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This study aims to provide practical insights on the research areas and prediction trends of learning technologies that support authentic assessment practices in the digital education setting. Technology-enabled learning can be utilized to enhance assessment design in meaningful ways that resembles the professional work environment. Innovative technology-enabled assessment practices necessitate an understanding of the technologies at-hand, hence a disciplined inquiry is called upon into the multitude of technologies that define the landscape. The study looked to investigate the technology groups and research efforts within the field of learning technologies that were shaping digitally mediated authentic assessment practices. Using Horizon Report (HR), an annual education-based technology forecasting publication, as a reference, the study looked to evaluate if HR is a good proxy to predict the evolution of learning technologies in the said scape. Research distinguished four key learning technology groups, namely ubiquitous learning technology, adaptive learning technology, immersive learning technology, and learning object. Network analyses performed on the four technology groups identified key generalizable research themes that can be utilized for research focus. The study also found that HR is a useful tool to predict technology trends, as corroborated from bibliometric analyses, and detailed the composition and evolution of these trends. This study can help educators and researchers identify and decide promising potential areas for future research and/or investment focus.

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The rapidly growing research landscape in finance, encompassing environmental, social, and governance (ESG) topics and associated Artificial Intelligence (AI) applications, presents challenges for both new researchers and seasoned practitioners. This study aims to systematically map the research area, identify knowledge gaps, and examine potential research areas for researchers and practitioners. The investigation centers around three research questions: key research themes for ESG and AI in finance, research intensity and interest evolution, and the use and progression of AI techniques within these themes. Eight archetypical research domains were identified: (i) Trading and Investment, (ii) ESG Disclosure, Measurement and Governance, (iii) Firm Governance, (iv) Financial Markets and Instruments, (v) Risk Management, (vi) Forecasting and Valuation, (vii) Data, and (viii) Responsible Use of AI. Distinctive AI techniques were found to be employed across these archetypes. The study contributes to consolidating knowledge on the intersection of ESG, AI, and finance, offering an ontological inquiry and key takeaways for practitioners and researchers. Important insights include the popularity and crowding of the Trading and Investment domain, the growth potential of the Data archetype, and the high potential of Responsible Use of AI, despite its low publication count. By understanding the nuances of different research archetypes, researchers and practitioners can better navigate this complex landscape and contribute to a more sustainable and responsible financial sector.

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