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Abstract
Artificial Intelligence (AI) is increasingly transforming education, with the emergence of Agentic AI introducing new opportunities for adaptive, autonomous, and context-aware support. This perspective paper explores Agentic AI, a bridge connecting educational leadership, developmental supervision, reflective professional learning, educator readiness, and sustainable education. Drawing on Glickmans Developmental Supervision Model, Gibbs Reflective Cycle, and research on teacher workload and professional growth, the paper argues that Agentic AI can support differentiated supervision, continuous reflection, and personalized professional development while reducing administrative pressures. However, its successful implementation depends on supportive leadership, organizational readiness, and educators’ willingness to engage with innovation. From a perspective paper, Agentic AI is positioned not as a replacement for educators, but as a tool that has the potential to enhance human expertise and strengthen educational sustainability. By integrating technological innovation with human-centered educational practices, Agentic AI offers a pathway toward more adaptive, inclusive, and future-oriented educational systems.
Highlights
Agentic AI: The technology/capability itself.
Agentic AI Bridge Framework: the specific theoretical model.
AI Agency: another name of Agentic AI.
Introduction
Background on AI in education
AI-enabled educational technologies have been present in schools and universities for over two decades. A content analysis of 100 AI-in-Education studies published between 2010 and 2020 revealed that these earlier applications could be organized across three layers: a development layer encompassing classification, matching, and recommendation; an application layer covering feedback and adaptive learning; and an integration layer including affective computing and gamification (). Despite this breadth, persistent challenges remained, including inappropriate use of AI techniques, shifting teacher and student roles, and unresolved ethical concerns (). These earlier AI systems were limited in what they could do. They worked best when given clear instructions and fixed tasks, such as grading a quiz or recommending a resource, but could not respond meaningfully to complex or unpredictable situations (). Managing knowledge in schools and universities still relied heavily on human effort, fixed curricula, and manual process that struggled to keep pace with the fast-changing demands of modern education ().
It became clear that simply digitalizing existing practices was not enough. What was needed were systems that could think, adapt and act, not just respond ().
Agentic AI refers to advanced artificial intelligence systems capable of acting autonomously, adapting to changing situations, making decisions, and pursuing goals with minimal human intervention (). Unlike traditional AI systems that merely respond to prompts, Agentic AI can continuously analyze information, monitor progress, and take proactive actions to support learning, teaching, and organizational improvement. Another study defines it as an interactive system that can process and react to information from its surroundings, including visual inputs, verbal or written communication, and other contextual cues, in order to perform meaningful and appropriate actions (). Agentic AI systems engage in meaningful human-like interactions, are autonomous and context-aware, and present goal-oriented behaviours, initiating actions based on their goals, state, and knowledge, and making informed decisions using data and environmental cues with limited human supervision (). This distinction in not merely technical. It represents a shift from AI as a passive tool that responds to instructions, to AI as an active partner that participates in how knowledge is created, shared, and managed within educational systems (; ).
In education, the growing importance of Agentic AI lies in its ability to create adaptive learning environments, reduce administrative burdens, support professional development, and enhance data-driven decision-making for both educators and school leaders ().
According to the meta-analysis conducted by , AI can support sustainable education by improving academic achievement and increasing learner engagement. Sustainability in education emphasizes institutional effectiveness, operational efficiency, and the long-term continuity of educational systems (). In this context, AI Agency is increasingly viewed as tool for enhancing educational quality, reducing administrative workload, and expanding access to learning opportunities ().
Traditional approaches to teacher professional development have long struggled with structural limitations, including the lack of strategic planning, discontinuous training programmers, and methods that prioritize theoretical knowledge over adaptive professional skills (). These limitations are not simply logistical, they reflect a deeper gap between training contexts and real classroom practice, where knowledge gained in professional development fails to transfer meaningfully into teaching behavior.
Addressing this gap requires more than better training content, it requires systems that can bridge the training context and the practical content continuously and adaptively (). Recent conceptual work has proposed integrating teacher professional development, AI supported learning, and the long-term development of teachers’ professional vision within a single framework. Rather than serving only as a technological tool, AI functions as a bridge that connects professional learning with classroom practice by reinforcing learning transfer, supporting evidence-based reasoning, and promoting adaptive teaching (). This is precisely the role of Agentic AI Bridge framework is designed to fulfill, not as a replacement for professional learning. But as the connecting mechanism that makes professional learning continuous, context-sensitive, and genuinely adaptive across the full educational system.
Within the context of this perspective, Agentic AI is conceptualized as a bridging mechanism that could facilitate the connection between educational leadership, teacher development, and sustainable educational outcomes, see Figure 1. Rather than operating as a collecting of isolated technological functions, Agentic AI facilitates continuous feedback, adaptive support, and data-informed decision-making across multiple levels of the educational system.
Through these processes, it links leadership practices, workload management, developmental supervision, reflective professional learning, and teacher readiness within a unified framework for educational improvement. Unlike conventional AI systems that primarily respond to user inputs or perform predefined tasks, Agentic AI is characterized by its ability to act autonomously, adapt to changing contexts, coordinate multiple actions, and provide proactive support in pursuit of specific goals (; ). As illustrated in Figure 2, Agentic AI differs from classical AI in its higher levels of autonomy, context awareness, adaptability, reasoning, and goal-oriented planning. These are not just technical upgrades; they reflect a fundamentally different way of operating. To understand where Agentic AI sits within the broader landscape of AI in education, propose a framework that classifies educational AI tools form simple general-purpose systems at the lowest level, through instruction executors and AI assistants, to human-AI collaboration at the highest level. Agentic AI operates at this top level, where both the degree of agency and the depth of human-AI interaction are at their highest. This is what makes it capable of doing something earlier AI tools could not: functioning as a genuine partner in educational processes rather than a background utility.
These capabilities enable agentic systems to move beyond simple information delivery and support more dynamic and personalized educational processes. For example, Agentic AI can monitor progress, identify emerging needs, recommend interventions, and assist decision-making without requiring constant human direction (). In educational settings, such capabilities may strengthen professional learning, facilitate adaptive supervision, and support continuous improvement by maintain ongoing feedback loops between leadership, educators, and institutional goals (). Consequently, the value of Agentic AI lies not only in automating tasks, but also in its ability to provide adaptive, context-aware, and goal-driven support that evolves in response to the changing needs of educational systems and their stakeholders.
Figure 1
Figure 2
The organizational implications of Agentic AI in education extend beyond individual classrooms or teachers. At the institutional level, AI tools are already reshaping how schools are organized, managed and lead, automating administrative processes, enabling data-driven decision making, optimizing resource allocation, and transforming leadership from administrative compliance toward instructional focus ().
Critically, however, this transformation is not automatic. argues that most current AI initiatives in school management represent innovation, incremental improvements in efficiency, rather than genuine transformation, where AI catalyzes new forms of participatory leadership, reimagined school organization, and fundamentally student-centered environments. The strongest cases for transformation emerge only when leadership, teacher development, school organization, and technology co-evolve together as a system. This systemic insight is central to the Agentic AI Bridge Framework, which proposes precisely this kind of co-evolution, where Agentic AI does not digitize existing control structures but transforms them by continuously connecting leadership, supervision, professional learning, and sustainability within a self-reinforcing system.
The limitations of earlier AI tools, and the distinct capabilities of Agentic AI, point directly to the need for a new conceptual framework. Prior AI applications in education fell short not because AI itself was inadequate, but because the systems being used lacked the autonomy, adaptability, and contextual judgment needed to engage with the full complexity of educational life (; ).
At the same time, simply deploying more powerful AI is not enough. For Agentic AI to genuinely improve educational outcomes, it must be embedded within clear structures, structures that connect leadership, supervision, professional learning, and institutional sustainability in a coherent and principled way (; ). It is necessary that the Agentic AI Bridge Framework, introduced in the following section, is designed to address. The framework explains how Agentic AI can serve as a dynamic connecting force across educational leadership, developmental supervision, workload management, reflective professional learning, teacher readiness, and long-term sustainability, not as a collection of isolated tools, but as an integrated system that continuously learns, adapts and improves.
This paper is organized as follows. The next section introduces the Agentic AI Bridge Framework and explains the rationale for integrating Glickmans Development Supervision Model, Gibbs Reflective Cycle, teacher readiness, workload management, and sustainability into a single model. The following sections then apply this framework to educational leadership, developmental supervision, and reflective professional learning in turn, showing how each construct is activated through the frameworks three mechanisms.
The paper then discusses teacher readiness as a moderating condition and sustainability as an emergent, long-term outcome. The next sections address the ethical conditions required for responsible deployment. The last sections outline a sustainable orientation, future research agenda and conclusion.
The Agentic AI bridge framework
While generative AI systems and learning analytics platforms have made significant contributions to education, they remain insufficient for the purposes of this framework. Generative AI systems, such as large language models, are reactive by nature: they produce outputs when prompted but cannot independently monitor educational processes, identify emerging needs, or initiate action without human direction (; ). Learning analytics platforms, meanwhile, can track and visualize data effectively but lack the capacity to act on that data autonomously, they surface information for human decision-makers rather than participating in the decision-making process itself (; ). What distinguishes Agentic AI is precisely its agency, the capacity to initiate, monitor, adapt, and act continuously across complex and evolving educational contexts without requiring constant human prompting (; ).
From detection to reflection: how Agentic AI connects leadership, supervision, professional learning and teacher’s readiness
A concrete example helps illustrate this distinction. Consider the challenge of identifying and responding to bullying behavior in schools. A conventional AI system or learning analytics tool might flag patterns in students if bullying happened. Unfortunately, it flags after bullying had already caused harm. A generative AI tool, if asked, might explain what bullying looks like or suggest intervention strategies. But neither can act in real time, without being prompted, to detect and respond to a live incident as it unfolds. Agentic AI, by contrast, can do precisely this. It can be integrated with camera systems and real-time interaction monitoring, Agentic AI can continuously analyze peer interactions, distinguish between bullying and joking in context, and initiate support responses immediately and without waiting for a teacher to notice or intervene (). This kind of continuous, real-time, proactive response is only possible because Agentic AI possesses genuine agency, it does not wait to be asked; it monitors, judges and acts. This example captures something essential about why Agentic AI is uniquely suited to educational environments, because the most important moments where “direct interference” is crucial in school life rarely happen at convenient times or in predictable ways.
In a school setting, to ensure a safe student environment, the school leadership must expect the unexpected. This simple example, when examined carefully, reveals how Agentic AI simultaneously activates all three domains of the framework: “Educations leadership, developmental supervision and reflective professional learning”.
At the level of educational leadership, the detection of a bullying incident generates immediate, evidence-based data that the school principal can act upon without spending hours reviewing footage or waiting for a teachers report. Rather than reacting to a problem that has already escalated, the principal receives a proactive alert, contextualized, specific and actionable. This frees the principal to focus on what matters most, deciding how to respond, speaking with parents, and arranging the right support of the student involved. Rather than spending time finding the problem, the leader can focus entirely on solving it. This is cognitive offloading in action: Agentic AI handles the monitoring so the leader can focus on judgment and people (; ; ).
The same incident also creates a direct opportunity for developmental supervision. The teachers whose classroom the bullying occurred in, do not receive a vague piece of feedback weeks later, they receive specific, real-time evidence of exactly what happened: how the situation developed, what the classroom dynamic looked like beforehand, and where attention may have been directed at key moments.
This gives the supervisor, whether a head of department or a senior colleague, something concrete to work with. If the teacher is newer and less experienced, the supervisor can offer clear, direct guidance on what to do differently. If they are more experienced, the conversation can be more collaborative, exploring together what the evidence reveals and what changes might help. In both cases, supervision moves from a vague impression-based discussion to a specific, evidence-informed conversation grounded in what actually happened (; ).
The process does not stop here. After the supervisory conversation, Agentic AI prompts the teacher to reflect, not at a fixed time on a calendar, but now when reflection is most useful and most meaningful. The prompt follows the stages of Gibbs reflective cycle: what happened, how did it feel, what went well or not well, what else could have been done, and what will be done differently next time (; ). This is not a generic exercise. It is directly connected to a real event the teacher experienced that day. Over time, as these reflective prompts accumulate across different classroom situations, teachers develop deeper professional knowledge and stronger adaptive skills, gradually closing the gap between what is learned in training and what actually happens in practice (; ).
What this example shows is that Agentic AI does not support leadership, supervision, and reflection as three separate activities. It connects them in a single, continuous process, the leadership response shapes the supervisory conversation, the supervisory conversation deepens the reflection, and the reflection builds the teachers readiness to respond better next time. Each part strengthens the next. This is exactly the dynamic the Agentic AI Bridge Framework proposes, and the example of bullying shows clearly why it requires genuine agency to work. A generative AI tool could suggest what to do about bullying if someone asked. A data dashboard could show a pattern after the fact. But only Agentic AI can detect the incident, alert the leader, support the supervisory process, and prompt the teachers reflection. All continuously, without waiting to be asked (; ).
This property is not only desirable; it is theoretically indispensable to the proposed framework. Educations leadership, developmental supervision and reflective professional learning are not isolated, one-time events. They are ongoing, interdependent, and context-sensitive processes that unfold across time and require continuous adaptive support (, ; ).
A system that only responds when prompted cannot maintain the feedback loops, adaptive interventions, and proactive goal pursuit that these processes require. Evidence supports this across multiple educational functions. In student support, Agentic AI systems have demonstrated the ability to analyze academic records, career aspirations, and engagement patterns simultaneously, generating tailored recommendations that dynamically adjust learning pathways in real time as student needs evolve (). At the institutional level, multi-agent architectures integrating large language models, reinforcement learning, and predictive analytics have achieved significant improvements in recommendation accuracy, grading efficiency, and dropout prediction, outcomes that no single reactive or analytics-only system could produce (). At the leadership level, Agentic AI is emerging as a proactive partner for academic leaders, serving as tutor, metacognitive coach, and collaborative teaching assistant simultaneously, reducing administrative burdens while enabling more personalized and responsive educational experiences ().
To make this concrete: a generative AI tool can answer a question about supervision, but it cannot monitor a teachers’ development over weeks, identify when an intervention is needed, and act on that without being asked.
A learning analytics dashboard can show a school leader patterns in teacher performance, but it cannot initiate a conversation, recommend a professional development pathway, or adjust support as the teacher grows. A camera system with classical AI can record a bullying incident, but it cannot distinguish context, judge severity and trigger a real-time response without human instruction. Agentic AI can do all these things continuously, simultaneously, and coherently across an entire educational system. This is why Agentic AI is not simply the most advanced option available, but the only theoretically appropriate foundation for a framework that spans leadership, supervision, professional learning, teacher readiness and sustainability (; ; ).
The framework
Figure 1 presents the Agentic AI Bridge Framework, which conceptualizes Agentic AI not as a standalone tool but as a dynamic mediating system operation through three explicit mechanisms that connect educational constructs to sustainable outcomes.
The left side of the figure shows four educational constructs arranged sequentially: educational leadership sets the institutional vision and goals, which shapes how workload is managed, which in turn creates the conditions for more meaningful developmental supervision, which ultimately deepens teachers’ capacity for reflective learning (, ; ).
These constructs are not independent, each one builds on the previous, forming a developmental chain that Agentic AI is positioned to strengthen.
At the center of the framework, the Agentic AI Bridge operates through three explicit mechanisms. The first is cognitive offloading: Agentic AI absorbs routine administrative tasks, reducing work pressure on school leaders and freeing cognitive resources for higher-order responsibilities such as strategic decision-making and teacher mentoring (; ). This mechanism directly activates the educational leadership and workload management constructs on the left. The second mechanism is reflective amplification: AI-generated prompts and data outputs scaffold deeper professional reflection, initiating and sustaining reflective cycle, prompting teachers to describe what happened, evaluate it critically, and plan deliberate changes to their practice (). This mechanism activates developmental supervision and reflective learning constructs. The third mechanism is adaptive readiness loop: the framework continuously adjusts its outputs in response to the readiness conditions present in the school, ensuring that AI integration remains responsive rather than imposed ().
The right side of the figure maps each mechanism to the AI capabilities it produces cognitive offloading generates decision-making assistance and data-driven supervision; reflective amplification produces reflective prompts and collaborative leadership support setting; and the adaptive readiness loop generates ongoing readiness scaffolding that builds teacher confidence over time.
A critical theoretical proposition of the framework is that teacher readiness functions as a moderate variable, not a sequential step. As shown in Figure 1, teacher readiness sits below the Agentic AI Bridge and sends upward arrows into all three mechanisms. This means the degree to which each mechanism activates and produces its intended capability is bounded by teachers’ individual and institutional readiness to engage with Agentic AI (). When readiness is low, the mechanisms are partially activated; as readiness develops through professional learning, the mechanisms operate at full capacity.
A second, distinct theoretical proposition concerns the ethical deployment condition, shown in Figure 1 as governing all three mechanisms rather than moderating them in the manner of teacher readiness. Where teacher readiness determines the degree to which each mechanism activates, the ethical deployment condition determines whether a mechanism may legitimately operate at all.
It functions as a gating condition rather than a graded moderator: consent, oversight, accountability and data protection are not variables that partially strengthen or weaken a mechanisms output, but requirements that must be satisfied before any mechanism is considered active within the framework. This distinction matters theoretically. Teacher readiness describes a continuum, low readiness produces partial activation, and readiness developed through professional learning produces fuller activation. The ethical deployment condition, by contrast, describes a threshold, a mechanism whether meets the requirements or not. Positioned this way, the ethical deployment condition and teacher readiness operates as two independent constraints on the same three mechanisms: readiness governs how fully a mechanism operates once permitted, while the ethical deployment condition governs whether it is permitted to operate in the first place.
Finally, the framework does not terminate at a fixed outcome. Sustainable education emerges as the product of a reinforcing feedback loop. As the three mechanisms produce improved leadership, supervision and reflective practice, these outcomes strengthen teacher readiness, which in turn deepens the effectiveness of all three mechanisms in the next cycle (; ).
This self-sustaining dynamic is what the framework proposes as the essential difference between Agentic AI and prior generations of educational technology. It does not produce one-time improvement, but rather a continuously improving system of educational practice.
These domains do not operate independently, they form a dynamic, interdependent system in which changes in one domain directly influence the others. Agentic AI initiates this system by first addressing workload. When administrative burdens on school leaders are reduced through cognitive offloading, leaders gain the time, attention, and relational capacity needed to engage in meaningful developmental supervision (; ). This matters because Glickmans development supervision model effective implementation becomes difficult when supervisors and teachers experience excessive workload. It requires supervisors to accurately read a teachers developmental sate and respond with the appropriate level of support, a judgement that demands genuine presence and focus rather that distracted compliance (). When supervisions quality improves, it creates the conditions for deeper professional reflection. Teachers who receive contextually sensitive evidence-based supervisory feedback are better positioned to move through the full sequence of Gibbs reflective cycle, progressing beyond surface description of events toward genuine evaluation, critical analysis, and deliberate action planning (; ).
This deeper reflection, sustained over time, builds teacher readiness, not simply familiarity with AI tools, but the professional confidence, adaptability, and self-regulation needed to engage fully with Agentic AI systems and benefit from what they offer (). As readiness grows, the three mechanisms of the framework, cognitive offloading, reflective amplification, and adaptive readiness loop. Activate more effectively in each subsequent cycle. Leadership might become more strategic, supervision could become more developmental, reflection probably becomes more transformative, and the system might become more capable of sustaining improvement over time. It is this self-reinforcing dynamic, where each domain strengthens the next, and the cycle continuously deepens, that produces sustainable educational outcomes not as a fixed endpoint, but as an emergent property of the framework itself (; ).
These two theories (Gibbs and Glickmans) were not combined simply because they appear together in the literature. Each one describes a different level at which change happens, and together they cover the full path from an individual teachers experience to system-wide outcomes. At the individual level, Reflective Cycle describes how a teacher processes and learns from experience.
At the relational level, Glickmans Development Supervision Model describes how a supervisor adjusts support, more directive or more collaborative, to match a teachers stage of development. At the capacity level, workload management and teacher readiness describe whether a teacher has the time, energy, and disposition needed for reflection and supervision to happen. At the sustainability level, it describes the long-term, cumulative outcome of these processes across an institution. Agentic AI is the one element that touches every level, which is what makes it possible to treat it as a causal connector linking these constructs, rather than a shared them running through them descriptively.
Boundary conditions and transferability
The Agentic AI Bridge Framework is developed with reference to educational leadership and supervision practice in the UAE, where supervisory authority is comparatively centralized and system-level AI adoption is actively promoted through national digital-transformation policy. We treat this setting as an illustrative case rather a definitional boundary. The frameworks three mechanisms are stated at a level of abstraction: cognitive offloading, reflective amplification and adaptive readiness, that we expect to generalize across school and higher-education contexts.
however, the strength and even the direction of the linkages between constructs are conditioned on at least four contextual factors: (a) the degree of centralization in supervisory authority, which shapes who controls AI-generated data and how it is used in evaluation; (b) the maturity of digital infrastructure and data-governance regulation; (c) prevailing norms of teacher autonomy and professional trust, which affect whether AI-mediated monitoring is experienced as support or surveillance; and (d) resource availability, which determines whether workload relief from automation is realized in practice or offset by new technical burdens. We therefore present the framework as a transferable but context-conditioned model, and we return to these conditions when discussing cultural and social considerations below.
Cultural and social considerations
Agentic AI in educational supervision is not purely technical intervention; it is embedded in relations of trust and authority between teachers, supervisors, and institutions. The same data-driven supervision mechanism (Mechanism 1) that a teacher is one setting experiences as evidence-based support may, in a context with lower institutional trust or stronger surveillance norms, be experienced as intensified managerial control.
Whether this occurs depends on teacher autonomy norms (the degree to which teacher expect independent professional judgment to be respected); privacy expectations and existing data-protection regulation; parental and community expectations about the role of technology in schooling; and the broader policy environments stance on algorithmic decision making in public institutions. We therefore treat cultural and social embeddedness as a moderating layer that sits alongside teacher readiness (Figure 1): the frameworks mechanisms are activated not only by individual readiness but also by the collective, institutional acceptability of AI-mediated supervision.
Ethical deployment condition
Ethical governance is not treated as an external constraint on an otherwise complete framework. It is a condition on which the frameworks mechanisms depend for legitimate operation (Figure 1). Specifically, any deployment of Agentic AI withing the supervisory mechanisms described above must satisfy four conditions: informed consent from teachers regarding what is monitored and how it is used; meaningful human oversight of AI-generated judgments before they inform supervisory decisions; clear accountability for erroneous or biased outputs; and data protection consistent with applicable regulations.
Any AI capabilities referenced as an illustrative example in this framework, such as automated behavioral incident detection, must be handled carefully. Before such as capability is treated as part of the framework, it must be examined against the following concerns. First, does the capability involve continuous monitoring that amounts to surveillance, regardless of its intended developmental purpose? Second, can meaningful consent realistically be obtained from those being monitored, rather than consent being assumed as a condition of participation? Third, how is the resulting data stored, accessed and protected, particularly where it concerns sensitive behavioral information? Fourth, what happens when the system produces a false positive, and what consequence follow for the individuals involved? Fifth, who is a accountable when such an error occurs? Sixth, does relying on the capability risk reducing a complex, context-dependent human interaction to a single algorithmic classification, at the cost of the relational nuance human judgment would otherwise bring? These questions are not treated as a brief limitation to be noted once and set aside. They are built into the Ethical Deployment Condition itself, so that no capability is considered part of the framework until it can be shown to satisfy consent, oversight, accountability, and data protection requirements ().
Agentic AI, educational leadership, and workload management
Despite the growing potential of Agentic AI in education, schools continue to face increasing administrative demands, data-driven responsibilities, and workplace pressures that affect teachers’ performance, decision-making, and professional growth (). Teachers are expected to manage instructional responsibilities while also handling documentation, reporting, assessment tracking, and continuous data analysis (). These growing demands often limit opportunities for meaningful professional development and reflective practice, placing operational tasks ahead of instructional improvement ().
In response to these challenges, educational institutions are increasingly exploring the use of AI to improve efficiency and support educational practices. AI-based systems can automate repetitive administrative tasks, organize educational data, and support more informed decision-making processes, allowing teachers to dedicate greater attention to teaching and student learning (). Effective school leadership remains essential in ensuring that these technologies are implemented responsibly and in ways that genuinely support educators rather than increase complexity ().
When integrated effectively, Agentic AI may reduce workload pressures, strengthen educational leadership through cognitive offloading, and contribute to more adaptive and sustainable learning environments.
Leadership is not treated here as a single enabling condition. Instead, several leadership traditions are distinguished, each showing a different pathway through which Agentic AIs mechanisms operate. In instructional-leadership terms, cognitive offloading (Mechanism 1) is theorized to redirect leaders’ freed attention toward instructional conversations, sharpened by AI-generated evidence rather than impressionistic observation. In distributed-leadership terms, the same offloading creates capacity for supervisory and monitory responsibilities to be distributed to teacher-leaders rather than concentrated in formal leadership roles (). In data-informed-leadership terms, Agentic AI changes not just the volume but the timeliness and granularity of decision-making data, altering the pace at which leadership decisions can reasonably be made. In technology-leadership terms, leaders are positioned not as passive beneficiaries of automation but as active governors of it, responsible for the Ethical Deployment Condition described in Figure 1.
Treating these strands separately clarifies that Agentic AI does not act on leadership generically but interacts with specific leadership practices in specific ways.
Beyond improving operational efficiency, Agentic AI also may enhance the creation of opportunities for more personalized and responsive approaches to teacher supervision and professional support. This aligns closely with developmental models of supervision that recognize teachers’ varying levels of expertise, readiness, and professional needs.
Agentic AI and the Glickman’s developmental supervision model
Building on the role of Agentic AI in reduction administrative workload and supporting educational leadership, its potential extends beyond operational efficiency to the enhancement of teacher development. As schools seek more personalized and sustainable approaches to professional growth, there is a growing need for supervisory models that recognize the diverse needs, experiences, and readiness levels of educators. In this regard, Agentic AI offers a promising mechanism for strengthening the implementation of Glickmans Developmental Supervision Model.
Glickmans Developmental Supervision Model recognizes that teachers vary in their levels of expertise, commitment, and instructional readiness, requiring different supervisory approaches ranging from directive supervision for novice teachers to collaborative and non-directive supervision for more experienced educators ().
Although the model provides a strong framework for differentiated teacher support, its implementation of often constrained by limited supervisory capacity, time pressures, and difficulties in providing individualized guidance at scale.
Agentic AI offers a practical solution to these challenges by enabling continuous, data-informed, and adaptive support. For novice teachers, AI agents can function as instructional coaches by identifying areas of improvement, recommending targeted professional learning resources, and providing structured guidance aligned with classroom needs. Such support can supplement the efforts of school leaders, particularly in contexts where increasing workload limits opportunities for frequent supervision ().
For teachers at intermediate stages of professional development, Agentic AI can facilitate more collaborative forms of supervision by analyzing classroom data, identifying professional learning needs, and recommending relevant peer-learning opportunities. Rather than replacing human interaction, these systems can strengthen collaboration by ensuring that professional dialogue is informed by evidence and focused on instructional improvement ().
Similarly, experienced teachers can benefit from non-directive forms of AI-supported supervision.
Through performance analytics, research recommendations, and reflective prompts, Agentic AI can encourage self-directed professional growth while preserving teacher autonomy. In this way, the technology serves as a catalyst for reflection rather than a prescriptive decision-maker.
The integration of Agentic AI with the Developmental supervision Model demonstrates how intelligent technologies can support differentiated and scalable professional development wile maintaining the human-centered principles of effective supervision. By aligning supervisory practices with teachers’ developmental needs, educational institutions can foster continuous growth, strengthen instructional quality, and better prepare educators and learners for the demands of an evolving educational landscape (; ).
While differentiated supervision provides teachers with support that aligns with their professional needs, long-term improvement also depends on continuous reflection and learning from experience. Effective professional growth requires educators to critically examine their practices, adapt to changing educational demands, and engage in ongoing development. In this regard, reflective frameworks such as Gibbs Reflective Cycle can be strengthened through the adaptive and personalized capabilities of Agentic AI.
Agentic AI and Gibb’s reflective professional learning
While differentiated supervision provides with support that aligns with their developmental needs, sustained professional growth also depends on continuous reflection and learning from experience. Reflective practice enables educators to evaluate their instructional decisions, identify areas for improvement, and adapt to evolving educational demands. In this regards, Gibbs Reflective Cycle offers a valuable framework that can be enhanced through the adaptive capabilities of Agentic AI.
Gibbs Reflective Cycle () provides a structured process through which teachers critically examine their experiences through six stages: description, feelings, evaluation, analysis, conclusion and action planning. Reflective practice is widely recognized as a key component of professional learning because it strengthens self-awareness, critical thinking, and instructional effectiveness. However, meaningful reflection is often constrained by time limitations, workload pressures, and the absence of structured support systems within schools ().
Agentic AI presents an opportunity to transform reflective practice from an occasional activity into a continuous and personalized learning process.
Unlike traditional AI systems, Agentic AI can provide adaptive guidance, contextual feedback, and ongoing reflective dialogue based on teachers’ experiences and classroom data (). For example, AI agents can guide educators through the stages of reflection, identify recurring instructional patterns, recommend evidence-based strategies, and support action planning for future practice. Through their ability to retain previous interactions and provide scaffolded support, these systems can foster more consistent and sustainable professional development ().
Nevertheless, the effectiveness of reflective practice depends on more than technological support. Professional learning remains deeply rooted in human interaction, collegial collaboration, mentorship, and contextual understanding. While Agentic AI can strengthen reflective processes, it cannot fully replicate the empathy, trust, and shared experiences that emerge through professional learning communities and instructional leadership. Consequently, Agentic AI should be viewed as a complement to, rather than a replacement for, the human dimensions of professional growth ().
This balance between technological support and human agency also highlights an important consideration for the successful integration of Agentic AI in education.
Although intelligent systems can provide personalized guidance and professional support, educators differ in their willingness and ability to engage with such innovations. As a result, individual characteristics and personal traits play a critical role in shaping how teachers perceive, adopt, and benefit from Agentic AI.
Personal traits and readiness for Agentic AI
The preceding discussion highlights the potential of Agentic AI to support educational leadership, reduce workload pressures through cognitive offloading, enhance developmental supervision by operationalising Glickmans model by providing continuous evidence, and strengthen reflective professional learning and growth that functions as an external scaffold that initiates Gibbs cycle. However, the successful implementation of these opportunities depends not only on technical capabilities but also on the individuals who engage with them. As a result, educators’ personal traits, experiences, and readiness for innovation play a critical role in determining the effectiveness of Agentic AI within educational settings, as it moderates the threshold at which each mechanism activates.
Research suggests that teachers differ in their readiness to adopt new technologies, technological competence, and willingness to engage in continuous learning ().
While some educators readily embrace innovation, others may experience challenges due to differences in personality, beliefs, professional experience, or prior exposure to technology. Teachers who demonstrate openness, adaptability, and confidence are generally more likely to integrate AI-based tools into their practice, whereas concerns related to technological complexity or fears of replacement may hinder adoption. These differences suggest that educational transformation cannot be achieved through a one-size-fits-all approach ().
From this perspective, Agentic AI has the potential to support more personalized pathways for professional growth. By adapting to teachers’ needs, capabilities, and levels of readiness, AI systems can provide differentiated professional development, individualized feedback, and tailored learning opportunities that promote confidence and continuous improvement (). Such personalized support aligns closely with the developmental and supervisory approaches discussed earlier, reinforcing the idea that effective educational improvement requires responsiveness to individual differences rather than uniform interventions ().
In addition to individual characteristics, contextual factors such as school culture, access to technology, and attitudes toward innovation also influence educators’ perceptions of Agentic AI.
Educational leaders therefore play an important role in fostering environments that encourage experimentation, professional learning, and responsible technology adoption (; ). Recognizing these human and contextual dimensions is essential for ensuring the Agentic AI serves as a tool for empowerment rather than a source of resistance.
Ultimately, the effectiveness of Agentic AI depends on the interaction between technological capabilities and human readiness (). This perspective reinforces the broader argument of this paper that sustainable educational transformation requires not only intelligent technologies but also supportive leadership, adaptive professional development, and educators who are prepared to engage with change (; ).
Collectively, the perspectives presented thus far suggest that the transformative potential of Agentic AI extends beyond any single educational function. By supporting leadership, reducing workload pressures, enabling differentiated supervision, fostering reflective professional learning, and accommodating individual differences among educators, Agentic AI creates the conditions necessary for sustainable and future-oriented educational systems. This raises an important question: how can these interconnected benefits contribute to the long-term sustainability of education in an increasingly complex and rapidly changing world?
Toward sustainable and future-oriented education
Collectively, the preceding discussion demonstrates that the value of Agentic AI extends beyond isolated educational functions.
Through its ability to reduce workload pressures, support educational leadership, facilitate differentiated supervision, strengthen reflective professional learning, and accommodate individual differences among educators, Agentic AI may contribute to the broader goal of educational sustainability as an emergent property of reinforcing loops. Rather than functioning solely as technological innovation, it has the potential to serve as an enabling mechanism that connects multiple dimensions of educational improvement.
Sustainable education is commonly associated with maintaining educational quality, institutional effectiveness, operational efficiency, and the long-term capacity of educational systems to respond to changing societal needs (). From this perspective, sustainability is viewed as an outcome of effective leadership, ongoing professional development, educator adaptability, and organizational capacity for continuous improvement. Agentic AI may offer significant opportunities to support these goals. Research suggest that AI-enabled educational technologies can enhance learning engagement, improve academic achievement, expand access to learning opportunities, and support more inclusive educational practices (; ).
Furthermore, adaptive and personalized learning environments can help address diverse learner needs while promoting educational quality and equity, which align closely with the objectives of Sustainable Development Goal 4 (; ).
However, the sustainability of educational transformation depends on more than technological advancements alone. As highlighted throughout this paper, effective implementation requires supportive leadership, manageable workloads, differentiated supervision, continuous professional learning, and educators who are willing and prepared to engage with innovation. When these elements operate in isolation, their impact may be limited. When connected through the adaptive capabilities of Agentic AI, they create a more coherent and responsive educational ecosystem that supports both teacher development and student success.
From a synthetic perspective, Agentic AI can be viewed as the bridge between current educational challenges and the future of sustainable education. It links leadership with professional growth, supervision with reflection, and technological innovation with human development. Importantly, its value lies not in replacing educators or educational leaders, but in augmenting their capacity to make informed decisions, personalize support, and foster meaningful learning experiences.
As educational systems continue to evolve in response to increasing complexity and rapid technological change, the successful integration of Agentic AI may provide a pathway toward more adaptive, inclusive, and sustainable educational environments that prepare both educators and learners to thrive in a rapidly changing world. Within the Agentic AI bridge Framework, sustainability is conceptualized as the cumulative outcome of the interconnected processes discussed throughout this perspective paper. Effective leadership, manageable workloads, developmental supervision, reflective professional learning, and teacher readiness collectively contribute to the long-term resilience and effectiveness of educational systems. Agentic AI may support these processes through adaptive feedback, personalization, and continuous decision support, thereby strengthening the capacity of educational institutions to sustain improvement over time.
While this perspective highlights the potential of Agentic AI to support educational leadership, teacher development, and sustainability, its implementation also raises important challenges related to data privacy, algorithmic bias, teacher surveillance, accountability, and unequal access to technology (; ).
Furthermore, excessive reliance on AI-supported recommendations may affect professional autonomy and create new forms of dependency on digital platforms (; ). These concerns highlight the importance of ethical governance, transparency, and human-centered implementation strategies when integrating Agentic Ai into educational systems.
Future research
As a conceptual contribution, the propositions advanced here require empirical testing before they can be treated as established findings. four lines of research are proposed, aligned with the frameworks structure. First, design-based research pilots that introduce Agentic AI tools into supervisory workflows, measuring workload and decision-making effectiveness before and after (testing Mechanism 1). Second, qualitative interview studies with school leaders and teachers examining whether AI-generated prompts measurably alter the depth or frequency of Gibbs-Cyle reflection compared with unprompted reflection (testing Mechanism 2). Third, development and validation of a teacher-readiness survey instrument, tested against observed patterns of AI utilization, to establish whether readiness functions as a moderator as proposed (testing Mechanism 3).
Fourth, a longitudinal mixed-methods study tracking institutions over multiple academic years, to test whether the proposed reinforcing feedback loop is observable as an accumulating trend toward the sustainability indicators used in the wider literature (; ).
Conclusion
This paper argues that the value of Agentic AI lies not in any single educational application, but in its potential to connect multiple dimensions of educational improvement within a unified framework. Through the proposed Agentic AI Bridge Framework, educational leadership, workload management, developmental supervision, reflective professional learning, teacher readiness, and sustainable education outcome are conceptualised as interconnected process rather than isolated initiatives. Agentic AI serves as the connecting mechanism, facilitating adaptive support, continuous feedback, and data-informed decision-making across educational systems (; ).
Realising this potential, however, depends on how honestly institutions engage with the risks that accompany AI integration. Continuous AI monitoring can erode the professional autonomy that effective supervision and reflection require. Predictive systems can amplify existing inequalities rather than correct them.
And the data collection that Agentic AI deepens upon creates privacy and consent obligations that many institutions are not yet equipped to meet (; ; ). There are not peripheral concerns, they are conditions. The frameworks reinforcing feedback loop only produces sustainable outcomes when AI recommendations are transparent and contestable, data governance is robust, and teacher professional autonomy is genuinely protected rather than monitored (; ). As a conceptual perspective, the relationships proposed in this paper should be viewed as theoretical possibilities rather than empirically established outcomes. Future research is needed to examine the practical implementation of Agentic AI in educational settings and to evaluate its impact on leadership practices, teacher development, and long-term educational sustainability.
Ultimately, the contribution of this perspective lies in repositioning Agentic AI from a standalone technological innovation to a principled connecting mechanism, one that supports more adaptive, human-centered, and sustainable educational systems. Sustainable education in the age of Agentic AI is not simply about institutional efficiency. It is about preserving the reflective depth that Gibbs cycle demands, the supervisory trust that Glickmans model requires, and the human judgment that no algorithm can replace.
When these conditions are met, Agentic AI does not replace what makes education meaningful, it strengthens it (; ; ).
Statements
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
Author contributions
MH: Writing – review & editing, Investigation, Writing – original draft, Supervision, Conceptualization, Funding acquisition, Resources, Formal analysis, Visualization, Project administration, Validation. AK: Project administration, Writing – original draft, Formal analysis, Visualization, Resources, Validation, Investigation, Supervision, Writing – review & editing, Funding acquisition, Conceptualization. WM: Writing – review & editing, Investigation, Writing – original draft, Funding acquisition, Supervision, Visualization, Resources, Conceptualization, Validation, Formal analysis, Project administration. AK: Resources, Project administration, Writing – review & editing, Funding acquisition, Validation, Visualization, Formal analysis, Supervision, Investigation, Writing – original draft, Conceptualization. MA: Funding acquisition, Writing – original draft, Writing – review & editing, Formal analysis, Resources, Visualization, Project administration, Supervision, Validation, Conceptualization, Investigation.
HA: Writing – original draft, Writing – review & editing, Supervision, Funding acquisition, Resources, Investigation, Project administration, Validation, Formal analysis, Visualization, Conceptualization.
Funding
The author(s) declared that financial support was not received for this work and/or its publication.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
Generative AI tools were used solely to support language editing, text refinement, formatting, and reference organization. No AI-generated content contributed to the research design, data analysis, findings, or interpretation of results. The authors thoroughly reviewed all outputs and retain full responsibility for the authenticity, accuracy, and integrity of the manuscript.
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References
1
Al-ZoubiZ.AlKaabiA.QablanA.BatainehO.IssaH. B. (2024). The impact of work pressure on decision-making effectiveness among department heads in faculties of educational sciences. PLoS One19 (8), e0304584. 10.1371/journal.pone.0304584
2
Al-ZyoudH. M. M. (2020). The role of artificial intelligence in teacher professional development. Univers. J. Educ. Res.8 (11B), 6263–6272. 10.13189/ujer.2020.082265
3
AlkaabiA. M. (2023a). “Designing enduring and impactful professional development to support teacher growth,” in Innovations in Teacher Development, Personalized Learning, and Upskilling the Workforce, ed. ChakravartiS. (IGI Global Scientific Publishing), 1–23. 10.4018/978-1-6684-5518-0.ch001
4
AlkaabiA. M. (2023b). “Revitalizing supervisory models in education: integrating adult learning theories and stage theories for enhanced teaching and learning outcomes,” in Restructuring Leadership for School Improvement and Reform, eds.
AlkaabiA. M.AbdallahA. K. (IGI Global Scientific Publishing), 253–277. 10.4018/978-1-6684-7818-9.ch013
5
AlsuhaymiA. O.AtallahF. A. (2026). Sustainable education in the age of artificial intelligence and digitalization: a value-critical approach. Sustainability18 (3), 1257. 10.3390/su18031257
6
ArtsinM.BozkurtA. (2026). “Dreams, distortions, and disruptions: deconstructing the educational promises of agentic AI,” in Rethinking Education and Agency in the Age of Human-Generative AI Interaction, ed. BozkurtA. (IGI Global Scientific Publishing), 1–18.
https://www.igi-global.com/chapter/dreams-distortions-and-disruptions/3924377
AryaM. K. J.BhaskarD. K.SinuT. S.BinuV. P. (2026). Agentic AI for education: a unified multi-agent framework for personalized learning and institutional intelligence. arXiv, 2604.16566. 10.48550/arXiv.2604.16566
8
AzevedoL.RoblesP.BestE.MallinsonD. J. (2025). Institutional policies on artificial intelligence in higher education: frameworks and best practices for faculty. New Dir. Adult Contin. Educ.2025 (188), 70–78. 10.1002/ace.70013
9
BandiA.KongariB.NaguruR.PasnoorS.VilipalaS. V. (2025). The rise of agentic AI: a review of definitions, frameworks, architectures, applications, evaluation metrics, and challenges.
Future Internet17 (9), 404. 10.3390/fi17090404
10
Córdova-EsparzaD.-M. (2025). AI-powered educational agents: opportunities, innovations, and ethical challenges. Information16 (6), 469. 10.3390/info16060469
11
DaiL.JiangY.-H.ChenY.GuoZ.LiuT.-Y.ShaoX. (2025). “Agent4EDU: advancing AI for education with agentic workflows,” in Proceedings of the 2024 3rd International Conference on Artificial Intelligence and Education, ICAIE ‘24. pp. 180–185. 10.1145/3722237.3722268
12
DengX.YuZ. (2023). A meta-analysis and systematic review of the effect of chatbot technology use in sustainable education. Sustainability15 (4), 2940. 10.3390/su15042940
13
DexterS. (2018). “The role of leadership for information technology in education: systems of practices,” in Second Handbook of Information Technology in Primary and Secondary Education, eds. VoogtJ.KnezekG.ChristensenR.LaiKW. (Springer Nature Link), 483–498. 10.1007/978-3-319-71054-9_32
14
FloridiL.CowlsJ. (2019). A unified framework of five principles for AI in society. Harv. Data Sci. Rev.1 (1), 10.1162/99608f92.8cd550d1
15
GibbsG. (1988). Learning by Doing: A Guide to Teaching and Learning Methods. Oxford: Further Education Unit.
https://cir.
nii.ac.jp/crid/157141712505297958416
HallingerP.HeckR. H. (2010). Collaborative leadership and school improvement: understanding the impact on school capacity and student learning. Sch. Lead. Manag.30 (2), 95–110. 10.1080/13632431003663214
17
HashimS. N. A.YaacobA.SuryaniI.Mohd AsrafR.BahadorZ.SupianN. (2023). Exploring the use of Gibbs’ reflective model in enhancing in-service ESL teachers’ reflective writing. Arab World Engl. J.14, 236–253. 10.24093/awej/vol14no2.17
18
HowardS. K.TondeurJ.SiddiqF.SchererR. (2021). Ready, set, go! Profiling teachers’ readiness for online teaching in secondary education. Technol. Pedagog. Educ.30 (1), 141–158. 10.1080/1475939X.2020.1839543
19
IbrahimH. R.AlghfeliA. H.AlnuaimiF. S.AlshamsiN. N.AlkaabiA. M. (2023). “STEM And leadership in the future: a path to innovation, sustainability, and entrepreneurship,” in Restructuring Leadership for School Improvement and Reform, eds. AbdallahA. K.AlkaabiA. M. (IGI Global Scientific Publishing), 420–439. 10.4018/978-1-6684-7818-9.ch021
20
IniestoF.BossuC. (2023). Equity, diversity, and inclusion in open education: a systematic literature review. Dist. Educ.44 (4), 694–711. 10.1080/01587919.
2023.2267472
21
International Task Force On Teachers For Education 2030 (2026). Our Mission [Teacher Task Force]. Paris: Teacher Task Force. Available online at:
https://teachertaskforce.org/who-we-are/our-mission(Accessed June 26, 2026).22
KhalidS.IslamA.AjmalM. (2025). The end of human-only knowledge management: agentic AI in education. J. Manag. Sci. Res. Rev.4 (4), 2024–2042. 10.5281/zenodo.18628398
23
KostopoulosG.GkamasV.RigouM.KotsiantisS. (2025). Agentic AI in education: state of the art and future directions. IEEE. Access.13, 177467–177491. 10.1109/ACCESS.2025.3620473
24
LaiJ. W.QiuW.LimF. S. (2025). “From insights to impact: an agentic learning intelligence framework for student-centred success,” in Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium, Blue Sky, and WideAIED, eds. CristeaA. I.WalkerE.LuY.SantosO. C.IsotaniS. (Switzerland: Springer Nature Switzerland), 109–118. 10.1007/978-3-031-99261-2_10
25
LampropoulosG.SiakasK.MakkonenP.SiakasE. (2021). A 10-year longitudinal study of social media use in education. Int. J. Technol. Educ.4 (3), 373–398.
10.46328/ijte.123
26
NisaU.ShiraziM.SaipM. A.PoziM. S. M. (2026). Agentic AI: the age of reasoning. J. Autom. Intell.5 (1), 69–89. 10.1016/j.jai.2025.08.003
27
OECD (2020). TALIS 2018 Results (volume II): Teachers and School Leaders as Valued Professionals. Paris: OECD Publishing. 10.1787/19cf08df-en
28
Okulich-KazarinV.ArtyukhovA.Skowron?ArtyukhovaN.Wo?owiecT. (2024). Will AI become a threat to higher education sustainability? A study of students’ views. Sustainability16 (11), 4596. 10.3390/su16114596
29
PappachanR.JabeenS. S. (2026). “Digital leadership and agentic AI in higher education,” in Augmenting Empathetic Leadership Through Agentic AI, ed. VihariN. S. (IGI Global Scientific Publishing), 447–470. 10.4018/979-8-3373-6650-0.ch016
30
PaulA.YuC. L.SusantoE. A.LauN. W. L.MeadowsG. I. (2024). Agentpeertalk: empowering students through agentic-AI-driven discernment of bullying and joking in peer interactions in schools. arXiv, 2408.01459. 10.48550/arXiv.2408.01459
31
ReimersF.AzimZ.PalomoM.-R.ThonyC. (2026). “AI And school organization and management,” in Artificial Intelligence and Education in the Global South: A Systems Perspective, eds. ReimersF.AzimZ.PalomoM.-R.
ThonyC. (Switzerland: Springer Nature), 117–133. 10.1007/978-3-032-11449-5_7
32
SpillaneJ. P.HalversonR.DiamondJ. B. (2004). Towards a theory of leadership practice: a distributed perspective. J. Curric. Stud.36 (1), 3–34. 10.1080/0022027032000106726
33
SudarshanV. K.SisodiaA.RamachandraR. A.BatraS.LengJ. C. L. (2026). Agentic AI ecosystems in higher education: a perspective on AI agents to emerging inclusive, agentic multi-agent AI framework for learning, teaching and institutional intelligence. arXiv, 2605.14266. 10.48550/arXiv.2605.14266
34
TammetsK.LeyT. (2023). Integrating AI tools in teacher professional learning: a conceptual model and illustrative case. Front. Artif. Intell.6, 10.3389/frai.2023.1255089
35
TaoH. B.DiaV. R.GuerraY. M. (2019). Artificial intelligence and education challenges and disadvantages for the teacher. Arctic. Med. Res.72 (12), 30–51.
36
UNESCO. (2024). Guidance for generative AI in education and research. Available online at:
https://school-education.ec.europa.eu/en/discover/publications/guidance-generative-ai-education-and-research(Accessed May 27, 2026).37
VindigniG. (2025). Data-driven disparities: how AI applications in education may perpetuate or mitigate inequality.
EJSMT1 (4), 4–54. 10.59324/ejsmt.2025.1(4).02
38
Zawacki-RichterO.MarínV. I.BondM.GouverneurF. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators?Int. J. Educ. Technol. High. Educ.16 (1), 39. 10.1186/s41239-019-0171-0
39
ZhaiX.ChuX.ChaiC. S.JongM. S. Y.IstenicA.SpectorM.et al (2021). A review of artificial intelligence (AI) in education from 2010 to 2020. Complexity2021 (1), 8812542. 10.1155/2021/8812542
Summary
Keywords
Agentic AI, developmental supervision, educational leadership, professional development, reflective practice, sustainability
Citation
Al Hasan M, Kashef A, Mohsen W, Khan AGY, Alomari M and Aishan H (2026) The Agentic AI bridge: a perspective on educational leadership, teacher development, and sustainability in the AI ERA. Front. Educ. 11:1895511. doi: 10.3389/feduc.2026.1895511
Received
30 May 2026
Revised
04 July 2026
Accepted
06 July 2026
Published
20 July 2026
Volume
11 - 2026
Edited by
Mohammed Borhandden Musah, Emirates College for Advanced Education, United Arab Emirates
Updates
Copyright
© 2026 Al Hasan, Kashef, Mohsen, Khan, Alomari and Aishan.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).
The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Wissal Mohsen 700039354@uaeu.ac.ae
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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