Transforming Teaching and Learning with Robotic Process Automation: A Systematic Review of Pedagogical Applications
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.
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.
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.
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.
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.
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.
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.
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.
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.
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