Using digital technologies for Indigenous sociocultural advancement in an era of AI: A systematic critical synthesis
Published in Preservation, Digital Technology & Culture (Online First), 2025
This paper examines how digital technologies are mobilized in Indigenous advancement projects and asks a functional question: what are these projects doing, and to what end? Using systematic review methods and critical synthesis, the study analyzes initiatives ranging from digital heritage archives to language revitalization platforms to map the types of capability these systems cultivate. Findings show a consistent pattern: most projects optimize for cultural preservation and symbolic affirmation, while fewer develop transferable problem solving or design capacities that support long term innovation. This is not a critique of cultural work, but a diagnostic observation showing how digital infrastructures often become vehicles for expressing identity more than exercising adaptive intelligence. Framed through an activity system lens, the study situates digital Indigeneity within the broader question of how human collectives learn and adapt in the age of AI. For non specialists, the takeaway is simple: technology is not only a mirror for culture, it is also a medium for cultivating capability, the difference between keeping a tradition alive and making it think.
Optimism, interest and gender equality: comparing attitudes of university students in Latvia and Ukraine toward IT learning and work
Published in Compare: A Journal of Comparative and International Education, 52(6), 895–913, 2020
This study captured a generation's mindset just before artificial intelligence redrew the boundaries of technical work. A thousand students in Latvia and Ukraine were asked how they felt about information technology—whether it inspired optimism, anxiety, or genuine curiosity, and how those feelings intersected with gender and equality. The patterns are strikingly relevant today. The data revealed two different technological climates. Ukrainian students, surrounded by rapid economic and infrastructural change, leaned toward optimism: technology was seen as a route to reinvention. Latvian students, situated in more mature systems, were more cautious, attuned to the social costs of digital acceleration. Men showed greater self-declared interest in learning IT, while women expressed comparable confidence about future workplace equality—a latent readiness still waiting for the right learning pathways. Viewed through the lens of today's AI transformation, these attitudinal differences read like an early topology of adaptation. Optimism and curiosity predict agency in the age of automation; anxiety and social concern describe the limits of inclusion when systems evolve faster than people can re-skill. What this research exposed, before most saw it coming, is that intelligence begins as disposition: how a society feels about technology often decides how intelligently it will use it.
Prepared for work in Industry 4.0? Modelling the target activity system and five dimensions of worker readiness
Published in International Journal of Computer Integrated Manufacturing, 34(1), 1–19, 2020
This paper set out to model what readiness meant as work entered the Industry 4.0 transition. Using Activity Theory as its backbone, it maps the workplace as an interacting system of motives, mediating tools, and shared objectives. From this model emerges five dimensions of readiness—technical skill, social collaboration, motivation, metacognition, and contextual understanding—each describing a way humans learn to align intention with technological affordance. The research combines conceptual modelling with cross-disciplinary synthesis, illustrating how automation was already transforming not only what workers do, but how they think about doing. Its diagrams of interacting subsystems anticipate the fluid boundaries that now define human–AI work. In 2020, the intelligent machine was still a programmable artifact. Today, it is a semi-autonomous actor. What the paper calls metacognitive readiness—reflecting on one's tools—has become reflection with one's tools. Re-read in the current era, the study's activity-system model serves as an early framework for understanding functional complementarity. It reminds us that intelligence in the workplace has always been relational: every new machine invites a new form of agency. The enduring task is to ensure that as machines become more adaptive, humans remain the ones orchestrating purpose within the system.
Exploring technology attitudes and personal–cultural orientations as student readiness factors for digitalised work
Published in Higher Education, Skills and Work-Based Learning, 11(3), 649–671, 2020
Across Eastern Europe, digitalisation arrives not as a uniform tide but as a cultural negotiation. In Latvia and Ukraine, young professionals grow up amid competing habits of hierarchy, risk, and autonomy. The research follows these undercurrents, tracing how optimism, anxiety, and learning interest toward technology intertwine with inherited cultural codes: individualism and collectivism, tolerance for ambiguity, power distance, and decision-making empowerment. The patterns it reveals feel familiar today. Curiosity flourishes where social distance shrinks and ambiguity is tolerated; anxiety dominates when authority is fixed and uncertainty avoidance is high. Ukrainian participants, shaped by fluid institutions and rapid change, show greater optimism and empowerment. Latvians, inheriting steadier systems, lean toward caution and structure. The contrast exposes not technological inequality but divergent cultural grammars of adaptation. These grammars still define who learns with intelligent systems and who waits for instruction. Cultures that valorize experimentation invite partnership with technology; those organized around predictability treat intelligence as external control. The study's quiet lesson endures: digital transformation succeeds not when tools advance, but when culture evolves fast enough to make them think with us.
Theorising effective uses of digital technology with activity theory
Published in Technology, Pedagogy and Education, 28(4), 447–462, 2019
Digital technology is never just a tool; it is a living participant in human activity. This study rebuilds the concept of effective use from the ground up, using activity theory to describe how people and technologies co organize action. From this scaffolding, a grammar of digitally mediated activity takes shape, outlining six structural elements and seven human machine dynamics that together constitute the modern ecology of intelligence. Within that grammar, four subsystems unfold. Functional systems describe how humans and tools form hybrid agencies that extend capability. Cultural mediation explains how digital technologies reshape what counts as expression, from the private act of thought to collective creation. Automation traces how operations migrate between humans and machines, revealing intelligence as a circulating property rather than a possession. Collective activity explores the architectures of collaboration where communication, trust, and distributed cognition decide whether a group thinks as one or fragments into silos. At its core, the paper treats Leontiev's and Engeström's systems as dynamic laboratories in which agency, trust, and transformation continuously renegotiate each other. What emerges is not a model of technology use, but an early theory of intelligent participation, a description of how humans and machines learn to act together in purpose driven systems.
Surveying digital competencies of university students and professors in Ukraine for fully online collaborative learning
Published in Technology, Pedagogy and Education, 27(3), 279–296, 2018
Before digital learning becomes an ecosystem, it begins as an experiment in shared presence. In post revolutionary Ukraine, this research tests whether the social and cognitive habits required for collaborative online learning exist in the first place. Using the General Technology Competency and Use (GTCU) framework, 244 students and professors map their experience and confidence across four dimensions of human computer activity: technical, social, informational, and epistemological, relating these to the three presences of the Community of Inquiry model: social, cognitive, and teaching. The portrait that emerges is transitional. Ukrainian students and professors show moderate technical and social competence but low epistemological capacity, the ability to think with technology rather than merely through it. They communicate, search, and manage information effectively, yet struggle with conceptual modeling, collaboration, and digital authorship. These gaps reveal an educational culture still oriented around content delivery rather than knowledge construction. What the study uncovers is a map of readiness for intelligent learning systems before such systems exist. It shows where the human side of the circuit is still forming, trust, self expression, and cognitive presence lagging behind infrastructure. The finding endures as a diagnostic principle, digital transformation succeeds only when technical, social, and epistemological competencies converge into a single, intelligent practice of learning together.
Re-examining Digital-Learning Readiness in Higher Education: Positioning Digital Competencies as Key Factors and a Profile Application as a Readiness Tool
Published in International Journal on E-Learning, 17(4), 425–451, 2018
Long before AI transforms digital learning, this research asks a deceptively simple question: are we ready? In a field obsessed with access and infrastructure, readiness has come to mean bandwidth and hardware. This paper reframes it as human configuration, the ability of teachers, students, and institutions to act intelligently within technology rich environments. Through a critical review of seventy six international studies, the analysis exposes a deep flaw: readiness tools measure attitudes and access but not the competencies that actually shape performance. To fix that, the work introduces the General Technology Competency and Use (GTCU) framework and its online Digital Competency Profiler (DCP), a model that connects how often people engage in digital activity with how confidently and conceptually they do it. A pilot performance study comparing self reports with observed digital learning behavior shows that confidence and practice predict genuine capability and reveal a threshold below which learners risk failure. In effect, the study creates an early diagnostic of human machine alignment in education. What begins as an assessment tool becomes a mirror for the cultural and cognitive conditions of intelligent work, reminding us that digital transformation is never about the tools alone but about the readiness of minds to think, build, and learn through them.
Ready for Digital Learning? A Mixed-Methods Exploration of Surveyed Technology Competencies and Authentic Performance Activity
Published in Education and Information Technologies, 23(3), 1061–1084, 2018
Inside the Educational Informatics Laboratory at Ontario Tech, a new kind of experiment takes shape, one that treats learning as observable human machine coordination. Fifteen participants enter a purpose built recording environment surrounded by sensors, dual cameras, and data capture tools. Their task seems simple, perform authentic digital learning activities using a tablet. Beneath that surface, the study probes a deeper question, how do self perceptions of digital competence align with the fluid realities of intelligent action. By pairing the Digital Competency Profiler (DCP) survey with multi stream video analysis, the research captures both what participants believe they can do and what they actually do in motion. Some move with fluid confidence, others hesitate, their problem solving strategies revealing cognitive, motivational, and emotional currents invisible to conventional measurement. Across scenarios, high self efficacy often predicts strong performance, but situational factors, fatigue, frustration, device familiarity, and persistence, reframe competence as a dynamic negotiation between intention and environment. The result is an early map of readiness for intelligent systems, a threshold model that distinguishes not simply skill from ignorance, but readiness from strain. What it documents is a new form of literacy, one expressed not in what learners know, but in how they improvise with technology when the system starts to think back.
Profiling the Digital Readiness of Higher Education Students for Transformative Online Learning in the Post-Soviet Nations of Georgia and Ukraine
Published in International Journal of Educational Technology in Higher Education, 15(37), 2018
Across Eastern Europe, education remains one of the few spaces where cultural inheritance and technological ambition still meet on equal terms. This study follows that encounter in Georgia and Ukraine, where students are reimagining learning amid shifting politics, persistent inequities, and accelerating automation. Using the Digital Competency Profiler, it measures how 279 university students navigate digital tools across four domains: technical, communicational, informational, and computational, interpreting these patterns as signs of a deeper cultural choreography. Georgian students display strength in technical and computational dimensions, grounded in a pragmatic engineering ethos that blends precision with resilience. Ukrainian students show fluency in communicational and informational use, reflecting an evolving public sphere where digital expression has become both creative practice and civic defense. Yet both groups reveal the same structural fragility: limited confidence in data reasoning and collaborative inquiry, the very capacities now required to coexist with intelligent systems. In an era of AI acceleration and renewed conflict, these profiles speak to more than readiness for online learning; they map the conditions of cognitive sovereignty. They show how education, even under strain, becomes a field of quiet resistance and renewal. Technology here is not a foreign inheritance but a local instrument for thinking, surviving, and rebuilding the social fabric, proof that intelligence, human or artificial, grows strongest where cultures refuse to stop learning.
Democratizing Digital Learning: Theorizing the Fully Online Learning Community Model
Published in International Journal of Educational Technology in Higher Education, 14(13), 2017
Long before artificial intelligence becomes the new engine of pedagogy, this work redefines what it means to learn together through machines. Conceived in the Educational Informatics Lab at Ontario Tech, the Fully Online Learning Community (FOLC) model challenges the inherited hierarchies of online education, replacing the teacher as broadcaster and student as receiver with a dynamic ecology of shared cognition. The model’s premise is deceptively simple, democracy is not taught, it is practiced. In a FOLC, learners co create digital spaces, negotiate tools, and distribute cognitive authority. Social and cognitive presences merge through synchronous and asynchronous collaboration, turning the online classroom into a living network of mutual accountability. The professional educator becomes an orchestrator rather than a director, and knowledge emerges as a collectively constructed artifact, open to challenge and refinement. Seen from the AI era, FOLC reads like an early blueprint for intelligent systems that learn with humans, not merely for them. It anticipates today’s debates about autonomy, agency, and machine mediation by grounding them in democratic pedagogy. The paper’s enduring insight is that learning technologies do more than transmit information, they model how societies think, deliberate, and evolve. Every truly collaborative online space is, at heart, a rehearsal for collective intelligence.