Beyond the hype: evidence-based approaches to responsible AI integration in workplace learning

Oct 17, 2026

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This paper examines how artificial intelligence (AI) can be integrated into organizational training and development using an evidence-based lens. It addresses the gap between technological enthusiasm and pedagogical effectiveness in workplace learning, highlighting misalignments between current AI practices and established learning principles. Findings show AI supports personalized delivery and routine skills, yet struggles with transformative learning and social learning dynamics. Cultural and ethical factors shape implementation success and outcomes.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Introduction

Methodology

Integrative literature review approach

Literature identification and screening

Phased analysis and synthesis

ET 67,9 928 Received 23 January 2025 Revised 26 July 2025 18 August 2025 Accepted 28 September 2025 Education þ Training Vol. 67 No. 9, 2025 pp. 928-939 © Emerald Publishing Limited e-ISSN: 1758-6127 p-ISSN: 0040-0912 DOI 10.1108/ET-01-2025-0034 Beyond the hype: evidence-based approaches to responsible AI integration in workplace learning Martin Sposato Zayed University, Dubai, United Arab Emirates Abstract Purpose – This paper examines the integration of artificial intelligence (AI) in organizational training and development through an evidence-based lens, addressing the gap between technological enthusiasm and pedagogical effectiveness in workplace learning contexts. Design/methodology/approach – An integrative literature review methodology was employed following Torraco’s (2016) framework for synthesizing diverse knowledge streams. The analysis examined 180 papers from multiple databases, combining theoretical frameworks from social cognitive theory (Bandura, 1986) and organizational learning theory (Dittmar et al., 2025) to develop an integrated conceptual framework. Findings – The analysis reveals critical misalignments between current AI implementation practices and established learning principles. While AI demonstrates effectiveness in personalized content delivery and routine skill development, significant challenges emerge in supporting transformative learning and maintaining social learning dynamics. Cultural variations and ethical considerations substantially influence implementation success. Originality/value – This paper contributes an integrated theoretical framework bridging individual and organizational learning perspectives in AI contexts, identifies specific gaps between technological capabilities and pedagogical requirements and proposes evidence-based guidance for practitioners navigating AI implementation decisions. Keywords Artificial intelligence, Training and development, Evidence-based practice, Workplace learning, Organizational learning theory Paper type Conceptual paper Introduction The accelerating adoption of artificial intelligence (AI) in workplace learning often leads organisations to deploy advanced learning solutions without adequate evidence of impact, reflecting broader organisational implementation challenges identified in systematic reviews of AI deployment Lee et al. (2023) . Recent systematic reviews demonstrate rapidly expanding applications of AI across educational contexts (Zawacki-Richter et al., 2019; Chen et al., 2020; Dogan et al., 2023), yet significant questions remain about effectiveness and appropriate implementation in organizational settings. This disconnects between technological capability and pedagogical understanding creates risks for organizations investing heavily in AI-enhanced learning solutions without adequate evidence of impact. The challenge extends beyond simple technology adoption. As organizations navigate digital transformation accelerated by recent global disruptions (Amankwah-Amoah et al., 2021), the integration of AI into workplace learning requires careful consideration of how these technologies interact with established learning theories and organizational practices. Meta-analytic evidence reveals substantial variation in AI effectiveness across different contexts and applications (Chiu et al., 2023), suggesting that implementation quality and contextual factors critically influence outcomes. This paper addresses a fundamental question: How can organizations move beyond technological enthusiasm to implement AI in ways that genuinely enhance workplace learning? Through systematic analysis of empirical evidence and theoretical frameworks, this investigation develops an integrated approach for understanding AI’s multifaceted impact on both individual and organizational learning processes. The analysis combines social cognitive theory’s insights into individual learning mechanisms with organizational learning th

FAQs

What problem does the paper focus on regarding AI in workplace learning?

It focuses on the mismatch between organizations adopting AI for training with limited evidence of impact and the need for pedagogically effective, evidence-based implementation.

What methodology does the study use?

The study uses an integrative literature review following Torraco’s framework, synthesizing evidence across theories and research streams.

What main findings does the paper report?

It finds misalignments between current AI implementation practices and established learning principles, with benefits for personalized and routine skill development but challenges for transformative and social learning.

Oct 17, 2026

Upload by jayni