Technology and self-regulated learning

Abstract

Technology and self-regulated learning (SRL) are at an important, convergent moment in higher education. The public release of generative AI in late 2022 has normalised “on-demand” cognitive support. Evidence shows, however that indiscriminate use of AI can blunt the metacognitive, motivational and strategic behaviours that underpin effective learning. This risk is most acute in first-year transition, when many learners arrive with fragile SRL skills and conflicting signals about institutional AI policy. SRL must become a first-order design lens for any AI-enabled curriculum.

To frame the challenge, this Trendsetter talk draws on systematic reviews, mixed-methods field studies and longitudinal analytics conducted by colleagues worldwide, as well as Chris’ two decades of research at the intersection of assessment, technology and SRL in higher education. Taken together, recurring, framing patterns present themselves and link to current problems in the intersection of AI and SRL. These include productive dialogic prompting, where learners iteratively question AI outputs, and shallow answer harvesting, marked by minimal goal setting and weak monitoring.

Building on this literature and Chris’ current work, the Talk will introduce the GenAI-SRL Design Framework, a concise set of principles that positions AI as both reflective tool and analytic lens. Four design levers—Goal Alignment, Metacognitive Visibility, Ethical Guard-rails and Data-informed Adaptivity—are illustrated through classroom cases and open resources, including prompt libraries, dashboard templates and SRL detectors.

For the Scholarship of Technology Enhanced Learning community, the session offers three practical take-aways: (1) a framework for embedding SRL into AI-rich course designs; (2) recommendations for capturing SRL in situ; and (3) guidance for conducting research, aligning policy, and progressing curriculum and professional development around responsible uses of AI.

Cultivating a reciprocal relationship between technology and SRL is essential for sustaining learner agency in an era of ubiquitous AI. Moving beyond binary narratives of “ban or embrace”, allows us to develop learning ecologies in which technology sharpens—rather than dulls—students’ ability to plan, monitor and evaluate their own learning.