The study was led by Xibei Xiong of Guangxi Normal University, working with colleagues from Xuchang University, Shanghai Normal University, the University at Buffalo in the United States, and The Education University of Hong Kong. Together, the team designed a new measurement instrument grounded in the Attitude–Cognition–Capability (ACC) model, a framework that treats digital leadership as three distinct but interrelated dimensions: how leaders feel about digital technologies, how well they understand them conceptually, and how effectively they can deploy them in real institutional settings. By separating these dimensions, the researchers could pinpoint precisely where administrators’ preparedness breaks down—something earlier, more monolithic measures of technology leadership could not do.

The results paint a nuanced picture. Overall, the administrators exhibited a moderately high level of digital leadership, and their attitudes toward digital technologies were generally positive. Their cognitive understanding—grasp of what digital tools can do and how they fit into educational strategy—was also reasonable. But when it came to practical capability, the ability to actually implement, manage, and lead digital initiatives, scores dropped noticeably. In other words, many administrators believe in the promise of AI and digital tools and can articulate their potential, yet lack the hands-on competence to translate that belief into institutional change. This gap between intention and execution is the study’s central finding, and it carries significant implications for how education systems around the world prepare leaders for technological transformation.

Beyond the aggregate scores, the team applied latent profile analysis, a statistical technique that identifies hidden subgroups within a population based on response patterns. This analysis revealed four distinct digital leadership profiles among the surveyed administrators. The first group, labeled Hesitant Adopters, shows reluctance or uncertainty toward digital technologies and limited capability. The second, Enthusiastic Implementers, combines strong positive attitudes with active engagement, though potentially without the deepest conceptual grounding. The third group, Balanced Moderates, displays even, moderate levels across attitude, cognition, and capability. Finally, Comprehensive Experts represent the ideal: administrators who score highly on all three dimensions and can genuinely lead AI-era transformation. The existence of these four profiles suggests that a one-size-fits-all approach to leadership development would miss the mark for most administrators.

“Unlike previous studies, we offer a multidimensional, empirically validated account of educational digital leadership in the AI era, based on large-scale data from China,” the authors, Xiong and colleagues, explain in the paper. This multidimensional framing matters because educational digital leadership is not simply a matter of personal enthusiasm for gadgets. As the researchers define it, EDL is the competency to integrate digital technologies strategically, foster innovation, and ensure that implementation is both equitable and ethical. In the AI era, that definition takes on added weight: school leaders must now weigh questions of data privacy, algorithmic bias, and the shifting role of educators while simultaneously championing personalized learning and streamlined administration.

One of the study’s most surprising results concerns which background factors actually predict strong digital leadership. Gender and educational attainment emerged as consistently associated with higher digital leadership scores, whereas age, years of work experience, and administrative position played far more limited roles. Simply put, having led schools for many years does not automatically make an administrator a stronger digital leader. This finding challenges a common assumption that seniority equates to readiness for technological change, and it suggests that experience built in a pre-digital era may not transfer directly to the demands of AI-mediated education.

Equally important is what the demographic factors did not explain. Gender, education, age, and experience together accounted for only a small fraction of the variation in digital leadership between administrators. The researchers interpret this as evidence that broader environmental conditions—such as a school’s digital culture, access to technological resources, and alignment with higher-level policy—may matter even more than individual characteristics. If that interpretation holds, then improving digital leadership is not solely a matter of recruiting or training particular individuals; it requires building institutional ecosystems in which capable leadership can flourish. A talented administrator placed in a school without infrastructure, support, or policy backing may achieve far less than a modestly skilled one embedded in a digitally mature environment.

Based on the four profiles, the research team recommends differentiated professional development pathways rather than uniform training. For Hesitant Adopters, the priority is confidence-building: workshops and supportive experiences that reduce anxiety and build basic familiarity with digital tools. For Enthusiastic Implementers, who already possess drive and energy, the recommendation is advanced training in AI ethics and data governance, ensuring that their enthusiasm is channeled responsibly. Balanced Moderates would benefit from targeted development that pushes all three dimensions forward in tandem, while Comprehensive Experts might serve as mentors, change agents, and models within their systems. This profile-based approach reflects a broader trend in educational research toward precision in professional development—matching interventions to actual needs rather than applying the same curriculum to everyone.

The study’s implications extend well beyond Guangxi or even China. Education systems worldwide are racing to integrate AI into teaching and administration, often focusing their preparation efforts on teachers and students. This research argues that such efforts are incomplete without parallel investment in school leaders. Administrators set budgets, choose platforms, design policies, and shape institutional culture; if they lack practical digital capability, even the best teacher training and the most advanced tools may stall at the classroom door. The implementation gap identified here is therefore not merely a Chinese problem but a structural challenge for any education system undergoing rapid digital transformation.

“Preparing school leaders—not just teachers and students—is a critical piece of the puzzle of educational change in the age of AI,” concludes Professor Hui Li of The Education University of Hong Kong, the study’s senior author. The researchers call for policies that explicitly strengthen administrators’ practical capability so that AI can be harnessed for equitable, human-centered education rather than deployed haphazardly. As AI continues its rapid advance into schools, the Guangxi survey serves as a timely reminder that the success of educational technology ultimately depends less on the sophistication of the tools than on the readiness of the people who lead the institutions using them. Closing the implementation gap between positive attitudes and practical capability may prove to be one of the defining leadership challenges of the coming decade in education.

Subject of Research: Digital leadership preparedness of Chinese educational administrators for the AI era

Article Title: New study reveals an "implementation gap" in Chinese educational administrators' digital leadership for the AI era

Article References: New study reveals an "implementation gap" in Chinese educational administrators' digital leadership for the AI era. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: educational digital leadership, artificial intelligence in education, school administrators, implementation gap, ACC model, latent profile analysis, Guangxi, professional development, ECNU Review of Education, K-12 education, AI ethics, digital transformation

The evidence for this rupture is already visible in labor markets that were once considered immune to technological displacement. Major technology companies have sharply reduced their reliance on traditional academic credentials in hiring, while generative AI systems can supply expert-level knowledge and sophisticated problem-solving on request, capabilities that previously required years of formal training and professional experience. According to the article, these developments strike at a foundational assumption of modern credentialing: that educational qualifications remain reliable indicators of genuine competence. If a machine can draft legal analyses, write functional software, and synthesize medical literature, the signaling power of the degrees once attached to those tasks weakens considerably. Shao situates this shift within the intellectual tradition of five Nobel Prize-winning scholars whose theories of human capital, skill formation, signaling, and educational returns underpinned the modern economics of education. Each of those frameworks, the paper argues, rests on assumptions that AI is now disrupting simultaneously, a convergence that has no precedent in the history of educational thought.

The study identifies five interlocking assumptions that have silently governed education systems for decades. The first is linear returns to education, the belief that each additional year of schooling yields proportionally greater economic rewards. The second is stable skill depreciation, the notion that competencies erode at a predictable pace over a working lifetime. The third is individual ownership of capability, the assumption that productive skills reside within single persons. The fourth is stable signaling through credentials, the idea that diplomas reliably communicate ability to employers. The fifth is that education generates broadly positive externalities, benefiting society beyond the individual learner. Shao contends that AI unsettles all five at once. Labor markets increasingly reward people who cross critical thresholds in human-AI collaboration rather than those who simply accumulate more schooling years, turning smooth returns into step functions. Skills now depreciate in a handful of years as AI capabilities advance. Productive capability is distributed across human-machine systems rather than contained in any one mind.

The disruption extends into territory that conventional economics has barely mapped. Credentials lose their signaling reliability precisely because AI can perform many of the tasks associated with credentialed expertise, forcing employers to reassess what a degree actually certifies. Meanwhile, educational externalities are becoming structurally more complicated. The knowledge and experience that educators and professionals contribute can now be used to train AI systems whose benefits are captured by technology platforms rather than by the people and institutions that supplied the underlying data and expertise. In economic terms, the positive spillovers of education are being privatized at scale. This asymmetry, the paper suggests, demands a new analytical category, because human capital theory was never designed to describe a world in which the productive output of learning can be extracted, aggregated, and resold by third parties. It is in response to this compounded breakdown that Shao proposes Intelligence Surplus as the new central concept for the economics of education, one suited to an era in which intelligence itself has ceased to be the binding constraint on production.

Intelligence Surplus is defined as a society’s accumulated reserve of intelligent capability beyond its basic operational needs, a pool of capacity that can be mobilized for innovation, adaptation, and institutional development. The crucial conceptual move is a shift of scale. Human capital is primarily an attribute of individuals, measured in earnings premiums and years of schooling. Intelligence Surplus, by contrast, is a societal and systemic resource, closer to infrastructure or public wealth than to personal property. To give the concept analytical texture, the author draws on Pierre Bourdieu’s influential theory of cultural capital and distinguishes three forms of Intelligence Surplus. The embodied form encompasses human capacities for collaborating with AI and navigating complex human-machine environments. The objectified form is represented by technical systems such as AI models themselves. The institutionalized form consists of governance systems, rules, standards, and accountability frameworks that shape how intelligent technologies are deployed. Treating these three forms as a coherent stock of societal intelligence reframes education’s task from producing workers to stewarding a shared resource.

If intelligence is no longer scarce, the paper asks, what has become scarce instead? Shao’s answer is a capacity he calls meaning-translation: the ability to turn unique human experiences, judgments, and values into forms that can interact productively with AI systems. To describe the people who perform this function, the article introduces the concept of the First Compressor, individuals who translate irreplaceable human experiences into representations that broader intelligent systems can understand and process. These translators occupy a strategically novel position in the economy, because their work is difficult to automate. It depends on human relationships, care, interpretation, and judgment, all of which resist the pattern-extraction methods that make machine intelligence so powerful elsewhere. The First Compressor concept also carries a critical edge: it implies that the most valuable human contribution in an AI-saturated economy is not raw analytical power, which machines now supply abundantly, but the articulation of human meaning that gives that power direction. Education systems that ignore this shift risk optimizing students for capabilities the market no longer prices.

These ideas lead to a systematic rethinking of education’s purpose. Rather than primarily preparing workers with marketable skills, education is presented as the social mechanism through which societies create, sustain, and renew their Intelligence Surplus. Shao therefore proposes understanding education as a form of intelligent infrastructure, an institution whose role is to support the creation, circulation, governance, and renewal of intelligent capabilities across society, much as energy grids support economic activity. The paper sketches this transformation at three interconnected levels. At the individual level, education should cultivate dual literacy, bringing technological literacy together with humanistic literacy so that learners can operate machines without surrendering interpretation to them. At the organizational level, schools and universities should become environments that support meaningful human-AI collaboration and lifelong learning, replacing the front-loaded degree model with continuous renewal. At the societal level, governments should build educational systems deliberately designed to function as intelligent infrastructure, coordinated with governance frameworks rather than siloed as vocational pipelines.

The paper ultimately looks beyond economic productivity to a classical philosophical question: what is education actually for? Drawing on Hannah Arendt’s distinction between labor, work, and action, Shao argues that education’s deeper purpose is to cultivate people capable of exercising judgment, caring for others, creating meaning, and acting responsibly within society. In Arendt’s terms, as machines absorb more of the routine cognitive work that constitutes labor and even skilled work, the distinctly human capacity for action, the unpredictable, value-laden engagement with other people and the public world, becomes not less important but more central to the purpose of education. This reframing has concrete stakes. If policymakers continue to measure education solely by earnings returns to credentials in a labor market where credentials no longer signal competence, they risk dismantling institutions whose real function is the maintenance of societal intelligence. If they adopt the Intelligence Surplus framework instead, education funding, curriculum design, and AI governance become part of a single policy question: how to build the infrastructure that keeps human judgment, care, and meaning-making viable in a world where answers are cheap and good questions are not.

Subject of Research: Economics of education and artificial intelligence: the shift from human capital theory to Intelligence Surplus

Article Title: When intelligence is no longer scarce, what comes after human capital?

Article References: When intelligence is no longer scarce, what comes after human capital?. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: artificial intelligence, economics of education, human capital theory, Intelligence Surplus, credentials, labor markets, ECNU Review of Education, AI literacy, human-AI collaboration, educational infrastructure, meaning-translation capacity, Hannah Arendt

The study, titled ‘Design and Implementation of Personalized Teacher Professional Development: A Case of an NGO-Based School System in Pakistan’, examines a professional development model implemented across 25 NGO-based schools in two regions of the country. Rather than delivering identical training packages to all teachers regardless of their experience, subject area, or classroom context, the programme began by identifying what individual teachers actually needed to learn. On the basis of those identified needs, targeted audio-visual modules were developed, covering areas such as formative assessment, classroom questioning techniques, and differentiated instruction. Teachers then accessed these modules through audio-visual studios established within the schools themselves, a design choice that placed professional learning resources physically close to the point of practice.

The technical architecture of the model is worth examining because it departs from the mobile-first or fully online formats that dominate discussions of digital education. Instead of relying on personal devices and internet streaming, the programme created shared, school-based AV studios where teachers could watch and work with audio-visual learning resources. This design acknowledged the infrastructure realities of resource-constrained settings: connectivity can be intermittent, electricity supply can be unreliable, and not every teacher owns a suitable device. By concentrating the technology in a dedicated school space, the model gave teachers a reliable venue for self-paced learning while also creating a natural site for peer collaboration. Teachers could revisit materials as often as they needed, a flexibility that conventional workshops, by their very nature, cannot offer.

Crucially, the digital resources were never intended to stand alone. The programme combined self-paced engagement with audio-visual content against a backdrop of practice, reflection, peer feedback, and mentoring. School-level facilitators, including school leaders and programme implementation personnel, played an active role in helping teachers connect what they encountered in the modules with their everyday classroom routines. This pairing of asynchronous digital content with synchronous human support reflects a growing consensus in the professional development literature: sustained, practice-embedded learning tends to outperform isolated training events, and the presence of a knowledgeable other can make the difference between passive consumption of content and genuine change in instructional behaviour.

To understand how the model worked in practice, the researchers adopted a case study design, interviewing key school-level stakeholders including programme developers, implementers, school leaders, and teachers. Programme documents were also examined in order to reconstruct the processes of design and implementation and to capture participants’ experiences of the model. This methodological choice matters. Case studies cannot establish causal effects, and the authors are explicit on this point: the study does not claim to demonstrate that the programme improved teaching quality in any measurable, causal sense. What it does provide is rich evidence of how a personalised, technology-supported approach to professional development can actually be designed and implemented under resource constraints, and of the conditions required to keep such professional learning alive over time.

The findings from the interviews paint a encouraging picture of teacher engagement. Teachers reported using more interactive instructional strategies in their classrooms, adjusting their questioning practices, and changing the pacing of their lessons. They described returning independently to the audio-visual materials to revisit concepts, and they spoke of becoming more reflective about their own teaching. These are precisely the kinds of behavioural shifts that professional development programmes aspire to produce but often fail to document. Equally significant was the role attributed to the implementers, who helped teachers interpret the digital content and translate it into classroom-appropriate actions. Without that interpretive layer, the study suggests, the modules risked remaining abstract resources rather than becoming catalysts for changed practice.

The challenges teachers encountered were neither trivial nor surprising. Internet connectivity was inconsistent, electricity supply was unreliable, access to equipment was uneven, and some teachers faced long travel to reach schools equipped with AV studios. These obstacles are endemic to technology-based interventions in developing contexts, and the study does not minimise them. Yet the most important insight to emerge from teachers’ experiences was not about infrastructure at all. It was that the technology became meaningful only when it was combined with dialogic professional learning, that is, with interaction among colleagues and with mentors. The technology itself was not the transformative element. Its integration with professional dialogue, mentoring, and collaborative reflection was what strengthened teachers’ engagement and sustained their learning.

This conclusion carries a caution that extends well beyond Pakistan. Enthusiasm for digital solutions in education frequently outruns the evidence, and funders and policymakers are often tempted to equate the provision of devices and content with the achievement of professional growth. The study pushes back against that equation directly, arguing that technology alone is unlikely to transform teacher professional development. Its potential, the authors contend, is far greater when it is embedded within a human-centred system of professional support. The practical implication is a question of balance: digital resources can extend reach, enable self-pacing, and allow repeated access to content, but they require sustained human support to be interpreted, contextualised, and converted into durable changes in teaching.

The wider relevance of the findings lies in what the study offers to other education systems searching for alternatives to one-off, standardised teacher training. The authors are careful not to suggest that the Pakistani model can simply be transplanted elsewhere. Instead, they argue that its underlying principles are what merit adaptation: identifying teachers’ learning needs before designing provision, providing flexible access to professional learning resources, supporting structured reflection, and maintaining local mentoring relationships. These principles, they suggest, could be adapted to different institutional and resource contexts, whether in other developing education systems or in settings where conventional training has similarly failed to engage teachers meaningfully. The emphasis on principles over procedures is a deliberate methodological stance, recognising that the specific configuration of studios, modules, and facilitators emerged from a particular set of local conditions.

The study closes with a sober assessment of what expansion would require. Education systems in developing contexts, the authors conclude, are fully capable of generating contextually grounded approaches to teacher development rather than importing models designed elsewhere. But successful scaling is not automatic. It demands careful planning, reliable infrastructure, continued mentoring, institutional commitment, and sustained investment in both people and technology. In an era when artificial intelligence platforms and mobile learning apps promise to personalise education at scale, this case study from 25 Pakistani schools offers a grounded counterpoint: personalisation in professional learning is less about the sophistication of the technology than about the quality of the human relationships and institutional structures that surround it. For policymakers, programme designers, and researchers interested in teacher development under resource constraints, the message is clear. Build the studios, yes, but build the mentoring around them, because it is the combination, not the hardware, that changes how teachers teach.

Subject of Research: Personalised, technology-supported teacher professional development in resource-constrained Pakistani schools

Article Title: A study from Pakistan offers contextual insights into Personalized Teacher Professional Development

Article References: A study from Pakistan offers contextual insights into Personalized Teacher Professional Development. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: teacher professional development, Pakistan, audio-visual studios, personalised learning, mentoring, ECNU Review of Education, case study, education technology, resource-constrained education, NGO schools, formative assessment, teacher training

The study was conducted by Professor Ge Wei and Professor Haiyan Wang of Capital Normal University in Beijing, who examined a service-learning program at a Chinese normal university. The program emerged in direct response to China’s “double reduction” policy, a national initiative designed to ease the burden of homework and after-school tutoring on schoolchildren. Within the program, pre-service teachers designed and delivered online courses to schoolchildren across the country, working under the guidance of experienced mentors. The setting mattered: rather than a brief, isolated teaching placement, the participants were embedded in an extended, socially consequential service activity with continuous institutional support.

Theoretically, the researchers grounded their analysis in activity theory, a framework that treats human learning as mediated by tools, communities, and social structures rather than as a purely individual mental achievement. They then added a distinctly Chinese philosophical layer. “We invoke the ancient Chinese concept of Yin and Yang (阴阳) as a metaphor for the dialectical relationship between practice and reflection in teacher education,” Wei and Wang explain. “It offered culturally grounded resources for rethinking teacher learning beyond dualism.” In this framing, practice and reflection are not opposing poles to be balanced but complementary aspects of one activity, each containing and generating the other, much as the yin and yang of traditional Chinese thought interpenetrate rather than exclude one another.

One of the study’s central findings is a critique of how the theory–practice divide has historically been understood. The authors found that the problem has typically been framed as an individual, psychological failing — a deficit in the student teacher’s knowledge, skills, or ability to “apply” theory. Their analysis reframes it instead as a collective, socio-cultural problem. On this view, the divide persists because the activity systems surrounding novice teachers — universities, schools, policies, and digital infrastructures — are not designed to let theory and practice transform each other. Fixing it therefore requires redesigning those systems, not simply exhorting student teachers to reflect harder.

The Chinese context proved unusually fertile for such a redesign. According to the study, the “double reduction” policy, combined with university–school collaboration and digital mediation, created what the researchers describe as a fertile ecology for dialectical unity to emerge. The national policy supplied purpose and urgency; the university–school partnership supplied mentors and authentic classroom connections; and the online delivery format supplied a mediating tool through which pre-service teachers could reach real learners at scale. Each element alone would have been insufficient. Together, they formed an activity system in which teaching practice and theoretical reflection could feed continuously into one another.

The most striking result concerns what happened to the pre-service teachers themselves. The study reveals that when novices engage in service learning guided by mentors and peers, their practice and reflection become mutually transformative rather than sequentially separated. Instead of first learning theory and then applying it, the participants reworked their theoretical knowledge in light of real classroom encounters and generated new professional understanding in the process. Reflection was not a post-hoc journaling exercise appended to teaching; it was woven into the act of designing, delivering, and revising instruction for actual children with actual needs. Theory, in turn, was not a fixed script but a living resource that the novices reshaped as they taught.

From these findings, the authors draw three practical implications for initial teacher education programs worldwide. First, programs should stop treating theory as something to be simply applied to practice. Well-designed service learning creates opportunities for the two to transform each other, as novices confront the gap between their assumptions and classroom reality and rebuild their professional understanding accordingly. Second, continuous mentoring and peer collaboration are essential. In the studied program, participants designed, rehearsed, and revised their teaching together rather than working in isolation, making the collective — not the lone student teacher — the basic unit of professional learning. Third, service learning can cultivate a sense of professional responsibility to society: by engaging in a national policy initiative and serving schoolchildren across the country, the pre-service teachers came to see teaching not merely as a job but as a socially meaningful commitment.

Taken together, these principles point to a conclusion that challenges a common policy reflex. Bridging the theory–practice divide, the authors argue, requires more than increasing the number of practicum hours. What matters is the design of authentic, socially consequential activities in which pre-service teachers can practice, reflect, and grow with the support of mentors, peers, and meaningful mediating tools. Hours spent in poorly structured placements, where novices are dropped into classrooms without collective inquiry or genuine purpose, may reproduce the very divide they are meant to close. The quality of the activity system, not the quantity of exposure, is the decisive variable.

The study also stakes out a broader ambition for the field. The authors propose a critical activist role for initial teacher education in transforming societies, a perspective that resonates with growing international calls for more equitable and socially engaged teacher preparation. On this account, teacher education should not merely reproduce the existing schooling system but equip future teachers to question and improve it — and service learning, by tying professional learning to real societal needs, offers one concrete mechanism for doing so. The Chinese program’s alignment with the “double reduction” policy illustrates how national priorities, when paired with university infrastructure, can become a vehicle for socially engaged professional formation rather than a top-down mandate alone.

“The making of high-quality teachers depends on both a sustainable policy ecology and locally grounded spaces in which practitioners can collectively redesign the practices of teacher education,” Wei and Wang conclude. The claim is a pointed one for policymakers and deans alike: high-quality teaching cannot be manufactured by curriculum documents and exam requirements alone. It requires an ecology — stable policies, collaborative institutions, digital tools, and communal spaces — in which the ancient dialectic of doing and thinking can operate as a unity. For a profession long divided between the seminar room and the classroom, the message from Beijing is that the bridge between the two is not built by adding more of either side, but by redesigning the activity that connects them. The authors declared no potential conflicts of interest with respect to the research, authorship, or publication of the article, which appeared in ECNU Review of Education under the title “Practice and Reflection as a Dialectical Unity: Pre-Service Teachers’ Experience in a Service-Learning Program.”

Subject of Research: Service learning as a means of integrating theory and practice in pre-service teacher education

Article Title: Chinese study reveals how service learning bridges the theory–practice divide in teacher education

Article References: Chinese study reveals how service learning bridges the theory–practice divide in teacher education. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: teacher education, service learning, pre-service teachers, theory-practice divide, activity theory, yin and yang, double reduction policy, Capital Normal University, ECNU Review of Education, mentoring, reflection, China