Identification of Artificial Intelligence Tools in Learning and Human Resource Development: A Systematic Review

Azari, Zahra; Azimi, Esmaeil; Hatami, Javad

doi:10.22034/lss.2026.580603.1068

Identification of Artificial Intelligence Tools in Learning and Human Resource Development: A Systematic Review

Document Type : Original Research Manuscripts

Authors

Zahra Azari ¹

Esmaeil Azimi ²

Javad Hatami ³

¹ PhD Candidate, Master of Educational Technology, Tarbiat Modares University, Tehran, Iran.

² Assistant professor, Faculty of Humanities, Tarbiat Modares University (TMU), Tehran, Iran.

³ Professor, Faculty of Humanities, Tarbiat Modares University (TMU), Tehran, Iran.

10.22034/lss.2026.580603.1068

Abstract

Artificial intelligence is increasingly transforming learning and human resource development (HRD) by enhancing instructional design, personalized learning, performance analysis, and professional development. However, existing reviews have primarily classified AI according to underlying technologies rather than their functional roles in learning and HRD. This systematic review aimed to identify AI tools used in learning and HRD and develop a function-oriented taxonomy of their applications. Following the PRISMA 2020 guidelines, a systematic search was conducted across Scopus, Web of Science, and ScienceDirect. Twenty-five peer-reviewed studies published between 2007 and 2024 met the inclusion criteria. Methodological quality was assessed using the Mixed Methods Appraisal Tool (MMAT, 2018). AI technologies were first coded deductively using the framework of Votto et al. (2021), followed by an inductive thematic synthesis to develop a functional taxonomy. Natural language processing emerged as the dominant AI technology, while teacher professional development was the most frequently investigated application context. The analysis identified five functional categories of AI applications: instructional response and teaching analytics, natural language processing and text analytics, video processing, data management, and interactive and personalized learning. These findings indicate that AI primarily supports instructional feedback, reflective practice, adaptive learning, and evidence-informed decision-making across educational and organizational settings. This review contributes a function-oriented taxonomy that complements existing technology-based classifications and provides a practical framework for researchers, instructional designers, and HRD practitioners. It also highlights the limited evidence in organizational HRD and identifies priorities for future research on the implementation and long-term effectiveness of AI-supported learning environments.

Keywords

Artificial intelligence

Instructional design

Learning and development

Systematic review

Subjects

Educational Technology

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