of foundational knowledge limits students' self-regulated learning (SRL) abilities, resulting in a performance gap between students with high and low levels of prior knowledge. To address this problem, this study developed CodeFlow Assistant (CFA), a specifically developed generative artificial intelligence (GenAI) tool that provides four levels of scaffolding guidance (flowcharts, cloze coding, basic coding solutions, and advanced coding solutions) to support novice programmers in mastering skills ranging from foundational understanding to advanced application. Through a controlled experiment comparing SRL-based, teaching assistant (TA)-assisted programming (SRLP-TA) and SRL-based, CFA-assisted programming (SRLP-CFA), this study evaluated the effect of CFA on coding performance, cognitive loads, and SRL abilities among novice programming students. The results indicated that compared with the SRLP-TA group, the SRLP-CFA group achieved statistically significantly higher coding scores but showed comparable improvements in understanding programming concepts. Moreover, CFA reduced intrinsic and extraneous cognitive loads while enhancing germane load, fostering deeper knowledge integration and engagement. These findings highlight the role of CFA in enhancing coding performance, particularly in translating conceptual understanding into practice. This tool also statistically significantly improved SRL abilities, such as intrinsic goal orientation, task value, and metacognitive self-regulation.