Educational Organizations in the AI Era: Digital Management and Leadership, Digital Skills, and Innovation

Synthesis: This review by Kalogeratos, Anastasopoulou, and Kapota argues that AI adoption in educational organizations is decided less by infrastructure than by three human variables: leadership practice, teachers' AI Literacy, and an innovation-oriented organizational culture. The authors describe their method as a systematic literature review, drawing on Scopus, Web of Science, and Google Scholar and limiting the search to publications between 2015 and 2025. Their central claim is that digital transformation is "not merely a technical upgrade" but a holistic shift that requires alignment between leadership, human capital, and organizational structures. Leaders appear as facilitators of change who set vision and make experimentation safe; teachers appear as the primary agents who translate technological possibility into classroom practice. Change Management is treated as an organizational problem rather than a procurement one. The review reports no study count and no quality appraisal, so it is best read as an argument about institutional conditions rather than as appraised evidence on effects.

Key Findings

1. Three drivers are named as decisive. Leadership practices, teachers' digital competence, and innovation culture are said to "collectively determine the success or failure" of AI integration, not technology choice.
2. Leadership is framed as facilitation, not administration. Effective leaders are described as facilitators of change who promote collaboration, strategic planning, and Workplace Learning, and who align innovation with institutional goals.
3. Teachers' digital skills have three dimensions. The review separates technical, pedagogical, and ethical competencies: tool operation, integration into teaching strategy, and responsible use including Privacy awareness.
4. The corpus came from three databases. Scopus, Web of Science, and Google Scholar were searched with a combined keyword set, restricted to publications between 2015 and 2025, then analyzed thematically.
5. Technology alone is called insufficient. Impact depends on how AI is adopted and used by people inside the organization, and leadership without skilled educators cannot achieve sustainable transformation.
6. Cultural conditions are treated as infrastructure. A culture valuing creativity, openness, and continuous learning is said to enable experimentation, while rigid or hierarchical cultures are said to discourage change.
7. Risks are acknowledged, not measured. Data privacy, algorithmic bias, and transparency are raised as concerns to be met with governance, ethical awareness, and inclusive practice, but no intervention is evaluated.

What the review argues

The paper's organizing claim is that AI integration is a human-capital problem wearing a technology costume. AI is credited with supporting Adaptive Learning environments, Intelligent Tutoring systems that simulate one-on-one guidance, and Automated Assessment that uses Educational NLP to grade essays and open-ended responses. Learning Analytics and predictive analytics are presented as tools for spotting at-risk students and planning. None arrives with reported outcomes; they come as narrative synthesis. The distinctive contribution is that such gains depend on leadership vision, teacher competence, and culture. Digital skills are unpacked into technical, pedagogical, and ethical dimensions and described as dynamic rather than fixed, so Teacher AI Competency must be maintained rather than certified once. Teaching shifts from transmitter of content toward designer of AI-mediated learning.

How the evidence was assembled

The method section describes identification, screening, eligibility assessment, and synthesis, with inclusion limited to studies on AI applications in educational settings, leadership in digital transformation, or teachers' digital competence. Keywords included "Artificial Intelligence in education," "educational leadership," "digital skills," and "innovation." The authors position thematic synthesis as a way to minimize bias and strengthen validity. What a reader would need to check that claim is missing: no protocol registration, no number of records screened or included, no quality appraisal, and no effect sizes. The paper concedes that longitudinal studies and empirical investigations are still needed, an implicit acknowledgment that the evidence base it summarizes is thin. Readers should compare it against the Meta-Analysis and Systematic Review literature and Limitations in AIEd Research norms.

Leadership, competence, and culture as one system

The review's most useful move is to refuse to treat its three variables separately. Innovation is described as emerging from the interaction between technological advancement, human capability, and organizational structure, with Human AI Collaboration implied: AI supplies the technical foundation, leadership supplies direction, teachers determine implementation. Transformational leadership is singled out because it articulates a shared vision and empowers staff to experiment. The authors stress alignment across policy, leadership, and practice, warning that misalignment produces fragmented efforts and limited impact. That framing places Educational AI Policy and AI Governance inside the same system as classroom practice rather than in a separate compliance layer. Equity appears in the same register: AI systems may reinforce existing inequalities if not carefully designed and implemented, so institutions are asked to weigh social and ethical dimensions alongside technical ones.

What this means for practice

• Treat AI adoption as a change-management program. Budget for Workplace Learning and protected experimentation time before buying tools, since the review ties results to competence, not procurement.
• Build competence across all three dimensions. Technical training alone leaves the pedagogical and ethical layers, including Privacy awareness and judgment about AI output.
• Fund leadership development, not just devices. The review's leaders set vision, allocate resources, and make experimentation safe.
• Check policy alignment. Ask whether institutional strategy, teaching practice, and AI Governance rules point the same way, since the authors attribute fragmented results to misalignment.
• Plan for equity and bias explicitly. Include Bias Mitigation review and provision for students with different access conditions.

Limitations

• Narrative, not systematic, despite the label. The paper calls itself a systematic literature review but reports no PRISMA protocol, no study count, and no quality appraisal, so its claims are not a synthesis of appraised evidence.
• Venue and evidential scope. It appeared in a general multidisciplinary journal, not an education research venue, and reports no primary data, outcome measures, or effect sizes.
• Assertions outrun the evidence. Statements such as leadership without skilled educators being unable to achieve transformation are asserted, not tested, and should be treated as hypotheses.
• Context of included work is unreported. The text does not say where the underlying studies were conducted or which levels they covered, so transfer to a specific system or Levels of Education remains uncertain.

Citation

Kalogeratos, G., Anastasopoulou, E., & Kapota, T. (2026). Educational Organizations in the AI Era: Digital Management and Leadership, Digital Skills, and Innovation. International Journal of Advanced Multidisciplinary Research and Studies, 6(3), 78-84.