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Item type:Publication, Generative AI solutions for faculty and students: A review of literature and roadmap for future research(Informing Science Institute, 2024-01-01)Aim/Purpose This paper aims to address the gap in comprehensive, real-world applications of Generative Artificial Intelligence (GenAI) in education, particularly in higher education settings. Despite the evident potential of GenAI in transforming educational practices, there is a lack of consolidated knowledge about its practical effectiveness and real-world impact. Background This study addresses this gap by conducting a systematic literature review to collate and analyze real-life instances of GenAI applications in higher education, thus providing a nuanced understanding of its practical implementations and measurable outcomes. Methodology The paper utilizes a systematic literature review methodology, adopting the PRISMA approach complemented by a thematic analysis procedure to ensure a comprehensive and in-depth evaluation of the literature. It synthesizes information from relevant articles from 2022 to 2024, focusing on the applications of GenAI in higher education. This analysis covers various aspects, including research settings, analysis scales, data types, collection tools, and analytical methods. Contribution The paper contributes to the academic community by offering a comprehensive review of GenAI applications in education, highlighting the current precision level of these tools, and providing strategic recommendations for their effective use in academia. Furthermore, the research defines seven specific cases where Gen AI can be utilized as a reference for educational institutions in their adoption strategies. Findings Key findings include the versatility of GenAI in generating teaching materials, enhancing skill development, supporting student tasks, academic performance evaluation, feedback delivery, and its role as a virtual assistant and in research support. Recommendations for Practitioners Practitioners are advised to explore the integration of GenAI for diverse educational purposes, from content creation to student assessment, while being cognizant of its limitations and ethical considerations. Recommendations for Researchers Future research should focus on addressing the gaps identified, such as the implications of GenAI in research roles, its application in various disciplines, and the exploration of newly developed AI tools tailored to specific educational needs. Impact on Society The findings of this paper highlight the potential of GenAI in revolutionizing the educational sector, offering personalized learning experiences, and significantly influencing teaching methodologies and student engagement, but it also reveals significant deficiencies of Generative AI, known as hallucinations, which can impact the expected results. Future Research Subsequent research should explore the evolving capabilities of GenAI models, their impact on various academic disciplines, and the development of pedagogical strategies to optimize their use in education. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, TRANSFORMING TEACHING AND LEARNING WITH ROBOTIC PROCESS AUTOMATION: A SYSTEMATIC REVIEW OF PEDAGOGICAL APPLICATIONS(Informing Science Institute, 2025-11-28)Aim/Purpose: Robotic Process Automation (RPA) is revolutionizing education by automating repetitive tasks, enhancing personalized learning, and optimizing assessment processes. This study examines how RPA transforms pedagogical practices, improves student engagement, and enables educators to focus on high-value instructional strategies. Through a systematic literature review, this research synthesizes best practices and identifies key opportunities for maximizing the impact of RPA on teaching and learning. Background: The digital transformation of education is reshaping how institutions deliver teaching and learning. Within this landscape, automation has become central to educational reform, yet its pedagogical implications remain insufficiently understood. Although RPA has been widely adopted in business contexts, its application in education is still emerging, with limited evidence on how it contributes to teaching effectiveness, student engagement, and institutional sustainability. This gap underscores the importance of a systematic review to consolidate current knowledge. Methodology: A systematic literature review following PRISMA methodology was conducted, analyzing peer-reviewed studies (2019-2024) from Web of Science, Scopus, and Google Scholar. Thematic analysis was applied to extract trends, benefits, and implementation challenges. Contribution: This systematic review synthesizes evidence from 17 peer-reviewed studies, moving beyond the conventional emphasis on administrative efficiency to highlight pedagogical applications of RPA. It identifies how RPA supports adaptive learning, enhances student engagement, and facilitates data-driven decision-making in education. The study also proposes a structured framework to guide integration strategies. Findings: The synthesis of the reviewed studies indicates that RPA is not merely an administrative tool but also has the potential to act as a catalyst for pedagogical transformation. The literature highlights five main areas of impact: (1) personalized learning, by dynamically adapting educational content to students' progress; (2) automated assessment and feedback, enhancing grading accuracy and providing data-driven insights; (3) student behavior analysis, supporting early identification of learning gaps; (4) experiential and simulated learning, making education more immersive, and (5) optimization of teacher time, enabling educators to prioritize higher-order instructional strategies. Recommendations for Practitioners: Institutions should prioritize RPA adoption in areas such as assessment, tutoring, and student engagement, where automation can demonstrably reduce workload and enhance instructional quality. Implementation should be accompanied by technical training and collaboration with IT professionals to ensure sustainability. Recommendation for Researchers: Future research should investigate how RPA integrates with AI-driven systems (e.g., adaptive learning models, natural language processing) to support personalized and scalable educational practices. Longitudinal and cross-institutional studies are also needed to assess sustained impacts on teaching efficiency and student outcomes. Impact on Society: By democratizing access to personalized education, RPA reduces teacher workload and enhances learning equity, particularly in underfunded institutions. If properly implemented, RPA can bridge gaps in educational quality and foster more inclusive learning environments. Future Research: Further studies should investigate the integration of RPA with AI-driven models to enhance automated feedback and grading. Research should also address competency development, scalable implementation, and barriers to adoption.2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Digital transformation and the future of human resources: emerging dimensions, implementation challenges and a strategic framework(Emerald Publishing Limited, 2026-07-01)This paper examines how digital transformation (DT) is reshaping human resource (HR) management across organizations of diverse sizes and sectors. Grounded in resource-based view and dynamic capabilities theory, it addresses three research questions: what is the current state of research on DT in HR; what challenges organizations face when implementing digital technologies in this field; and what gaps remain in the academic literature requiring further exploration. Design/methodology/approach A systematic literature review was conducted following PRISMA guidelines. The search string was applied to Web of Science in April 2026, retrieving 418 publications spanning 2015–April 2026. Following a two-pass screening process and thematic analysis using Braun and Clarke (2006) six-step procedure, 60 empirically grounded studies were retained. Findings Thematic synthesis of the 60 retained studies reveals five dimensions through which DT is reshaping human resources: people analytics, AI for talent management, process automation, digitalization of learning and cultural transformation. Cultural transformation emerges as the foundational enabling condition across all dimensions. Critical challenges are identified as mutually reinforcing barriers: cultural resistance, digital skills gaps, ethical and governance concerns and structural constraints. Practical implications Ethical data governance frameworks must precede analytics deployment. AI should be adopted through an augmentation logic, enhancing rather than replacing human judgment, with bias, transparency and consent governance as non-negotiable prerequisites. Automation implementation should begin incrementally with low-complexity, high-volume processes. Digital learning must be treated as a strategic capability driver, integrated with real work processes and designed inclusively to avoid techno-overload. Critically, cultural readiness assessment and resistance measurement must begin before technology deployment, not after. Originality/value The study proposes two practitioner-oriented tools: a cultural framework for DT in HR, structured around five strategic nodes and a five-phase implementation roadmap, providing an evidence-based architecture for responsible, effective and sustainable HR DT applicable across diverse organizational contexts.