Jacqueline Wong

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.

Generative AI (GenAI) in education has become a divisive topic. While some teachers view GenAI as a transformative tool, others caution against harms and impacts on teaching and learning. Since the usefulness of a tool depends on the user, how teachers view and use GenAI and their perceptions of its role in teaching and learning may influence its benefits and risks. The study adopts a qualitative approach, interviewing 23 higher education teachers with teaching experience across disciplines. Using the Technological, Pedagogical, and Content Knowledge (TPACK) and sensemaking frameworks to guide and analyse the interviews, a set of opportunities, challenges, and threats associated with integrating GenAI in higher education was identified. Teachers highlighted opportunities, including using GenAI as a study buddy. Concerns were raised about students’ overreliance on GenAI, GenAI undermining the teaching and learning process, and issues like undetected plagiarism. The findings suggest a need for professional development to help teachers understand GenAI and how it can be effectively used in teaching. Some teachers warned against the paradox of using a tool to save time, only to find that it might increase workload and frustration. These insights contribute to developing guidelines and informing policymaking to ensure integration of GenAI in education.

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.

Effective behavioural engagement (BE), such as putting effort and time into using learning materials, is critical to academic success. However, students often struggle to maintain their BE over time. As web-based learning becomes increasingly integral to STEM education, offering students the flexibility to consolidate complex materials at their own pace, supporting BE is essential. This longitudinal study examines the effect of an intervention—using biweekly planning prompts and personalised feedback during a 9-week course—on BE and course performance. In addition, the moderating role of motivation on this effect was examined. BE was operationalised using theory-driven log-type indicators capturing several aspects of online effort and time. Students (N=173) were randomly assigned to one of three conditions: (1) control group received general reminders, (2) planning-only group received planning prompts, and (3) planning-feedback group received planning prompts and personalised emails comprising process-based feedback based on their BE with the web-based platform. Results showed that planning prompts could enhance student effort in completing the tasks. Personalised feedback combined with planning prompts could promote consistent BE among students with strong value beliefs, suggesting the need for better integration of motivational support. The study provides insights into the development of adaptive systems to support complex engagement processes.

Engaging in independent learning during self-study time is an essential part of learning in higher education. The ubiquity of mobile devices and their applications offer students a flexible, on-the-go learning experience. However, flexible learning environments require students to self-regulate their learning. The current study examined student engagement with a mobile study app intended for students to engage in retrieval practice with feedback as part of self-regulated learning (SRL). Three research questions were addressed: (1) What are the student engagement profiles that can be identified based on students’ activity with the study app? (2) Do the profiles differ in SRL and academic performance? and (3) What is the relationship between the identified student profiles, SRL, and academic performance? Through a learning analytics approach, results identified three distinct student profiles: active, disengaged, and utilitarian. Self-efficacy and time-management positively predicted exam grades, while self-evaluation showed an inverse effect. Active engagement positively influenced exam grades, underscoring individual differences in SRL.

Purpose: As key figures in curriculum design and implementation, course coordinators operate at the intersection of institutional goals, the needs of students, institutional policy and classroom practice, balancing educators alike (Stark and Lattuca, 1997). Their perspective is especially valuable in understanding the broader implications of GenAI integration, as they are often responsible for shaping educational experiences and adapting course content in response to new technologies. Therefore, this study examines how course coordinators view and integrate GenAI into curriculum and teaching practices. The study contributes to the ongoing discourse on GenAI in education, providing practical implications for how higher education institutions might develop policies and support structures that foster responsible and effective GenAI integration. Design/methodology/approach: A qualitative case study methodology was adopted which facilitated an in-depth exploration within a real-world context (Stake, 1994). In this instance, it provided a comprehensive understanding of the processes, challenges and opportunities faced by university-based course coordinators as they adapted to GenAI at a prominent higher education institution in the Netherlands, selected for its position at the forefront of educational innovation. Representing new educational strategies, an interdisciplinary framework and a forward-thinking approach to technology integration, this institution served as a “microcosm” of broader trends in higher education (Crowe et al., 2011). Findings: The findings illustrate that course coordinators at a Dutch university have mixed reactions to GenAI, viewing it as both an educational innovator and a disruptor. They raised concerns about its impact on academic integrity, curriculum relevance and educational values. Coordinators stressed the need for a re-evaluation of skills and knowledge taught in response to GenAI’s disruptive potential. While some faculties experimented with GenAI, many lacked clear policies or strategies for integration, highlighting a knowledge gap. Course coordinators also acknowledged GenAI’s inevitability in education, suggesting a shift toward adoption driven by external pressures rather than perceived usefulness. They proposed collaborative efforts and training programs to support GenAI integration. Research limitations/implications: This study had several limitations. The recruitment of course coordinators was targeted, potentially introducing selection bias, as participants with prior interest in GenAI may have skewed the findings. Future studies should use random sampling to capture diverse perspectives. Additionally, the rapid evolution of GenAI technology may limit the long-term relevance of the findings. Future research could adopt a longitudinal design to continuously update the framework and data collection methods. Finally, the study’s focus on a single university limits transferability; comparative studies across multiple institutions would provide broader insights into GenAI integration in different educational contexts. Practical implications: Our study offers several practical implications for higher education. First, our findings suggests facilitating structured collective sensemaking sessions through workshops and discussions. Additionally, it calls for a re-evaluation of educational values due to GenAI’s disruptive potential and the need for clearer top-down strategies alongside bottom-up approaches. The study advocates for targeted training programs as well to bridge knowledge gaps, while also proposing adaptive strategies to prepare institutions for the inevitable integration of GenAI into education. Originality/value: The study offers valuable empirical insights into how course coordinators make sense of GenAI integration within higher education. Course coordinators view GenAI with a mix of optimism and concern, recognizing its transformative potential while grappling with challenges to academic integrity and the traditional educational framework. The findings highlight the need for collective sensemaking, institutional support and adaptive strategies to harness GenAI effectively. The study’s limitations underscore the need for ongoing research to adapt to technological shifts and include diverse perspectives in the GenAI discourse.