npj Science of Learning最新文献

Motivation is a key driver of learning. Prior work on motivation has mostly focused on conventional learning contexts that did not necessarily involve AI. Hence, little is known about students' motivation to learn AI. This study examined the structure of students' AI motivational system using self-determination theory as the theoretical framework. Self-determination theory posits that there are qualitatively distinct types of motivation, including intrinsic motivation, identified regulation, introjected regulation, external regulation, and amotivation. Students' motivation, in turn, is strongly shaped by whether their basic psychological needs for competence, autonomy, and relatedness are satisfied. We used network analysis to explore the structure of students' AI motivation. Participants included 1465 students from 47 universities. Introjected regulation was central to the AI motivational system but intrinsic motivation was less central. This meant that many students learned AI primarily out of guilt or shame and not because of personal enjoyment. Furthermore, competence satisfaction seemed more important than autonomy and relatedness satisfaction in AI-enriched learning environments. Hence, key practical implications include the need to have clear goals and standards as well as to build students' competence in using AI tools. This study enriches the AI education literature by focusing on students' motivational systems and suggesting ways to cultivate better engagement with AI.

Sleep is crucial for memory consolidation, underpinning effective learning. Targeted memory reactivation (TMR) can strengthen neural representations by re-engaging learning circuits during sleep. However, TMR protocols overlook individual differences in learning capacity and memory trace strength, limiting efficacy for difficult-to-recall memories. Here, we present a personalized TMR protocol that adjusts stimulation frequency based on individual retrieval performance and task difficulty during a word-pair memory task. In an experiment comparing personalized TMR, TMR, and control groups, the personalized protocol significantly reduced memory decay and improved error correction under challenging recall. Electroencephalogram (EEG) analyses revealed enhanced synchronization of slow waves and spindles, with a significant positive correlation between behavioral and EEG features for challenging memories. Multivariate classification identified distinct neural signatures linked to the personalized approach, highlighting its ability to target memory-specific circuits. These findings provide novel insights into sleep-dependent memory consolidation and support personalized TMR interventions to optimize learning outcomes.

While evidence has accumulated to support the argument of shared computational mechanisms underlying language comprehension between humans and large language models (LLMs), few studies have examined this argument beyond native-speaker populations. This study examines whether and how alignment between LLMs and human brains captures the homogeneity and heterogeneity in both first-language (L1) and second-language (L2) readers. We recorded brain responses of L1 and L2 English readers of texts and assessed reading performance against individual difference factors. At the group level, the two groups displayed comparable model-brain alignment in widespread regions, with similar unique contributions from contextual embeddings. At the individual level, multiple regression models revealed the effects of linguistic abilities on alignment for both groups, but effects of attentional ability and language dominance status for L2 readers only. These findings provide evidence that LLMs serve as cognitively plausible models in characterizing homogeneity and heterogeneity in reading across human populations.

Many students in STEM experience decreased performance due to anxiety, namely math and science anxiety. However, spatial skills are correlated with better STEM outcomes. Our research addressed a previous gap in the literature, investigating if STEM anxiety or spatial experiences have a stronger relationship with STEM outcomes. In this online study, we explored which factors were related to STEM outcomes in a sample of deaf, hard of hearing (DHH), and hearing adults (N = 115) who had experience with American Sign Language, which has been associated with improved spatial skills. Participants completed a mental rotation task, and self-reported interest in STEM, and anxiety. Results showed math and science anxiety were significant predictors of mental rotation performance and interest in studying STEM, even when accounting for other spatial factors. For DHH and hearing people alike, math and science anxiety are important factors that must be addressed to encourage STEM success.

Recognising a need to investigate the concerns and barriers to the acceptance of artificial intelligence (AI) in education, this study explores the acceptability of different AI applications in education from a multi-stakeholder perspective, including students, teachers, and parents. Acknowledging the transformative potential of AI, it addresses concerns related to data privacy, AI agency, transparency, explainability, and ethical deployment of AI. Using a vignette methodology, participants were presented with four scenarios where AI agency, transparency, explainability, and privacy were manipulated. After each scenario, participants completed a survey that captured their perceptions of AI's global utility, individual usefulness, justice, confidence, risk, and intention to use each scenario's AI if it was available. The data collection, comprising a final sample of 1198 participants, focused on individual responses to four AI use cases. A mediation analysis of the data indicated that acceptance and trust in AI vary significantly across stakeholder groups and AI applications.

Studies have shown that explicit strategies make a significant contribution to visuomotor adaptation. However, little attention has been given to potential unconscious cognitive biases in these strategies, despite that they involve a sequence of cognitive decision-making processes. To reveal the possible cultural biases involved in motor learning, we compared Norwegian and Japanese participants in a visuomotor adaptation task using a verbal report paradigm. The results showed that Japanese participants aimed at locations more deviant from the target to account for rotated visual feedback. Additionally, a greater proportion of Japanese participants changed their aiming direction more frequently than Norwegian participants, even after successfully hitting the target. However, both groups showed similar behavioral performance, with comparable reaching accuracy and aftereffect amplitudes. These results suggest that the explicit component, which is estimated based on verbal reports, includes cognitive biases. The present study challenges the assumption of universality of motor learning among cultures.

Creativity is a key 21st-century skill and a consistent predictor of academic learning outcomes. Despite decades of research on creativity and learning, little is known about the cognitive mechanisms underlying their relationship. In two studies, we examined whether creativity supports associative learning through associative thinking-the ability to generate novel word associations-an ability central to creativity which has not been previously tied to associative learning. In Study 1, we found that students who generated more novel word associations learned more words on a foreign language learning test 24 h later. In Study 2, we replicated and extended the effect to naturalistic creativity tasks (i.e., writing short stories and sketching line drawings), finding associative thinking mediated the relationship between creativity and associative learning. Importantly, both studies controlled for general intelligence. Our findings suggest that creativity's contribution to learning operates partly through a shared cognitive capacity for making new connections.

Humans implicitly pick up on probabilities of stimuli and events, yet it remains unclear how statistical learning builds expectations that affect perception. Across 29 experiments, we examine the influence of task-irrelevant distributions-defined across acoustic frequency-on both tone detection in noise and tone duration judgments. The shape and range of the frequency distributions impact suppression and enhancement effects, as does a given tone's position within the range. Perception adapts quickly to changing distributions, but past distributions influence future judgments. Massed exposure to a single frequency impacts perception along a range of subsequently encountered frequencies. A novel bias emerges as well: lower frequencies are perceived as longer and higher ones as shorter. Probability-driven learning dynamically shapes perception, driven by interacting influences of sensory processing, distributional learning, and selective attention that sculpt a gain function involving modest enhancement of more-likely stimuli, and robust suppression of less-likely stimuli.

Large Language Models (LLMs) present a radically new paradigm for the study of information foraging behavior. We study how LLM technology is used for pedagogical content creation by a sample of 25 participants in a doctoral-level Artificial Intelligence (AI) in Education course, and the role of computational-thinking skills in shaping their foraging behavior. We used editable prompt templates and socially-sourced keywords to structure their prompt-crafting process. This design influenced participants' behaviors towards exploration (to generate novel information landscapes) and exploitation (to dive into specific content). Findings suggest that exploration facilitates navigation of semantically diverse information, especially when influenced by social cues. In contrast, exploitation narrows the focus to using AI-generated content. Participants also completed a Computational Thinking survey: exploratory analyses suggest that trait cooperativity encourages exploitation of AI content, while trait critical thinking moderates reliance on participants' own interests. We discuss implications for future use of LLM-driven educational tools.

This study investigates crossmodal fear generalization, testing whether conditioned fear spreads between different sensory modalities. Participants in the unimodal group were presented with visual stimuli-images of a sparrow (CS+) and a laptop (CS-)-while the crossmodal group received auditory stimuli-sparrow calls (CS+) and keyboard typing sounds (CS-). During the generalization phase, both groups were presented with conceptually similar visual stimuli (GSs) with varying similarity to the CS+ (e.g. high: Pigeon, moderate: Duck, low: Goat). Measures included US expectancy ratings, skin conductance responses (SCR), and functional near-infrared spectroscopy (fNIRS). Results showed successful fear acquisition in both groups, with significantly higher US expectancy ratings, SCR, and mPFC HbO activity for CS+ compared to CS-. Both groups exhibited a gradient effect during the generalization phase, with GSs that were more perceptually similar to the CS+ eliciting higher US expectancy ratings. These findings support crossmodal fear generalization and offer new insights into the overgeneralization of fear in anxiety disorders.