Scaffolding Generative AI as a Tutor: A Quasi-Experimental Study of Learning Outcomes and Motivational, Cognitive and Metacognitive Processes

Generative artificial intelligence (AI) is increasingly used in higher education as an interactive tutoring partner rather than a passive information tool. While AI offers opportunities to support learning, concerns remain regarding cognitive offloading, reduced engagement, and unreflective use. Although instructional scaffolding is a well-established design principle for supporting complex learning, its role in shaping cognitive and metacognitive processes in AI-supported settings remains underexplored. This quasi-experimental pre–post study examined how varying levels of scaffolding influence learning outcomes and motivational, cognitive and metacognitive processes during AI-tutored learning. A total of 175 first-semester students from two faculties and diverse academic backgrounds completed the same academic task within a four-hour university session under one of three conditions: (1) full scaffolding, including a structured prompting template based on the Goal–Context–Constraints (GCC) strategy, iterative refinement, and reflective guidance; (2) light scaffolding, including the GCC prompting template; or (3) no scaffolding template as the control condition. Measures included knowledge gain, motivation, cognitive load, critical thinking, and reflective use. Data were analysed using ANOVAs, ANCOVAs, regression models, and PROCESS moderation and mediation analyses. Across the conditions, students showed significant gains in knowledge, critical thinking, and reflective use, while motivation remained stable and intrinsic and extraneous cognitive load decreased; no significant differences between scaffolding conditions were observed. The scaffolding conditions did not produce significant interaction effects, although descriptive trends suggested higher gains in higher-order knowledge under scaffolded conditions. Overall, the findings suggest that short-term learning gains in AI-supported settings may not depend on scaffolding intensity alone, but rather on how learners engage with AI during the learning process.