Abstract

Abstract
Training employees in organizations is essential for enhancing productivity and profitability, updating their knowledge, and better preparing them for market demands. Through digital platforms (LMS) and the e-Learning method, training occurs in web-based environments, enabling content management and accessibility across multiple devices. e-Learning typically follows a modular structure, ensuring adaptability, flexibility, and asynchronous learning. This study applies to the Design Science Research method to implement a data protection training course via an LMS, facilitating knowledge dissemination and employee self-assessment. The organization faces challenges in rapidly spreading knowledge due to its widespread locations, diverse working hours, and geographical constraints. The study evaluates training dissemination through microlearning, leveraging Moodle (LMS) and Digital Storytelling techniques. Additionally, it assesses the pedagogical and engagement aspects to ensure training is efficient, standardized, flexible, and more appealing to employees, increasing their receptivity and interest.

Abstract
Artificial intelligence (AI) is transforming how organizations develop human potential, offering scalable and data-driven support for coaching and capability building. This study proposes and validates a conceptual framework for integrating AI into organizational coaching processes to enhance competence development and strategic alignment. AI-supported coaching in this research is treated as an emerging organizational technology whose potential organizational value depends less on model capability and more on governance design, decision rights, and auditable evaluation outputs. Following a mixed-methods, multi-phase design, the research combined a Systematic Literature Review (SLR) with the construction of a layered design architecture in which OSCAR serves as the primary coaching-process scaffold, complemented by KSA for competency specification, Situational Leadership for adaptive guidance, and KPIs for monitoring and governance. The framework structures AI-supported coaching across 10 interrelated phases, from contextual anchoring to review and measurement, while preserving iterative re-entry to earlier phases whenever review evidence, contextual change, or insufficient progress makes adjustment necessary. Prototyping demonstrated feasibility and coherence across models, while the focus group provided qualitative expert feedback on the framework’s clarity, governance needs, and perceived usefulness for competence development. At this stage, however, the KPI structures generated by the framework and the descriptive comparison across AI tools should be interpreted as prototype-level outputs rather than as empirically validated performance measures or evidence of added value over baseline approaches. Because the evaluation relied on two fictional prototyping scenarios and a small expert-oriented focus group (n = 6), the findings should be interpreted as evidence of prototype demonstration and qualitative refinement rather than of real-world effectiveness or organizational impact. The study also does not include a control group or comparison with traditional human coaching, so the added value of the AI-supported framework over alternative coaching arrangements remains a question for future empirical testing. Findings suggest that AI can usefully support organizational coaching by personalizing dialogue, structuring reflection, and generating auditable development artefacts, provided ethical safeguards and human oversight remain integral. The research contributes a preliminarily validated, ethics-informed, and governance-aware framework for AI adoption in organizational coaching and offers practical insights for embedding AI-enabled development in learning organizations.

Abstract

Healthcare organizations increasingly rely on complex digital systems, but software onboarding often depends on manuals and classroom-based training that do not fit well with fast-paced clinical workflows. Interactive in-app guidance may better support learning during real work, although healthcare-specific evidence is still limited.

Abstract