Individual differences predict applied competence, but the routes by which they do so are rarely modelled. Drawing on investment and aptitude theory for cognitive resources and on appraisal theory for affective orientations, we propose an appraisal-correspondence principle: the closer an individual-difference variable stands to the outcome in its level of self-appraisal, the more directly it should act; the further away it is, the more mediation should be required. We tested the principle on pre-service teachers’ competence to teach with generative artificial intelligence (AI-TPACK), using a latent-variable model on cross-sectional data from 164 pre-service primary-school teachers in Türkiye. AI readiness (cognitive) and AI competence anxiety (affective) served as antecedents and generative AI literacy and AI self-efficacy as mediators. Fit was good, χ2(139) = 181.44, CFI = 0.978, TLI = 0.973, RMSEA = 0.045, and SRMR = 0.061, with 45.8% of AI-TPACK variance explained. Readiness produced a strong total effect (β = 0.600, 95% CI [0.444, 0.738]), half of it indirect; model comparison indicated that generative AI literacy, rather than self-efficacy, carries the mediated share, although the literacy → AI-TPACK path itself was only partially supported. Competence anxiety predicted neither mediator, and its direct path to AI-TPACK was small (β = −0.105, CI [−0.266, 0.059]) and not significant in any of 16 pre-specified data-handling variants. The cognitive road is robust; the affective road, as predicted by the principle, bypasses the mediators but leaves only a faint and uncertain trace. Full scalar invariance held across year of study and use frequency. We state the principle as a hypothesis for performance-based tests rather than as an established mechanism.
As generative artificial intelligence (GenAI) becomes increasingly integrated into second language (L2) learning, there is a growing need for reliable instruments to assess learners' cognitive evaluation of GenAI-mediated interactive learning experiences. This study developed and validated the GenAI L2 Interactive Learning Experience Scale (AI-L2-ILES) among 1130 Chinese university students. Drawing on an interactive experience perspective and adapting it to L2 learning, the study conceptualized learners' cognitive evaluation of GenAI interaction as comprising five dimensions: Effectiveness, Sociability, Novelty, Flow, and Seamlessness. Exploratory and confirmatory factor analyses supported this five-factor structure, with satisfactory evidence of reliability, validity, and measurement invariance across gender and academic disciplines. Network analysis further revealed that Flow was the most central dimension, Sociability showed the strongest association with Effectiveness, and Novelty occupied a relatively peripheral position. The network structure was stable across academic disciplines. Overall, the AI-L2-ILES provides a psychometrically sound measure of learners' cognitive evaluation of GenAI-mediated interactive L2 learning experiences and captures five distinct but interconnected dimensions of this emerging construct. The scale offers a useful tool for examining how L2 learners cognitively evaluate their interactions with GenAI.
Understanding the cognitive architectures that enable learners to process feedback and self-regulate is fundamental to fostering intelligent second language (L2) learning, particularly as AI-based pedagogies become prevalent. Direct written corrective feedback, while common in beginner L2 writing, often triggers only surface-level processing. This study investigates whether written languaging (WL), a metacognitive activity where learners explain language problems in writing, can deepen feedback processing, thereby activating a more robust cognitive architecture for error correction. Using a within-subjects crossover design with 15 beginner Chinese-as-a-second-language (CSL) learners from a UK secondary school, we compared the immediate and transfer effects of direct feedback with WL versus direct feedback only. Results showed that the WL condition significantly reduced errors per 100 characters (Z = -2.556, p = .011, r = 0.66), with 86.7% of participants showing improvement, indicating enhanced cognitive regulation at the local level. However, the effect was hierarchical, with no significant impact on clause-level accuracy. General Certificate of Secondary Education (GCSE) writing scores improved significantly from pre-to-post-test (Z = -3.342, p = .001, r = 0.89), demonstrating transfer to subsequent performance. Qualitative analyses revealed that learners' attention focused predominantly on characters (48.4%) and vocabulary (29.8%), with engagement moderated by proficiency and motivation. This study provides empirical evidence for a cognitive architecture of feedback processing, wherein WL functions as a metacognitive amplifier. This architecture is operationalised as a three-stage processing sequence noticing, hypothesis-testing, and metalinguistic reflection, with writing accuracy and WL texts serving as observable proxies for the underlying cognitive processes. Specifically, our findings reveal that WL's facilitative effects are hierarchical, strongest at the level of local error reduction and not yet extending to clause-level syntactic accuracy, and are moderated by individual differences in proficiency and motivation. These empirical insights offer a cognitive-psychological foundation that could inform the future design of adaptive feedback systems; however, as this study did not involve any AI system, these implications are theoretical and await empirical validation in AI-mediated learning environments.
Developing pre-service mathematics teachers' ability to understand students' mathematical thinking and anticipate students' difficulties remains a challenge in teacher education. This exploratory mixed-methods study involved 21 pre-service mathematics teachers in a six-week AI-supported mathematical task design module. The study combined pre-post assessment, fuzzy-set Qualitative Comparative Analysis (fsQCA), and qualitative analysis of participants' AI interactions and reflections. The key findings were as follows: (1) Participants showed significantly higher post-test scores in overall diagnostic thinking and in perception, interpretation, and judgement than at pre-test. (2) The configurational analysis identified a comparatively stable pattern associated with high post-intervention diagnostic thinking, combining lower initial diagnostic thinking with high AI acceptance and high prompting knowledge density. (3) Qualitative evidence further showed that AI served as an interactive resource for exploring mathematical and pedagogical considerations, evaluating generated suggestions, and revising task designs. Exploratory comparisons suggested that AI use could range from broader knowledge-oriented support to more selective verification and refinement. The findings highlight the heterogeneous nature of AI-supported professional learning and underscore the importance of critical evaluation and professional agency in teacher-AI collaboration.
Vocational rehabilitation (VR) plays an important role in supporting successful school-to-work transitions for individuals with intellectual disabilities (IDs). However, the organizational conditions that enable effective implementation of VR practices remain insufficiently understood. Because participation outcomes among individuals with ID are influenced by interactions between intellectual functioning, adaptive abilities, and environmental supports, examining organizational implementation capacity provides an important systems-level perspective. This study examined professionals' perceptions of organizational implementation capacity for VR during secondary transition in Saudi Arabia and explored differences according to institutional and professional characteristics. A cross-sectional survey was conducted among 89 professionals working in government secondary school programs and specialized education centers in Al-Kharj, Saudi Arabia. Participants completed the Vocational Rehabilitation Implementation Capacity Measure (VRICM), a context-specific, theoretically informed measure developed for this study. The VRICM examines four theoretically derived areas: Support and Related Services, Vocational Preparation Services, Collaborative Planning, and Workplace-Based Training. We conducted descriptive statistics and nonparametric group comparisons. Overall perceived organizational implementation capacity was moderate (M = 3.25, SD = 0.95). Support and Related Services and Vocational Preparation Services received higher ratings than Collaborative Planning and Workplace-Based Training. Differences were observed according to institutional sector and professional experience. Findings provide preliminary evidence regarding organizational factors supporting VR implementation within a Saudi Arabian secondary-city context. Further psychometric evaluation of the VRICM, including structural validity assessment, is required before broader application.
