Adaptive AI-generated feedback (AIF) is increasingly integrated into higher education; however, its cognitive and psychological implications remain insufficiently understood, particularly in engineering education. Grounded in feedback intervention theory and emotion regulation theory, this study developed and tested an integrative model examining the perceived relationships between AIF, cognitive flexibility (CF), and academic psychological safety (APS), including mediating and moderating patterns.
A cross-sectional survey was conducted among 471 engineering students. Data were analyzed using partial least squares structural equation modeling. The measurement model demonstrated satisfactory reliability and validity, with factor loadings above 0.70, AVE values exceeding 0.50, composite reliability values ranging from 0.908 to 0.936, Cronbach’s alpha values ranging from 0.887 to 0.921, and HTMT values below 0.85. The structural model showed moderate explanatory power for CF (
R
2
= 0.443) and APS (
R
2
= 0.626), with adequate predictive relevance and no multicollinearity concerns.
AIF was significantly associated with CF (
β
= 0.580,
p
< 0.001) and APS (
β
= 0.352,
p
< 0.001). CF was significantly associated with APS and showed a significant indirect association between AIF and APS (indirect effect
β
= 0.257,
p
< 0.001). Significant interaction effects indicated that ESAF was associated with a stronger relationship between AIF and CF, whereas MSAF was associated with a stronger relationship between CF and APS.
Perceived AIF was associated with APS directly and indirectly through CF, while emotional and mindfulness-supportive features were associated with a greater strength in these relationships. Given the study’s cross-sectional design, the findings indicate associations rather than causal effects. This study advances the current understanding of cognitive and affective patterns in AI-supported engineering education and highlights the importance of integrating adaptive, emotional, and reflective features into AI feedback systems.
Amani BinJwair· Frontiers in Psychology· 0 citations
Obstacles remain in the form of inconsistent outcomes of AI applications in English as a foreign language (EFL) speaking instruction, particularly in Saudi contexts, where language anxiety and a sense of insecurity prevent learners from becoming more empowered through technological exposure. In this study, a mediator variable, self-efficacy, was postulated in the interaction between AI dialogic scaffolding, language anxiety, and speaking confidence. The current research employed a quantitative cross-sectional design, with data analyzed using partial least squares structural equation modelling (PLS-SEM) among 243 Saudi students studying at EFL universities. The findings established that AI-dialogic scaffolding had positive effects on speaking confidence and self-efficacy, and negative effects on language anxiety were very high. These relationships were partially mediated by self-efficacy, which is a critical psychological mediator. The results present a new model that incorporates technological and affective factors, offering meaningful theoretical and practical implications for the creation of AI-based language-learning contexts that facilitate psychological stability and skills acquisition. The originality of this research lies in empirically verifying complex mediating pathways in the context of Saudi EFL and extending the models of direct effects that most other researchers have previously explored. This study offers a rational framework that combines technological, cognitive, and affective features, thus contributing to the theoretical knowledge of both applied linguistics and educational technology. It extends beyond the examination of immediate impacts and shapes models of how scaffolding procedures align with the wavy paths through which they exert their influence. The studies suggest using an evidence-based approach in the Saudi context, and researchers should focus on enhancing self-efficacy to break anxiety and lack of self-confidence. Finally, the study illuminates that the true potential of AI in ed-tech is not just its ability to copy an interaction; rather, its capacity to be organized in a way that instills psychological strength and confidence in the messages it delivers. Thus, the study provides a distinct path for the evolution of a better, more holistic, and learner-focused digital language-learning environment.
S. Alshraah, Amani BinJwair, A. Mufleh et al.· Electronic Journal of e-Lear...· 0 citations
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