ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 9
www.rsisinternational.org
Transforming Teacher Leadership and Learning with Artificial
Intelligence: A UAE Educational Perspective
Aishah Ahmed Hamdan AlZeyoudi
1
, Mohd Fauzi Kamarudin
2
1,2
Fakulti Pengurusan Teknologi dan Teknousahawanan, Universiti Teknikal Malaysia Melaka
DOI: https://dx.doi.org/10.47772/IJRISS.2025.92800002
Received: 01 November 2025; Accepted: 08 November 2025; Published: 18 December 2025
ABSTRACT
The integration of Artificial Intelligence (AI) in education represents a transformative force reshaping teacher
leadership and student-centered learning practices. This study examines how AI technologies influence teacher
leadership roles, decision-making processes, and pedagogical approaches within the United Arab Emirates
(UAE) educational context. Employing an integrated theoretical framework combining the Technology
Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), Technology
Readiness Index (TRI), and Bandura’s self-efficacy theory, we investigate the complex relationships between
AI integration, teacher perceptions, and educational outcomes. The research identifies key barriers and enablers
affecting AI adoption in UAE schools, including technological readiness, perceived usefulness, self-efficacy,
and cultural-linguistic considerations. Our findings reveal that effective AI integration requires simultaneous
attention to technical infrastructure, professional development, ethical considerations, and supportive leadership
structures. The study contributes to understanding how teacher leaders can facilitate AI adoption while
maintaining student-centered pedagogies in culturally diverse educational settings.
Keywords: Artificial Intelligence, teacher leadership, student-centered learning, UAE education, technology
acceptance, educational innovation
INTRODUCTION
The worldwide phenomenon of globalization has transformed numerous sectors, including educational
institutions throughout the planet [1]. The contemporary education landscape has been fundamentally changed
through the integration of Artificial Intelligence (AI), which includes machine learning and natural language
processing among other subfields [2]. As a nation that leads through innovation and economic diversification,
the United Arab Emirates (UAE) stands out as a forwardthinking state in today’s dynamic global environment
[3]. AI serves as a strategic tool for transforming educational practices and administrative leadership in schools
throughout the
UAE [4]. UAE leaders have embraced AI technologies as they offer personalized learning opportunities and
administrative automation while generating meaningful insights through their digital transformation goals [5].
However, the potential of AI to improve teaching performance spans multiple dimensions including instructional
quality, student engagement, and learning outcomes [6].
The primary challenge remains the disconnect between how widely available AI technologies are and their
effective incorporation into everyday teaching methods [7]. The UAE has made significant investments in state-
of-the-art digital infrastructure, yet many teachers remain unsure about how to use AI for better student-centered
learning and classroom management [8]. Teacher leadership in AI contexts requires directing professional
learning networks alongside managing digital content curation and advocating for teaching methods based on
data insights [9]. This study addresses the fundamental gap in understanding how AI integration influences
teacher leadership and student-centered learning in UAE schools. The primary research question guiding this
investigation is: How does the integration of artificial intelligence influence teacher leadership and student-
centered learning in UAE schools?
The research objectives include: (1) to evaluate the impact of AI on teacher leadership roles, including decision-
making and collaboration, (2) to assess how AI tools support student-centered learning practices, (3) to identify
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 10
www.rsisinternational.org
barriers and enablers influencing teachers’ adoption of AI, and (4) to propose model explaining the mediating
effects of perceived usefulness, self-efficacy, and technological readiness on educational outcomes [10].
LITERATURE REVIEW
2.1 Theoretical Frameworks
Four theoretical constructs guide the explanation of why teachers adopt AI tools. The Technology Acceptance
Model (TAM) proposes that teachers form behavioural intentions mainly by weighing perceived usefulness and
perceived ease of use of the technology [11]. Davis introduced these constructs in 1989 to explain workplace
behaviours, arguing that when people expect a tool both to deliver clear benefits and to require minimal effort,
they form stronger intentions to use it [12].
The Unified Theory of Acceptance and Use of Technology (UTAUT) emerged as an effort to bring coherence
to fragmented models of technology adoption [13]. Venkatesh et al. synthesized eight prominent frameworks
into a single model centred on four core constructs: performance expectancy, effort expectancy, social influence,
and facilitating conditions [14]. The Technology Readiness Index (TRI) shifts the focus from beliefs about a
particular system to more enduring, dispositional traits that predispose individuals toward or away from new
technologies [15]. Bandura’s self-efficacy theory centres on individuals’ beliefs in their capacity to organize and
execute the actions necessary to achieve specific performance attainments [16]. In the context of digital
pedagogy, teacher self-efficacy reflects an educator’s conviction that they can successfully integrate and leverage
digital tools while maintaining effective classroom engagement [17].
2.2 AI Integration in UAE Educational Context
In the Gulf region, and specifically the UAE, national strategies explicitly position AI at the heart of economic
diversification and knowledge economy goals [18]. The UAE’s Vision 2031 and related policy documents
underscore education as a cornerstone for cultivating future-ready citizens, prompting substantial investment in
AI-enabled learning environments [19]. The Ministry of Education’s introduction of an AI curriculum as a
mandatory subject from the 2025-26 academic year signals a cross-curricular “core capability” approach rather
than optional add-ons [20]. The UAE’s multicultural, multilingual classrooms present unique challenges and
opportunities for AI tools [21]. AI-driven systems often rely on datasets and training materials rooted in Western
contexts; when applied in UAE classrooms, language mismatches can hinder effectiveness. Cultural references
embedded in content may either resonate or alienate learners depending on how well the system reflects local
traditions and values [22].
CONCEPTUAL FRAMEWORK
This study proposes an integrated theoretical model that combines TAM, UTAUT, TRI, and selfefficacy theory
to explain how AI integration influences teacher leadership and student-centered learning outcomes. The
conceptual framework is illustrated in Figure 1.
Figure 1: Conceptual Framework: AI Integration Impact on Teacher Leadership and Student Centered Learning
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 11
www.rsisinternational.org
The model posits eleven hypotheses that describe the relationships between AI integration, teacher perceptions,
and educational outcomes:
H1: AI Integration positively influences Perceived Usefulness among teachers.
H2: AI Integration positively influences Perceived Ease of Use.
H3: AI Integration positively influences Technological Readiness.
H4: AI Integration positively influences Teacher Self-Efficacy.
H5: Perceived Usefulness positively influences Behavioural Intention to use AI.
H6: Perceived Ease of Use positively influences Behavioural Intention.
H7: Technological Readiness positively influences Behavioural Intention.
H8: Teacher Self-Efficacy positively influences Behavioural Intention.
H9: Perceived Usefulness, Perceived Ease of Use, Technological Readiness, and Teacher Self-Efficacy jointly
influence Behavioural Intention.
H10: Behavioural Intention positively influences Teacher Leadership Outcomes.
H11: Teacher Leadership Outcomes positively influence Student-Centered Learning Outcomes.
RESEARCH METHODOLOGY
This study employs a mixed-methods approach combining quantitative surveys with qualitative interviews to
examine the relationships between AI integration, teacher leadership, and student-centered learning in UAE
schools. The research
design incorporates an integrated theoretical framework that combines TAM, UTAUT, TRI, and self-efficacy
theory to provide a comprehensive understanding of technology adoption in educational contexts [25].
Data collection involves surveys administered to teachers across multiple emirates, focusing on their experiences
with AI tools, perceptions of technology usefulness and ease of use, self-efficacy levels, and leadership
behaviors. The survey instrument includes validated scales adapted for AI contexts, measuring constructs such
as perceived usefulness, ease of use, technological readiness, and self-efficacy [26].
Semi-structured interviews with teacher leaders, school administrators, and educational technology specialists
provide deeper insights into implementation challenges and success factors. The sampling strategy targets
teachers in public and private schools across Dubai, Abu Dhabi, and other emirates, ensuring representation of
diverse educational contexts [27].
The research examines adaptive learning platforms, intelligent tutoring systems, and learning analytics
dashboards as primary AI tools of interest. These tools were selected because they are increasingly used in UAE
classrooms to streamline administrative work while providing personalized learning experiences and actionable
data insights [28].
RESULTS AND DISCUSSION
5.1 Key Findings on AI Integration Impact
The analysis reveals that AI integration significantly influences teacher leadership roles through several
pathways. Teachers who adopt AI tools demonstrate enhanced decision-making capabilities, improved
collaborative practices, and increased mentorship of peers. The study identifies that effective AI integration
requires a holistic approach addressing technical infrastructure, professional development, ethical
considerations, and cultural sensitivity [29].
Teachers’ willingness to lead and embrace AI technologies plays a crucial role in their successful incorporation
into everyday teaching within the rapidly changing educational context of the UAE. Educators who adopt AI
tools into their teaching methods find these tools to be highly beneficial for boosting their productivity and
student achievement [30].
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 12
www.rsisinternational.org
5.2 Barriers and Enablers in UAE Schools
Key barriers identified include insufficient professional development, data privacy concerns, cultural and
linguistic challenges, and inadequate technical infrastructure in some schools. Teachers report feeling
unprepared to interpret AI-generated analytics and lack confidence in managing AI-driven classroom dynamics
[31].
Enabling factors include supportive school leadership, peer mentoring networks, user-friendly AI platforms, and
clear policy guidelines. Professional learning communities emerge as crucial vehicles for diffusing AI expertise
across schools. Teacher leaders who master AI integration provide essential mentorship to colleagues, offering
practical guidance on setting up adaptive platforms and interpreting learning analytics dashboards [32].
The concept of technological readiness represents a comprehensive state where users demonstrate both necessary
skills and psychological willingness to embrace new technological advancements. Direct active participation
with AI technologies strengthens teachers’ readiness and adaptability [33].
5.3 Student-Centered Learning Transformation
AI tools enable personalized learning routes that let students advance at their own speed while exploring topics
of interest or difficulty. However, personalization alone does not ensure that educational experiences are truly
focused around students. Instructors need to modify their teaching strategies to promote active student
engagement and ownership of their educational paths [35].
The integration of AI catalyzes shifts in how lessons are conducted and how teacher-student interactions unfold.
Traditional teacher-centered lecturing gives way to facilitation roles: educators guide students in navigating AI-
supported tasks, encouraging them to engage critically with automated feedback [36].
In UAE contexts where adaptive STEM or language-learning tools have been piloted, teacher leaders often guide
colleagues in interpreting dashboard outputs, translating raw metrics into pedagogical actions such as grouping
students by skill profiles, adjusting pacing, or selecting supplementary resources [37].
IMPLICATIONS AND RECOMMENDATIONS
6.1 Theoretical Contributions
This research advances theoretical understanding of AI in education through its exploration of teacher leadership
and student-centered learning as key components of AI integration within the UAE educational environment.
The study extends the applicability of TAM and UTAUT frameworks in school-based AI adoption, revealing
how cultural and linguistic differences along with specific educational policies influence teacher readiness to
adopt AI technologies [39].
The integrated theoretical model demonstrates that perceived usefulness combined with ease of use,
technological readiness, and self-efficacy creates a comprehensive framework to evaluate AI’s educational
impact for educators and policymakers in the UAE’s innovative environment [40].
6.2 Practical Implications for Educators
Practical recommendations include developing comprehensive teacher training programs that address both
technical skills and pedagogical integration of AI tools. Schools should establish peer mentoring networks and
professional learning communities to support AI adoption. Professional development should emphasize self-
efficacy building through scaffolded mastery experiences, peer observations, and constructive feedback cycles
[41].
Teacher leaders should receive dedicated time allocations, training, and recognition rather than being
overburdened on top of existing duties. Schools need to recognize and support evolving teacher identities
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 13
www.rsisinternational.org
through recognition mechanisms and clear role definitions that balance innovation leadership with workload
considerations [42].
6.3 Policy and Institutional Recommendations
Policy makers need to address data privacy concerns and establish clear guidelines for ethical AI use in
educational settings. The absence of robust regulations creates uncertainty: some educators hesitate to adopt AI
tools without assured compliance, while others experiment via nonstandardized methods, risking inconsistent
practices [44].
Educational institutions should create supportive settings that enable teachers to learn data interpretation from
AI systems while collaborating with colleagues to modify instructional approaches and develop innovative
teaching methods supported by technology [45].
The UAE’s commitment to technological innovation in education requires strategic investments targeting
teacher training programs and technology infrastructures while building collaborative networks that respect
cultural values and ethical principles [46].
CONCLUSION
This study provides valuable insights into the complex relationships between AI integration, teacher leadership,
and student-centered learning in UAE schools. The research demonstrates that successful AI adoption requires
a holistic approach addressing technical infrastructure, professional development, ethical considerations, and
cultural sensitivity [47].
The findings contribute to the growing body of knowledge on educational technology adoption while providing
practical guidance for stakeholders in the UAE’s innovative educational environment.
The study highlights the UAE’s distinct educational system to produce knowledge that has regional importance
and potential international impact [48].
Future research should examine longitudinal impacts of AI integration on student outcomes and explore the
evolution of teacher leadership roles as AI technologies continue to advance. Additional investigations should
focus on the effectiveness of different professional development models and the long-term sustainability of AI-
enhanced pedagogical practices [49].
The UAE’s development into an innovation centre demands detailed research into AI adoption to guarantee
technology advances fulfill the mission of providing quality education that is inclusive and future-ready. The
lessons from UAE educational research hold broad relevance as countries across the globe face common
obstacles such as teacher readiness, leadership development, ethical data handling, and technology integration
that respects cultural diversity [50].
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ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
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ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 15
www.rsisinternational.org
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