Generative AI in Education: Cognitive Mechanisms, Learning Outcomes, and Evidence-Based Practice - Volume II

This Research Topic is the second volume of Research Topic "Generative AI in Education: Cognitive Mechanisms, Learning Outcomes, and Evidence-Based Practice". Please, see the first volume here.The rapid adoption of generative AI tools — including large language models such as ChatGPT, DeepSeek, and domain-specific tutoring systems — has outpaced the research evidence needed to guide their use. Educators and learners are making high-stakes decisions about when, how, and how much to rely on AI with little empirical grounding. This Research Topic calls for rigorous, theoretically grounded investigations into the cognitive and psychological mechanisms through which generative AI affects learning.A central concern is the quality and type of cognitive engagement that AI-assisted study produces. Does AI offloading reduce the effortful retrieval and spaced practice that are known to consolidate long-term memory, or can it be structured to complement these processes? How do AI tools interact with metacognitive monitoring — do learners accurately calibrate their own understanding when AI provides fluent, confident-seeming answers? These questions sit at the intersection of cognitive load theory, self-regulated learning, and educational technology, and demand the kind of controlled, longitudinal, and methodologically transparent research that has been largely absent from the early AI-in-education literature.This Research Topic welcomes empirical studies, systematic reviews, and theoretical papers that advance understanding of how generative AI shapes learning at the cognitive, motivational, and behavioural levels. Submissions should be grounded in established learning science frameworks — cognitive load theory, dual-process theory, self-determination theory, metacognitive theory — and should prioritise research designs capable of drawing meaningful conclusions about learning outcomes. Purely descriptive surveys of AI adoption rates, or studies that treat AI use frequency as a proxy for learning quality, fall outside scope.TOPICS COVERED· Cognitive load and cognitive offloading in generative AI-assisted study· AI use and effortful learning: substitution, complement, or bypass of retrieval practice?· Metacognitive monitoring accuracy and calibration when using AI tools· AI dependency, over-reliance, and consequences for critical thinking· Self-regulated learning strategies in AI-enhanced educational environments· AI literacy: skills learners need to use AI productively rather than dependently· Emotional and motivational responses to AI tools — anxiety, trust, and acceptance· AI-generated feedback and its effects on writing, language learning, and STEM outcomes· Teacher adaptation, professional identity, and technostress in the AI era· Methodological standards and research design quality in AI-in-education intervention studies· Longitudinal effects of AI use on academic performance and depth of processing· Equity and access: AI tools in under-resourced and diverse educational settingsARTICLE TYPES WELCOMEDOriginal Research | Systematic Review | Meta-Analysis | Conceptual Analysis | Methods Article Topic editors: Daniel H Robinson Source: https://www.frontiersin.org/research-topics/85559/generative-ai-in-education-cognitive-mechanisms-learning-outcomes-and-evidence-based-practice---volume-ii