Aug 2026· EDUCATIONE· pp. 1245-1256· 0 citations· 28 references
TL;DR
The evidence indicates that GenAI can support SRL by functioning as a learning tutor, assisting goal setting and planning, facilitating monitoring and self-evaluation, and improving learning efficiency, and higher-education institutions should combine GenAI access with pedagogical practices that strengthen planning, verification, reflection, critical thinking, and responsible AI literacy.
Abstract
opportunities for self-regulation and risks of academic dependency. This narrative review aimed to synthesize how GenAI may support self-regulated learning (SRL), how intensive or uncritical use may promote cognitive offloading, and which contextual conditions appear to distinguish these outcomes. Ten peer-reviewed articles published between 2022 and 2026 were identified through Publish or Perish using Google Scholar and synthesized thematically. The evidence indicates that GenAI can support SRL by functioning as a learning tutor, assisting goal setting and planning, facilitating monitoring and self-evaluation, and improving learning efficiency. At the same time, overreliance on GenAI is associated in the reviewed literature with cognitive offloading, academic dependency, reduced cognitive effort, weaker critical evaluation, and diminished learner autonomy. The synthesis proposes four contextual boundary conditions - purpose of AI use, SRL capability, AI literacy, and intensity of use - that may shape whether GenAI acts primarily as a learning scaffold or as a substitute for cognitive engagement. The framework is conceptual and has not been empirically validated. Higher-education institutions should therefore combine GenAI access with pedagogical practices that strengthen planning, verification, reflection, critical thinking, and responsible AI literacy.
It is argued that GenAI’s value cannot be judged by writing products alone but requires understanding the interplay among cognition, autonomy, and regulation.
Mohan Li· Journal of English language...· 0 citations
Asynchronous online courses require substantial learner independence, yet structural flexibility does not necessarily lead to self-determined learning. This study examines the transition from self-regulated learning (SRL) to self-determined learning and explores how generative artificial intelligence (GenAI) may scaffold heutagogical development.
We conducted a secondary qualitative analysis of 304 SRL-coded meaning units from 75 preservice teachers enrolled in an asynchronous course. The data were coded using a 0–3 heutagogical-gap scale. The resulting patterns were then translated into a GenAI prompt repository, presented as an empirically grounded design output rather than a tested intervention.
The heutagogical gap was defined as the developmental space between learners' capacity to regulate learning within a predefined structure and their capacity to define learning goals, pathways, products, and evaluative criteria more independently. Overall, 86.8% of meaning units reflected some level of gap, while 37.5% showed substantive or critical gaps. Patterns included dependence on external feedback, limited knowledge transformation, help-seeking characterized by isolation or dependence, and coping without explicit emotional regulation. The findings informed an AI-Enhanced Heutagogical Cycle (AIHC) aligning SRL dimensions, heutagogical transitions, and pedagogically constrained GenAI roles.
The study distinguishes effective regulation within a given structure from self-determined learning. It proposes GenAI as a differentiated scaffold for expanding learner agency rather than substituting for learners' cognitive effort, judgment, and responsibility. The framework and prompt repository require validation in future intervention studies.
Liat Eyal, Inbal Koloshi-Minsker· Frontiers in Education· 0 citations
Abstract Self-regulated learning (SRL) has long been central to language education, encompassing how learners plan, monitor, and evaluate their learning in pursuit of meaningful goals. The growing integration of artificial intelligence (AI) into language learning environments, however, is reshaping these regulatory processes by distributing aspects of planning, feedback, monitoring, and strategy use across human and technological systems. This article introduces the Hybrid-Regulated Learning (HRL) Framework, a conceptual model describing how learners co-regulate language learning with AI across eight stages: motivation, needs analysis, goal setting, planning, task engagement, monitoring, reflection, and integration. The framework links each stage to specific AI roles (tutor, evaluator, recommender, simulator, and tool), the critical literacies required to engage with them, and the artefacts through which regulatory processes become visible and assessable. Drawing on research in self-regulated learning, AI-supported learning, and language education, the article argues that effective regulation in AI-rich environments depends not only on learners’ strategic abilities but also on their capacity to critically manage and interpret AI support. The HRL framework offers a foundation for understanding and designing language learning environments in which regulatory control is negotiated across human and artificial agents.
H. Reinders· Digital Studies in Language...· 0 citations
Self-regulated learning (SRL) is a topic of considerable scholarly interest because of its high correlation with autonomous learning, academic achievement, metacognition, motivation, and emotion regulation. This narrative review examines theoretical, empirical and methodological developments in the field of research concerning self-regulated learning published between 2015 and 2025 on senior secondary students. This paper aims to fill a research gap related to the analysis of the issue in question in senior secondary educational settings. Articles were identified through Scopus, ERIC, Web of Science, and Google Scholar databases. Following a screening process, 40 articles were selected for interpretive synthesis after meeting the inclusion criteria. Three SRL models, namely the cyclical phases model of Zimmerman, the COPES model of Winne and Hadwin and the metacognitive and affective model of self-regulated learning (MASRL) by Efklides, are reviewed. The findings demonstrate that SRL among senior secondary students is determined by the degree of congruence between motivational beliefs, metacognitive abilities, emotion regulation skills, teacher scaffolding, peer interactions, digital learning environment, and context of classroom teaching. An increasing tendency to use mixed methods, trace-based methods, think aloud approach, reflective diaries, and technology-based measurement of SRL. Overall, the review indicates that late adolescence is certainly a distinctive developmental phase with three major characteristics: examination pressure, identity formation, and emotional instability as well as readiness to change. The current SRL review provides a developmentally informed perspective on self-regulated learning and offers important recommendations for curriculum planning, assessing SRL in classrooms, and teacher education.
Shubhangi Taneja, Kanchan Khatreja· Asian Journal of Interdiscip...· 0 citations
This study examines how university students' conceptions of learning shape their motivation, self-regulation and information processing throughout different academic stages, showing how these factors interact and transform as a function of academic progression. The objective, therefore, is to provide evidence on the mechanisms that sustain self-regulated learning and its impact on retention and academic success during higher education.
A formative structural model was developed and validated using Partial Least Squares Path Modeling (PLS-PM), with 5,000 bootstrap resamples from a large and diverse sample of 1,630 Colombian university students. This methodology helped to capture the complex dynamics among conceptions, motivation, self-regulation and information processing as well as to analyze variations across three cohorts at different stages of academic progression.
The results indicate that in the early semesters, learning conceptions drive motivation and indirectly influence information processing. In the intermediate stages, self-regulation emerges as a key mediator. By the later semesters, metacognitive self-regulation becomes the primary determinant of deep processing, overshadowing the direct influence of conceptions. These findings confirm the dynamic and evolving nature of learning patterns throughout university education and highlight the development of self-directed and deep learning.
This study has some limitations that should be acknowledged. First, the cross-sectional design limits the possibility of establishing causal relationships between the variables, since the observed associations reflect patterns at a single point. Future research should adopt longitudinal designs that better capture the evolution of learning processes throughout academic progression. Second, the use of non-probability convenience sampling may limit the generalizability of the findings. Although the sample was large and diverse, it is recommended that future studies replicate the model in different institutional and cultural contexts. Finally, the use of self-report measures may introduce potential response biases.
The findings provide explicit guidelines for curriculum design and university policies aimed at promoting constructivist principles and intrinsic motivation through active methodologies in the early stages of education. In more advanced stages, it is essential to enhance metacognitive regulation, autonomy and reflective practices. The implementation of these strategies can result in enhanced academic performance, increased student retention rates and the development of self-reliant graduates equipped for lifelong learning.
This study provides a novel contribution by proposing a dynamic and developmental model of learning patterns, demonstrating how the relationships among conceptions, motivation, self-regulation and information processing reorganize across academic progression.
Hedilberto Granados López, Manuela Giraldo Ospina, Felipe Antonio Gallego López et al.· Journal of Applied Research...· 0 citations
Generative artificial intelligence (GenAI) is rapidly changing professional language education, yet the behavioral and cognitive mechanisms through which learners engage with GenAI-mediated language tasks remain insufficiently synthesized. This critical integrative review reframes translation and interpreting education as a high-cognitive-load case of professional language learning in which students must evaluate AI output, regulate feedback use, and remain accountable for meaning across languages. A documented evidence-selection workflow was applied to an author-curated bibliographic corpus and a supplementary source-expansion corpus comprising 690 potentially relevant records, from which 21 core studies were selected for focused synthesis. The included studies directly addressed GenAI, translation/interpreting education, AI-generated feedback, post-editing, learner revision, interpreter assessment, or AI literacy. The synthesis is theoretically grounded in cognitive load theory, self-regulated learning, feedback literacy, trust in automation, and social-cognitive accounts of agency. Across the reviewed evidence, learners’ engagement with GenAI involves cognitive load redistribution, trust calibration, feedback uptake, metacognitive monitoring, affective responses, and behavioral revision decisions. The review proposes a behavioral model that links technological mediation, cognitive appraisal, self-regulated behavioral engagement, pedagogical regulation, observable process evidence, and professional agency. We argue that the central educational challenge is not whether GenAI improves single-task language performance, but how learners develop calibrated trust, critical judgment, self-regulated feedback use, and professional agency in human–AI language-learning environments. The framework may also inform adjacent AI-mediated language-learning contexts where learners must evaluate feedback, revise language, and justify communicative choices.
