Learning Letters

Editorial

Lisa-Angelique Lim, Jacqueline Wong — Fri, 03 Jul 2026 00:00:00 +0000

Self-regulated learning (SRL) underpins academic success and lifelong learning, yet remains a persistent challenge for many learners. With the increasing adoption of generative AI (GenAI) in higher education, learners, educators, and researchers find themselves walking a tightrope: how can they balance using GenAI to support human learning while preserving learner agency and epistemic responsibility? This special issue of Learning Letters explores this tension, offering timely insights into the impact of GenAI on SRL. The four featured articles examine how learners self-regulate their learning when using GenAI tools across different tasks and learning contexts, and how GenAI can be leveraged as a dynamic, context-aware SRL support. Together, the studies reveal a recurring tension between efficiency and depth. The editorial synthesises these contributions, identifying two key distinctions: distinguishing cognitive offloading as reduction versus substitution, and assessment for learning versus assessment of learning. Building on these distinctions, two directions for future research are advocated: designing tailored GenAI tools that scaffold SRL and prioritising teaching learners how to self-regulate effectively. Overall, this special issue highlights the core challenge for SRL research: finding the balance between GenAI-enabled support, and the imperative to develop critical, independent learning skills.

How do students regulate their learning with a genAI chatbot?

Lyn Lim, Maria Bannert — Mon, 23 Feb 2026 00:00:00 +0000

Self-regulated learning (SRL) is essential for effective learning, yet students often struggle to regulate in digital environments, including suboptimal learning tool use. The rapid integration of generative AI (e.g., ChatGPT) into learning settings raises questions about their role in supporting or hindering SRL. This exploratory study investigated how students’ SRL in a technology-enhanced learning environment with a genAI tool was associated with learning processes and performance. Thirty university students were tasked to read texts and write an essay within 45 minutes. Learning performance was measured using knowledge and transfer tests, and the essay. SRL processes were measured using trace-based SRL event analysis and learner-genAI interaction with chatbot log events and coded queries. Most students (73%) used the chatbot voluntarily, primarily for seeking information. Chatbot users achieved higher essay scores than non-users. Chatbot interaction frequencies correlated positively with high cognitive activities; durations of chatbot use and high cognition negatively correlated with reading time. Qualitative data indicated reliance on the chatbot to summarise and extract key points, suggesting offloading of learning. Findings highlight potential performance benefits but also risks of outsourcing critical SRL processes to genAI. Implications point to instructional and tool design that align technological advances with educational fit.

Efficiency vs. effectiveness

Olga Viberg, Yael Feldman Maggor, Jacqueline Wong — Sun, 01 Mar 2026 00:00:00 +0000

AI tools powered by large language models (LLMs) are rapidly entering students’ study practices, yet we know little about how they reshape self-regulated learning (SRL), particularly help-seeking and related task strategies. We report a qualitative study with 20 STEM students at a large Swedish university who were interviewed about their use of commercial LLM chatbots. Guided by Zimmerman’s SRL model and the Online SRL subscales, we conducted a thematic analysis. Findings show that LLMs are integrated into a layered, context-dependent help-seeking ecosystem rather than replacing human support. Students described a four-stage process: (1) deciding whether help is needed, typically by attempting problems independently first; (2) choosing a source and using ChatGPT as a low-barrier first step, then peers for conceptual negotiation, and instructors for complex or high-stakes issues; (3) determining the type of help, from seeking hints and explanations to scaffolding problem-solving, streamlining routine work, and extending learning; and (4) judging the help by exercising selective trust, verifying AI outputs against coursework or with humans, and reserving human support for nuanced understanding and affective needs. Overall, students aligned LLM use with SRL goals and task demands, favouring instrumental over executive help-seeking to retain control of problem-solving. The findings suggest an adapted model of help-seeking for LLM-mediated learning practices and implications for promoting verification practices, instrumental help-seeking, and sustained learner agency.

Scaffold or shortcut?

Amara Atif, Camille Dickson-Deane — Mon, 30 Mar 2026 00:00:00 +0000

Generative artificial intelligence (GenAI) tools such as ChatGPT and Copilot are increasingly integrated into higher education, where students use them to summarise texts, solve problems, and generate code. While these tools are can reduce cognitive load and improve learning efficiency, they may also challenge students’ ability to regulate their learning (i.e., self-regulated learning; SRL) by encouraging surface-level engagement and overdependence. This study investigates how GenAI shapes SRL behaviours within a postgraduate information technology (IT) subject/unit/course. A mixed-methods design was employed with 267 students, combining pre- and post-semester surveys with semi-structured interviews. The study examined how students engaged with GenAI and how this affected SRL components of goal setting, monitoring, and self-evaluation. Findings show varied patterns: some students used GenAI to clarify goals, check understanding, and reflect on progress, while others relied on it as a shortcut, outsourcing monitoring and evaluation. The study highlights GenAI’s dual role as a scaffold and shortcut, offering insights for designing learning environments that foster productive use and sustain student agency and autonomy.

GenAI as a learning partner

Yige Song, Paula de Barba, Eduardo Oliveira — Fri, 03 Jul 2026 00:00:00 +0000

Generative AI (GenAI) offers significant potential to scaffold self-regulated learning (SRL) by acting as an adaptive agent or “co-regulator”. However, effectively balancing technological assistance with student effort requires AI systems that recognise SRL not as a static trait, but as a temporal and personalised process. This study investigates these dynamics over a full semester, utilising surveys at the beginning, during and end of semester to track the SRL, motivation, and emotion of 75 first-year university students. We first examine pre- and post-semester shifts, finding individual consistency alongside systemic declines in metacognitive knowledge and wellbeing. We then analyse week-to-week fluctuations, identifying curriculum demands—such as major assessment deadlines—as primary drivers of shifts in student internal states. Finally, we provide a proof-of-concept demonstration by leveraging this longitudinal and contextual information within a Large Language Model to generate tailored support directions. Our findings demonstrate that, when provided with personal, temporal, and contextual information, GenAI can identify appropriate directions for SRL support that respond to a learner’s evolving cognitive and metacognitive needs. This work underscores that, for GenAI to function as an effective learning partner that preserves rather than diminishes student effort, it must be designed with strong contextual awareness, adapting its scaffolding to support students without replacing their cognitive effort.