Developing highly active electrocatalysts for the oxygen evolution reaction is a pivotal challenge in sustainable water electrolysis. Herein, we report a novel in situ oxidative phase-restructuring strategy to fabricate oxygen vacancy-rich NiFe2O4 (NFO) directly on nickel foam. Distinct from conventional hydrothermal methods that typically yield thermodynamically stable crystals with limited intrinsic defects, our unique one-pot process involves the formation of a reduced metallic intermediate. The subsequent drastic phase transformation from this metallic state to a spinel oxide thermodynamically enforces the generation of abundant oxygen vacancies to relieve lattice stress, resulting in unique polycrystalline nanocrystal assemblies (NFO-1). Electrochemical evaluations reveal that NFO-1 significantly outperforms its thermodynamically equilibrated counterpart (NFO-2), exhibiting a low overpotential of 330 mV at 20 mA cm−2 and a remarkable mass activity of 6.78 A g−1. This superior performance is primarily attributed to intrinsic oxygen vacancies generated during the oxidative phase evolution, which optimize the active-site electronic structure and enhance charge–transfer kinetics. Furthermore, the catalyst demonstrates excellent durability over 1200 cycles. This work highlights oxidative phase restructuring as a powerful pathway to engineer intrinsic defects for high-efficiency energy-conversion applications.

The metaverse and artificial intelligence (AI) are increasingly intersecting in educational contexts, yet limited empirical research has examined how generative AI avatars function within socially interactive virtual environments. This study investigates the deployment of generative AI avatars within a socio-educational metaverse environment. Using a developmental evaluation approach, data were collected through interviews with seven institutional stakeholders, teacher-generated reflections, internal documentation, embedded user feedback captured through in-platform reporting tools, and longitudinal field memos across an iterative deployment cycle. Findings indicate that the transition from scripted NPCs to generative AI avatars recalibrated users’ attribution of agency, intensified dialogic unpredictability, and elevated social realism beyond visual fidelity. Voice-mediated interaction emerged as a threshold mechanism for co-presence, while algorithmic improvisation exposed tensions between pedagogical intent and stochastic response generation. The deployment further revealed affective frictions, expectation misalignments, and the mediating role of AI literacy in shaping trust, participation, and interpretive coherence. Overall, the study advances a sociotechnical understanding of AI avatars as co-constructors of meaning and interaction, offering implications for the design, implementation, and governance of future AI-enhanced metaverse learning environments.

As artificial intelligence (AI) continues to permeate the education sector, the question is no longer about what it can do but what drives its successful adoption. Despite a growing body of literature on AI in education, research specifically addressing its adoption in developing countries is still lacking, even though the use of AI is potentially even more widely adopted there than in other places. Thus, we examined the critical factors influencing AI adoption in Ghanaian higher education institutions. Anchored in the diffusion of innovation (DoI) Theory and the unified theory of acceptance and use of technology 2 (UTAUT2), we specifically investigated the role of interoperability, relative advantage, pedagogical alignment, accessibility and affordability, and ethical considerations in shaping its adoption in these institutions. Quantitative data from 230 participants across 34 universities in Greater Accra was analyzed using a structural-equation-modeling approach. With an explanatory power of 77.3%, our model confirms the significant role of all five factors in shaping AI adoption. Our findings highlight the necessity for structured AI implementation strategies, including phased rollouts, professional development initiatives, and continuous system optimization to facilitate sustainable integration in resource-limited contexts. This study provides empirical evidence to guide policymakers and institutional leaders in aligning AI-driven educational innovations with strategic and contextual imperatives.

Artificial intelligence (AI) has reshaped various aspects of human lives, particularly through its capabilities to address complex sustainability challenges. Despite the rapid expansion of AI applications, their contribution to cleaner production and sustainable development remains underexplored, especially in developing nations. In Southeast Asia (SEA), where AI adoption has grown substantially across environmental, economic, and social dimensions, research that examines its role in cleaner production outcomes remains fragmented. In view of this gap, this study conducts a systematic literature review (SLR) of AI applications related to cleaner production and sustainable development by examining relevant themes, application areas, and sustainability dimensions addressed by AI, while evaluating the maturity of AI methodologies, alignment with cleaner production outcomes, and integration with circular economy and resource efficiency goals. Moreover, it investigates the barriers and challenges that constrain AI application and offers future research directions to advance AI deployment for cleaner production and sustainable development across SEA countries.