Francis Arthur

Background: The educational metaverse presents emerging opportunities for transforming university learning, yet students' willingness to adopt such environments may depend on psychological, technological and contextual factors. This study examined the predictors of university students' behavioural intentions to use the educational metaverse in Ghana, with particular attention to mediating and moderating mechanisms and gender differences. Methods: An analytical survey design was employed, and data were collected from 917 university students selected through purposive sampling. Partial Least Squares Structural Equation Modelling (PLS-SEM) was used to examine the direct, mediating and moderating relationships among the study variables. Results: Perceived observability, compatibility, cyber risk, personal innovativeness, trust and perceived usefulness were significantly associated with behavioural intention, whereas perceived complexity was negatively associated with behavioural intention. Perceived ease of use and enjoyment were not directly associated with behavioural intention. Trust significantly mediated the association between perceived usefulness and behavioural intention, while observability mediated the association between personal innovativeness and behavioural intention. However, enjoyment did not significantly mediate the association between perceived ease of use and behavioural intention. Gender significantly moderated the associations of compatibility, personal innovativeness, trust and perceived usefulness with behavioural intention, indicating differences in adoption patterns between male and female students. Cyber risk also moderated the association between trust and behavioural intention, weakening the positive relationship between the two constructs. Conclusions: The findings suggest that educational metaverse adoption among university students is shaped by a combination of technological perceptions, personal innovativeness, trust and cyber risk, with several relationships operating through mediating and moderating mechanisms. The study extends educational metaverse adoption research by demonstrating the importance of examining these mechanisms rather than relying solely on direct predictors of behavioural intention. Universities and educational technology developers should therefore consider trust, cybersecurity and gender-related differences when designing and implementing metaverse-based learning environments.

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.

This chapter examines the integration of artificial intelligence (AI) literacy into higher education curricula using the DARE (Discern, Analyse, Reflect, Evaluate) framework. Drawing on Critical Digital Pedagogy and Transformative Learning Theory, it conceptualises AI literacy as a multidimensional competence encompassing understanding, application, evaluation, and ethical responsibility. The paper highlights gaps in institutional preparedness, assessment practices, and ethical governance in the era of generative AI. It proposes practical instructional, assessment, and policy strategies to foster critical engagement, evaluative judgement, and responsible AI use. The study argues that embedding AI literacy across curricula is essential for developing students' critical thinking, academic integrity, and ethical reasoning in digitally mediated learning environments.

The rapid integration of artificial intelligence (AI) into higher education is transforming how students access information, solve problems, and engage with learning. While AI offers significant benefits, concerns have emerged regarding cognitive dependency, reduced critical thinking, and diminished intellectual autonomy. This chapter examines the implications of AI-mediated learning for economics students, whose academic development depends on analytical reasoning, model-based thinking, and critical evaluation. Drawing on Cognitive Offloading Theory, Dual-Process Theory, Self-Regulated Learning Theory, Automation Bias, and Neuroplasticity, the chapter proposes the AI-Cognitive Dependency and Growth Framework (AI-CDGF). The framework explains how AI engagement can lead to either cognitive dependency or cognitive growth, providing guidance for responsible AI-supported learning in higher education.

Artificial intelligence (AI) is increasingly shaping learning processes in higher education, raising concerns about cognitive dependency and the erosion of critical thinking. These concerns are particularly significant in economics education, where analytical reasoning, judgement, and problem-solving are essential. This chapter conceptually examines how human-centred AI design can support cognitive development rather than replace it. Drawing on Human-Centred AI, Cognitive Load Theory, Self-Regulated Learning Theory, Dual-Process Theory, and insights from neuroscience, the chapter proposes the Human-Centered AI for Economic Thinking (HCAI-ET) Framework. The framework offers design and pedagogical principles that promote cognitive engagement, metacognition, intellectual autonomy, and responsible AI integration in economics education.

This chapter introduces the Technologically Mediated Academic Altruism (TMAA) Framework to examine how digital technologies are reshaping mentorship, knowledge sharing, and scholarly support in higher education. Situated within the broader discourse of academic altruism and the experiences of Black scholars, the chapter explores how technology can both facilitate and challenge altruistic engagement. This chapter conceptualises digital altruism as the intentional use of technology to expand access, democratise knowledge, and foster inclusive scholarly communities. The TMAA Framework comprises five dimensions: digital access equity, virtual mentorship ecosystems, knowledge democratisation, AI-augmented altruism, and techno-emotional sustainability. The chapter concludes with practical strategies for promoting ethical, inclusive, and sustainable technology-enabled academic altruism.

This chapter advances the Synergistic Collaborative Altruism Framework (SCAF) as an integrative model for understanding how collaborative research and innovation function as expressions of academic altruism in knowledge production. Grounded in social capital theory, innovation systems, and collaborative learning, the framework conceptualizes collaboration as a relational and ethical process shaped by trust, reciprocity, and shared scholarly purpose. SCAF comprises five interrelated dimensions: relational trust capital, co-creation synergy, inclusive innovation ecosystems, mentorship diffusion, and collaborative resilience. These dimensions explain how equitable collaboration enhances research productivity, innovation, scholarly impact, and long-term academic development. The framework offers a principled pathway for advancing research excellence, recognition justice, and sustainable knowledge communities in higher education.

This chapter advances the Afro-Altruistic Compassionate Pedagogy (AACP) framework as an integrative model for conceptualising and operationalising teaching with compassion within the broader discourse of academic altruism. Grounded in culturally responsive pedagogy, the ethics of care, and Afrocentric philosophies such as Ubuntu, the framework articulates five interrelated dimensions: empathetic engagement, relational accountability, culturally sustaining instruction, emancipatory mentorship, and reflective resilience. These dimensions enable educators to foster belonging, affirm identity, and support scholarly development while navigating structural inequities. By reframing compassionate teaching as both a pedagogical practice and a form of scholarly giving, the AACP framework provides a principled pathway for advancing equity, academic excellence, and sustainable scholarly communities in higher education.