Ernest Opoku

Purpose: Teaching is widely recognised as a psychologically demanding profession, with sustained exposure to work‐related stressors posing risks to teachers' mental well‐being and professional functioning. Drawing on the ‘Job Demands‐Resources model and Self‐Determination Theory’, this study examined how teachers' psychological capital (PsyCap), a core positive psychological resource, relates to ‘professional commitment (PC)’ and whether ‘job satisfaction (JS) and subjective well‐being (SWB)’ mediate this relationship. Method: A ‘descriptive cross‐sectional survey design’ was employed, with data collected from 225 basic school teachers using a simple random sampling technique. ‘Partial least squares structural equation modelling (PLS‐SEM)’ was utilised to test the proposed direct and mediating relationships among the study variables. Finding: The results indicated that PsyCap had significant positive effects on ‘teachers' job satisfaction, subjective well‐being, and professional commitment’. Subjective well‐being partially mediated the relationship between ‘PsyCap and professional commitment’, highlighting well‐being as a key psychological mechanism linking personal psychological resources to work‐related behavioural outcomes. In contrast, ‘job satisfaction neither predicted professional commitment nor mediated the PsyCap‐commitment’ relationship. Conclusion: Psychological capital enhances teachers' professional commitment primarily through subjective well‐being rather than job satisfaction. Educational authorities should strengthen teachers' psychological capital and well‐being to sustain commitment and improve professional outcomes in demanding educational contexts.

The rapid integration of generative artificial intelligence in higher education has intensified interest in understanding how students' motivational beliefs shape their engagement with AI‐supported learning. This study examines how “expectancy beliefs and task value” dimensions, namely “attainment, utility, interest, and cost”, influence Economics students' use of generative AI (GenAI) and how such use supports self‐regulated learning. Survey data were collected from 416 university Economics students in Ghana and analysed using “Partial Least Squares Structural Equation Modelling”. The findings show that expectancy, utility, interest, and cost significantly predict students' use of GenAI, whereas attainment value does not exert a significant effect. GenAI use, in turn, strongly predicts self‐regulated learning, indicating that sustained engagement with AI tools enhances students' capacity to plan, monitor, and regulate their learning processes. Mediation analysis further demonstrates that generative AI use transmits the effects of expectancy, utility, interest, and cost on self‐regulated learning, confirming its central role in translating motivational beliefs into effective learning behaviours. By integrating expectancy‐value theory with self‐regulated learning in an AI‐enabled context, this study advances understanding of the motivational foundations of responsible and purposeful AI use in higher education. The findings offer practical implications for curriculum design and instructional practices aimed at strengthening students' motivation and fostering autonomous, reflective, and productive engagement with GenAI tools.

As artificial intelligence (AI) is transforming global education, it is crucial to understand the psychological barriers to its integration, given the low adoption rate among teachers. This study explores AI anxiety, as conceptualised through the Technophobia Framework, among basic school teachers in Ghana. A descriptive cross-sectional design was employed to collect data from 319 participants, with the sample size determined by an a priori power analysis. Confirmatory factor analysis (CFA) validated a four-dimensional scale measuring AI learning, job replacement, sociotechnical blindness, and configuration anxiety, demonstrating high reliability (α = .942; ω = .969). The descriptive statistics revealed moderate overall AI anxiety, while a one-way MANOVA identified a significant gender difference in sociotechnical blindness anxiety specifically, with male teachers reporting higher levels of anxiety. These findings emphasise the need for targeted professional development and gender-sensitive support strategies to address AI-related concerns and facilitate the successful integration of technology into Ghana’s educational landscape.