Publish at May 06 2026 Updated May 06 2026
Going fast has become a norm, an expectation, an almost indisputable injunction. In professional environments and training systems alike, speed is now associated with efficiency, performance and even competence itself. Being able to process information quickly, to respond without delay, to adapt continuously to changing flows: these are all qualities that are valued, encouraged and sometimes even demanded.
Digital technology has not only accompanied this transformation: it is one of its main gas pedals. Learning platforms, online resources, instant messaging, collaborative tools, generative artificial intelligence... contemporary learning environments are structured by an implicit promise: to learn faster, more efficiently, with less apparent effort. Immediate access to information, the permanent availability of content and the multiplication of media seem to lift the traditional constraints on learning time.
But this acceleration comes at a cost... a cost that is often invisible, rarely objectified, and even less thought of as a pedagogical issue in itself.
Learning is not just a question of access to information. It's a complex, demanding cognitive process, requiring attention, memory, reasoning capacity and, above all, time. Time to understand, connect, consolidate and transfer. Today, however, this time is tending to shrink, squeezed by environments saturated with demands and by training systems themselves caught up in the logic of optimization.
Learners evolve in a dense, fragmented and often discontinuous universe of information. Notifications, multiple resources, fragmented instructions, varied media: cognitive activity is no longer linear, but fragmented. Multitasking, long considered a skill, is becoming an implicit norm. It's no longer just a question of learning, but of learning at the same time as consulting, responding, navigating, comparing and anticipating.
Against this backdrop, a central question emerges, and it deserves to be asked straightforwardly: can we still learn effectively in an environment that constantly demands our cognitive capacities?
The cognitive sciences provide particularly enlightening answers to this question. For several decades now, they have been describing the structural limits of our cognitive system, and in particular those of working memory, the restricted space in which information is consciously processed. When the quantity of information to be processed exceeds its capacity, a phenomenon of cognitive overload occurs, leading to a deterioration in comprehension, memorization and decision-making.
However, everything points to the fact that today's digital environments do more than simply exploit these capacities: they tend to saturate them. The abundance of resources, the speed of interactions, the multiplicity of information channels, combined with a permanent injunction to react, create the conditions for chronic cognitive overload. This overload does not always manifest itself spectacularly, but it has a profound effect: attention fatigue, concentration difficulties, the impression of learning without really understanding, a feeling of efficiency that does not translate into lasting competence.
The paradox is striking. Never have learners had access to so much information, so many tools, so many resources for learning. And yet, the quality of learning has never been so critical. Apparent efficiency - speed of access, fluidity of interaction, accelerated production - sometimes masks a weakening of fundamental cognitive processes.
In the specific field of healthcare training, this tension takes on a particularly acute dimension. For it's not just a question of learning fast, but learning right. Understanding in depth. Developing reliable clinical reasoning, capable of withstanding pressure, uncertainty and urgency. In these contexts, cognitive overload is not just a discomfort: it can become a risk factor.
Should we slow down? Should we return to more linear, less technological, "slower" pedagogical models? The answer is neither simple nor unequivocal. Acceleration also brings opportunities: greater access to knowledge, personalized learning paths, more responsive systems. Digital technology is not in itself the problem. It becomes problematic when it is seen solely as a lever for optimization, without consideration for the cognitive constraints it imposes.
The challenge, then, is not to pit speed against quality, but to understand the conditions under which acceleration can remain compatible with quality learning. In other words, how can we design learning environments that take account of human cognitive limitations, while exploiting the potential of digital technology?
This article explores this tension from two angles. On the one hand, by mobilizing the contributions of cognitive science to understand the mechanisms of overload and their effects on learning. Secondly, by analyzing digital environments as systems that structure, and sometimes constrain, learners' cognitive activity.
The aim is not to produce a technophobic critique, nor to give in to a technological fascination, but to set out a demanding framework for analysis: that of a pedagogy that takes seriously the question of time, attention and cognitive load.
Ultimately, it's less a question of knowing whether we're going fast - we are, and will continue to do so - than of understanding the cognitive cost of this speed, and the conditions under which it can remain compatible with learning.
The acceleration of learning rates cannot be understood in isolation from the environment in which it takes place. What is at stake today goes far beyond the simple question of the volume of content or the time available: it is the very nature of the cognitive ecosystem in which learners evolve that has profoundly changed.
Today's digital environments are not simply learning media. They are cognitive environments in their own right, structured by their own logic: instantaneity, permanent accessibility, multiplicity of sources, continuous interactivity. These characteristics profoundly redefine the conditions in which attention is deployed, information is processed, and knowledge is constructed.
One of the major transformations is the explosion of information flows. Where learning used to be based on relatively stable sequences - a course, a medium, a dedicated time - it now takes place in an environment saturated with solicitations.
Learners navigate between platforms, resources, notifications, synchronous and asynchronous exchanges, sometimes within the same learning activity. This density of information, far from being neutral, requires constant mobilization of the ability to select, sort and prioritize.
In this context, attention becomes a critical and fragile resource.
Contrary to what is still widely believed, attention is not infinitely extensible. It is limited, costly, and subject to dispersion as solicitations multiply. Yet digital environments are precisely designed to capture and maintain this attention, often at the cost of fragmenting cognitive activity.
The rapid transition from one task to another, encouraged by the very architecture of tools (multiple tabs, notifications, hyperlinks), establishes a form of discontinuity that is gradually becoming the norm.
In this context, multitasking appears less as a skill than as a forced adaptation to a saturated environment.
Yet cognitive science research is unambiguous: multitasking, understood as the simultaneous performance of several cognitively demanding tasks, is largely illusory. What we perceive as the ability to "do several things at once" actually corresponds to a rapid alternation between different tasks, each mobilizing specific cognitive resources.
This alternation has a cost: loss of concentration, increased processing time, reduced quality of performance.
Applied to learning, this phenomenon produces particularly deleterious effects. Fragmented attention prevents the deep cognitive processes required for comprehension and memorization. Information is processed more superficially, links between concepts are less consolidated, and the ability to mobilize knowledge in situations is impaired.
This fragmentation is compounded by another structuring characteristic of digital environments: the hyper-accessibility of information.
In a matter of seconds, it is possible to access an almost unlimited quantity of resources. This accessibility is undeniably a major step forward. But it is also changing our very relationship with knowledge. When information is always available and can be mobilized immediately, the need to memorize, structure and integrate it may seem secondary.
The cognitive effort tends to shift: it's no longer so much a question of understanding as of finding information quickly.
This shift is not without consequences. It can foster an illusion of competence, based on the ability to access information rather than the ability to master it. Learners may feel they understand because they recognize information, or because they know where to find it, without being able to explain, mobilize or adapt it.
The recent introduction of generative artificial intelligence tools further accentuates this dynamic. By producing structured, synthetic and immediately usable answers, these tools considerably reduce the time needed to access relevant information.
But they also raise an essential question: what happens to the learner's cognitive activity when the answer has already been constructed?
The risk is not so much the disappearance of effort as its transformation. The learner does not disappear from the process, but his or her role evolves: from being a producer of knowledge, he or she tends to become an evaluator, a selector, and sometimes simply a validator of the content generated.
This evolution can be fruitful, provided it is accompanied. But it can also reinforce the trend towards the externalization of cognitive processes, which is already well underway in digital environments.
A paradoxical environment is thus taking shape. On the one hand, ever more powerful tools, capable of facilitating access to information, supporting learning activity and saving time. On the other, an intensification of demands, a fragmentation of attention, and a transformation of the relationship to knowledge that can weaken fundamental cognitive processes.
This paradox calls for particular vigilance. It's not simply a question of changing tools, but of changing cognitive regimes. Learning in an accelerated digital environment does not mobilize the same resources, does not rely on the same dynamics, and does not expose us to the same risks as learning in a more linear, less saturated environment.
This raises the question: if these environments are profoundly transforming the conditions of learning, what precisely are the cognitive limits they are coming up against? In other words, at what point does the accumulation of information and stimuli cease to be a lever and become an obstacle?
This is the question that the theory of cognitive load answers, providing a particularly relevant framework for understanding the mechanisms at work.
To understand the effects of digital acceleration on learning, we need to return to a fundamental principle: the human brain's capacity to process information is limited.
Cognitive load theory, developed by John Sweller in particular, offers a particularly relevant framework for understanding these limits. It is based on an essential distinction between three forms of load that are exerted simultaneously in any learning situation:
The pedagogical challenge is not to reduce the cognitive load as such, but to control its distribution. In other words, it's a question of limiting anything that unnecessarily diverts cognitive resources (extrinsic load), so as to enable the learner to fully mobilize his or her capacities on what is really learning.
At the heart of this theory lies working memory, the space in which the conscious processing of information takes place: understanding, reasoning, comparing, deciding.
This memory has well-established characteristics. Its capacity is severely limited, both in terms of the number of elements and the length of time they can be retained. It is also particularly sensitive to interference. As soon as several items of information enter into competition, or attention is called upon in a dispersed manner, the quality of cognitive processing deteriorates rapidly.
In structured teaching environments, these limitations can be compensated for by appropriate didactic choices: progression of content, segmentation of information, repetition and explanation. But in environments characterized by multiple demands and rapid interaction, these same limitations become a major factor of vulnerability.
Working memory does not adapt to acceleration. Learning suffers the effects.
Cognitive overload occurs when the resources available in working memory are exceeded. This phenomenon is not always spectacular. It often sets in gradually, almost imperceptibly, but its effects are profound.
When the cognitive load becomes excessive, comprehension becomes more fragile. Information is no longer processed in a sufficiently elaborate way to be integrated sustainably. The links between concepts remain superficial, and the ability to mobilize knowledge in new situations is limited.
Added to this is an even more insidious phenomenon: the illusion of understanding. The learner may have the feeling of following, recognizing or even mastering content, even though the processes of appropriation remain incomplete. This dissociation between perception and reality of learning is a central issue, all the more so as it makes cognitive overload difficult to identify, both for the learner and the trainer.
In today's digital environments, multitasking is one of the main vectors of cognitive overload.
Contrary to popular belief, multitasking is not the ability to process several pieces of information simultaneously, but the rapid alternation between different tasks. Each attentional switch involves an interruption in current processing, followed by an effort to reactivate it when it is resumed. This process generates what is known as a switching cost.
This cost, though often underestimated, is cumulative. It alters the continuity of reasoning, weakens memorization and lengthens the time needed to complete tasks. Above all, it hinders the development of the deep cognitive processes that are essential for sustainable learning.
Learning presupposes the ability to maintain stable attention, manipulate information, establish links and structure mental patterns. These operations are hardly compatible with fragmented and continuously interrupted cognitive activity.
One of the major paradoxes of cognitive overload is that it is often accompanied by a feeling of increased efficiency.
Digital environments offer a fluid, fast, responsive experience. Immediate access to information, the ability to navigate quickly between different sources, the sensation of "doing a lot" in a short space of time all contribute to reinforcing this impression.
However, this efficiency is largely superficial. It relies more on processing speed than on processing quality. And yet, learning is not about accumulating information, but transforming it into usable knowledge.
This discrepancy between perceived and actual efficiency is a powerful cognitive bias. It can lead people to overestimate what they have already learned, underestimate the need for consolidation, and favor rapid but shallow learning strategies.
All these factors point to a structural tension in contemporary learning environments. On the one hand, a logic of acceleration that values speed, fluidity and time optimization. On the other, cognitive processes that require stability, attentional availability and time for elaboration.
In concrete terms, this tension translates into a shift in cognitive resources. An increasing proportion of mental activity is mobilized to manage the environment itself - interfaces, demands, information flows - to the detriment of the learning activity itself.
This shift is far from trivial. It reduces learners' ability to engage in demanding cognitive processes, the very ones that enable the construction of lasting skills. In other words, by seeking to go faster, learning systems run the risk of compromising their primary purpose: the quality of learning.
As a result, the question can no longer be posed solely in terms of individual cognitive abilities. It refers directly to the design of learning environments themselves.
If cognitive overload is partly the result of the characteristics of digital devices, then it is not inevitable. It becomes a problem of pedagogical design.
We therefore need to question these environments for what they are: not mere tools, but systems that organize, guide and sometimes constrain learners' cognitive activity.
Digital learning environments are often designed with one overriding objective in mind: to facilitate access to information. Platforms, resource libraries, online modules, collaborative tools... everything is designed to make content available, accessible and consultable at all times.
This development is undeniably a step forward. It removes many logistical constraints, broadens access to knowledge and diversifies learning methods. However, it is based on an implicit assumption that deserves to be questioned: is making information accessible tantamount to promoting learning?
But these two dimensions do not overlap. Access to information is a necessary, but by no means sufficient, condition for learning. Learning presupposes the transformation of information into knowledge, which implies structured, progressive and guided cognitive work.
By multiplying resources without always structuring them, digital environments tend to shift responsibility for this organization onto the learner. The learner must not only understand the content, but also select relevant information, prioritize it and build overall coherence. All these operations mobilize significant cognitive resources, at the risk of increasing the extrinsic load.
One of the recurring features of digital devices is the progressive accumulation of resources. Video clips, documents, quizzes, forums, external links, complementary media: each addition is designed to enhance and enrich the learning experience.
But this additive logic produces a paradoxical effect. As the number of resources multiplies, the readability of the system deteriorates. The learner finds himself confronted with an increasingly dense environment, in which it becomes difficult to identify what is essential, what is secondary, what needs to be prioritized.
This complexity is all the more problematic in that it is often invisible from the designer's point of view. Each resource, taken in isolation, seems relevant. It is their unregulated juxtaposition that generates overload.
In this context, learners no longer devote their cognitive resources solely to learning, but also to orienting themselves within the device itself. This orientation activity, rarely anticipated as such, nevertheless constitutes a significant extrinsic load, competing directly with learning processes.
The introduction of generative AI tools into learning environments further reinforces this dynamic, while introducing new paradoxes.
At first glance, these tools appear to be powerful facilitators. They can synthesize content, produce explanations and generate tailored answers to complex questions. By reducing the time needed to access structured information, they appear to lighten the cognitive load.
But this simplification is partly illusory.
By providing pre-constructed answers, AI tends to short-circuit certain stages of reasoning. Learners are less encouraged to analyze, compare and formulate hypotheses. Their cognitive activity shifts towards validation, verification or selection tasks. This shift is not neutral: it modifies the very nature of cognitive engagement.
What's more, the abundance of possible answers, the variability of formulations and the absence of an explicit hierarchy can introduce an additional form of complexity. The learner must then assess the relevance of the content generated, cross-reference sources, exercise critical judgment... all cognitively demanding activities.
So, far from systematically reducing cognitive load, AI can help to reconfigure its distribution, sometimes to the detriment of knowledge construction.
Contemporary digital environments frequently claim the ability to personalize learning paths. Adaptation of content, targeted recommendations, adjustment of pace: these functionalities are presented as levers of pedagogical efficiency.
However, this personalization is often based on a logic of individualizing the choices left to the learner. The learner is invited to navigate multiple paths, select resources and build his or her own path.
While this freedom can be beneficial for certain autonomous profiles, it can also generate a significant cognitive load for others. Choosing means mobilizing resources. Deciding implies evaluating, comparing and anticipating.
When these decisions accumulate, they can lead to decision fatigue. The learner then finds himself confronted with a succession of micro-arbitrations which, taken in isolation, seem insignificant, but which, taken as a whole, contribute to overload.
Personalization thus becomes paradoxical: thought of as a lever for adaptation, it can turn into a factor of complexity.
All these elements lead to a central observation: digital environments are not simply neutral tools. They structure learners' cognitive activity, guide their behavior and directly influence the quality of their learning.
Cognitive overload cannot therefore be seen solely as an individual limitation. It is also, and perhaps above all, the product of design choices.
Designing a teaching device is not just a matter of selecting content or tools. It involves thinking about the learning experience as a whole: the way in which information is presented, organized and prioritized; the pace of activities; the interactions proposed; the margins left to the learner.
From this perspective, the question is no longer whether digital environments generate cognitive overload, but to what extent they can be designed to limit, regulate or even prevent it.
The challenge then becomes one of ensuring the true cognitive sustainability of teaching systems.
It's not a question of renouncing the benefits of digital technology, or reverting to previous models, but of rethinking learning environments by explicitly integrating learners' cognitive constraints. This means moving away from a logic of accumulation to one of structuring; from an approach focused on access to one focused on appropriation.
In other words, we need to design systems that are not just rich in resources, but demanding in their architecture, legible in their organization, and coherent in their progression.
If cognitive overload can thus be analyzed as the product of design choices, one question remains: what concrete levers can be used to design pedagogical devices that are compatible with learners' cognitive capacities?
In other words, how can we move from a critical observation to truly operational pedagogical engineering?
If cognitive overload is partly the result of design choices, then the first lever for action is to act on extrinsic load, i.e. everything in the pedagogical environment that unnecessarily mobilizes learners' cognitive resources.
This requires simplification, not by impoverishing content but by clarifying its organization. This means making explicit what is implicit, prioritizing what is juxtaposed, and structuring what is dispersed.
A cognitively sustainable system is first and foremost a legible system. The learner must be able to quickly identify what is expected, what is central, and what needs further study. This legibility is not self-evident in digital environments, which are often marked by a progressive accumulation of resources and functionalities.
Reducing extrinsic load means making choices. Choosing what to show, what to leave out, what to organize. It means accepting that the quality of a device is not measured by the quantity of resources on offer, but by the coherence of the learning experience it provides.
The question of cognitive load cannot be dissociated from that of time. Learning presupposes a rhythm, an alternation, distinct phases that enable information to be processed, consolidated and reinvested.
In fast-paced environments, this temporality tends to be compressed. Contents follow one another, activities follow one another, without always leaving time for appropriation.
Designing devices that are cognitively sustainable means reintroducing tempo engineering. This implies thinking explicitly about the sequencing of learning, alternating phases of exposure, practice and reflection, and integrating moments of pause that are not perceived as "lost" time, but as conditions for learning.
Pedagogical rhythm thus becomes a design object in its own right. It's no longer just a question of planning content, but of organizing cognitive dynamics.
One of the major risks of digital environments is to reduce learning to exposure to content. Watching a video, reading a document, consulting a resource: these activities can give the impression of learning, without guaranteeing sufficient cognitive processing.
However, the construction of lasting knowledge is based on more demanding processes: manipulating information, making connections, reformulating and problem-solving. These activities require an active commitment on the part of the learner, which cannot be replaced by a simple consumption of content.
Consequently, designing a system that is cognitively sustainable implies placing the learner's activity at the center, by proposing situations that genuinely solicit his or her reasoning abilities. It also means accepting a form of slowdown. Deep cognitive engagement is incompatible with a logic of continuous acceleration.
The integration of artificial intelligence tools into educational systems opens up major prospects, but requires particular vigilance in terms of cognitive load.
Used without a framework, AI can encourage excessive externalization of cognitive processes, by providing ready-made answers that reduce the effort required for reflection. Conversely, when used in a regulated way, it can become an interesting lever for supporting certain aspects of learning, provided it does not replace the learner's cognitive activity.
The challenge, then, is not to prohibit or limit the use of AI, but to consider how it can be integrated. This may involve explicit instructions, activities that call for critical analysis of the output generated, or situations in which AI is used as a starting point, and not as an end in itself.
From this perspective, the trainer's role is changing. It is no longer simply a matter of transmitting content, but of regulating interactions between the learner and the tools, in order to preserve the conditions for real cognitive engagement.
Beyond device design, the issue of cognitive overload also relates to the skills of the learners themselves.
In complex, saturated environments, learning is no longer just about acquiring knowledge, but also about managing one's own cognitive resources. This involves developing self-regulation skills: knowing how to identify situations of overload, adjusting one's pace, prioritizing information, maintaining attention.
These skills do not come spontaneously. They need to be explicitly worked on, supported and integrated into teaching methods. In other words, cognitive load management itself becomes an object of learning.
This evolution is particularly significant in vocational training, and especially in healthcare, where the ability to make decisions in constrained contexts depends directly on the management of cognitive resources.
All these levers converge towards a single requirement: to rethink pedagogical engineering in terms of cognitive sustainability.
It's no longer just a question of designing effective systems in the organizational or technological sense, but systems capable of respecting learners' cognitive limits while supporting their development.
This approach implies a change of perspective. The question is no longer simply: "How can we optimize learning time?", but rather: "How can we create the conditions for quality learning in a time-constrained environment?"
This means accepting that speed cannot be the only indicator of performance. The quality of learning, the ability to transfer and the robustness of skills must once again become central criteria.
At the end of this analysis, one thing is clear: the acceleration of learning environments is neither neutral nor without consequences. It is profoundly transforming the conditions under which individuals learn, to the point of calling into question the very sustainability of current models.
What remains to be examined is what this transformation says about our relationship to time, learning and performance. For, beyond the devices themselves, it is a certain conception of efficiency that is at stake.
We move fast. Ever faster. And this acceleration, long seen as a sign of progress, is now becoming a fact of life that is difficult to dispute. In training environments and organizations alike, speed has become an implicit criterion of performance: to move fast is to be efficient; to respond quickly is to be competent; to produce without delay is to be up to the task.
But as this logic intensifies, one question becomes increasingly clear: what happens to learning in a world where the time to think is shrinking?
Learning cannot be decreed. It cannot be compressed indefinitely. It cannot be accelerated without limit. Learning is a process fundamentally constrained by the very nature of our cognitive capacities. It requires attention, availability and continuity. It involves going back and forth, hesitation and repetition. It requires time; not residual time, but time fully invested.
Yet contemporary digital environments, by seeking to optimize this time, paradoxically tend to weaken it. By multiplying demands, fragmenting attention and emphasizing immediate reactivity, they create the conditions for a diffuse cognitive overload that is often invisible, but profoundly structuring.
The paradox is obvious. We have technologies capable of facilitating access to knowledge as never before, and yet the very conditions for its appropriation are deteriorating. We gain time in accessing information, but lose time in constructing meaning.
This shift is far from insignificant. It's not simply a question of adjusting practices, but of transforming our relationship with knowledge itself. When speed becomes the norm, it redefines what is expected: no longer to understand in depth, but to respond quickly; no longer to construct, but to mobilize immediately; no longer to elaborate, but to produce.
In some contexts, this evolution may seem appropriate. But in other contexts, and particularly in highly responsible training programs such as those for healthcare professionals, it raises an essential question: can we still train for complexity in environments that value simplification and immediacy?
The answer is neither a rejection of digital technology, nor blind acceptance of its promises. It requires a more demanding stance, which involves reexamining the very conditions of learning in the age of acceleration.
This means recognizing that speed has a cost. A cognitive cost, a pedagogical cost, and sometimes a decision-making cost. And that this cost cannot be ignored without consequences.
It also means rehabilitating dimensions that are often marginalized: long time, relative slowness, consolidation, repetition. Not as obstacles to efficiency, but as the very conditions of its sustainability.
Finally, this means rethinking pedagogical engineering not just in terms of optimization, but in terms of sustainability. Sustainability of systems, in terms of learners' cognitive capacities. Sustainability of practices, in terms of professional requirements. More broadly, the sustainability of an educational model that cannot be based solely on acceleration.
Ultimately, the question is not whether to go fast or slow. It's a question of knowing under what conditions speed remains compatible with learning.
Because going fast is not a problem, as long as you keep control. As long as attention is not dispersed. As long as the margins are compatible with our ability to understand, decide and act.
This is perhaps where the central issue lies: not slowing down as a matter of principle, but reintroducing control into a world that tends to accelerate without limit.
And, ultimately, to reaffirm an obvious fact that acceleration sometimes tends to make us forget: learning is not just about going faster. Above all, it's about going far enough to understand.
Illustration: Digital acceleration
Generated by AI (Canva) - Flavien Albarras
References
Amadieu, F., & Tricot, A. (2020). Apprendre avec le numérique. Retz. https://shs.cairn.info/apprendre-avec-le-numerique--9782725638768
Bernard, F. (2023, February 1). Hartmut Rosa's pedagogy of resonance: How school connects students to the world. The Conversation. https://doi.org/10.64628/AAK.vnwtpec53
Bjork, E., & Bjork, R. (2011). Making things hard on yourself, but in a good way: Creating desirable difficulties to enhance learning. Psychology and the Real World: Essays Illustrating Fundamental Contributions to Society, 56-64.
Fonchais, B. de L. (n. d.). Le cerveau attentif : La dynamique de l'attention | Cortex Mag - Cerveau, cognition et neurosciences pour tous. Retrieved April 30, 2026, from https://www.cortex-mag.net/cerveau-attentif-dynamique-lattention/
Kirschner, P. A., & De Bruyckere, P. (2017). The myths of the digital native and the multitasker. Teaching and Teacher Education, 67, 135-142. https://doi.org/10.1016/j.tate.2017.06.001
Li, C., Cui, H., & Hagedorn, L. S. (2026). The cognitive impact of ChatGPT in higher education: A systematic review of critical and creative thinking outcomes. Computers and Education: Artificial Intelligence, 10, 100571. https://doi.org/10.1016/j.caeai.2026.100571
Lin, L. (2009). Breadth-biased versus focused cognitive control in media multitasking behaviors. Proceedings of the National Academy of Sciences, 106(37), 15521-15522. https://doi.org/10.1073/pnas.0908642106
Lodge, J. M., & Harrison, W. J. (2019). The Role of Attention in Learning in the Digital Age. The Yale Journal of Biology and Medicine, 92(1), 21-28.
Mayer, R., & Fiorella, L. (2022). The Cambridge Handbook of Multimedia Learning (3rd ed.). https://doi.org/10.1017/9781108894333
Mayer's 12 Principles of Multimedia Learning | DLI. (s. d.). Digital Learning Institute. Retrieved May 1, 2026, from https://www.digitallearninginstitute.com/blog/mayers-principles-multimedia-learning
Ophir, E., Nass, C., & Wagner, A. D. (2009). Cognitive control in media multitaskers. Proceedings of the National Academy of Sciences, 106(37), 15583-15587. https://doi.org/10.1073/pnas.0903620106
Paas, F., Renkl, A., & Sweller, J. (2010). Cognitive Load Theory and Instructional Design: Recent Developments. Educational Psychologist, 38, 1-4. https://doi.org/10.1207/S15326985EP3801_1
Parry, D., & Le Roux, D. (2021). "Cognitive control in media multitaskers" ten years on: A meta-analysis. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 15. https://doi.org/10.5817/CP2021-2-7
Presentation | Atole. (n. d.). Retrieved April 30, 2026, from https://project.crnl.fr/atole/attentif-ecole/presentation-0
Risko, E. F., & Gilbert, S. J. (2016). Cognitive Offloading. Trends in Cognitive Sciences, 20(9), 676-688. https://doi.org/10.1016/j.tics.2016.07.002
Robert Bjork: A Teacher's Guide to Desirable Difficulties. (n. d.). Retrieved May 1, 2026, from https://www.structural-learning.com/post/robert-bjork-teachers-guide-desirable
Sinna, S. (2026, April 28). The impact of technological acceleration on the resilience of cognitive learning. COG'X. https://cogx.fr/impact-acceleration-technologie-apprentissage-cognitif/
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257-285. https://doi.org/10.1016/0364-0213(88)90023-7
Van Merriënboer, J. J. G., & Sweller, J. (2010). Cognitive load theory in health professional education: Design principles and strategies. Medical Education, 44(1), 85-93. https://doi.org/10.1111/j.1365-2923.2009.03498.x
Young, J. Q., Van Merrienboer, J., Durning, S., & Ten Cate, O. (2014). Cognitive Load Theory: Implications for medical education: AMEE Guide No. 86. Medical Teacher, 36(5), 371-384. https://doi.org/10.3109/0142159X.2014.889290
Zintchem, R., Ngono Zintchem, M. A., Ngae, D., Tembe-Fokunang, E. A., & Ntungwen Fokunang, C. (2025). Generative artificial intelligence and learning clinical nursing reasoning: Scope review, proposal of a "socioconnectrist" approach. Research and Advances in Nursing, (2). https://doi.org/10.25965/reasci.606
AI usage statement - ChatGPT and Perplexity were used as assistive tools for: (a) bibliographic review assistance (locating/sorting articles and structuring reading leads), (b) rewording certain passages to improve clarity and fluency, (c) writing the abstract for this article and (d) spelling correction. Canva was used to generate the illustrative image for the article. The AI did not produce arguments or data without validation: all references were checked and no quotations were invented. The content, analyses and interpretations remain my sole responsibility.
You might also like:
How do you tell a story? Some authors have looked for the invariants, the ingredients of effective storytelling... They found patterns, principles and tropes present in stories and cultures far apart. There's no magic recipe, but there are plenty of rules to follow, or to shake up!
Competency support and non-deficit approach
An employee is hired based on his or her qualifications, which are determined by a job description. The employee is subject to a set of rules and standards enacted by his employers. Within this process of professional socialization lies the rigidity of the hiring. By changing the way we look at the work of employees, it is possible to envision an accompaniment where the reflection on the action is joint and does not measure the gap to be closed but rather what the action teaches us.
Influential algorithms or control over our brains?
What is an algorithm? How does it work? What are the associated issues? These are some of the questions we are facing, including in education.
The e-book, a revolution? Just one more...
The emergence of tablets, e-readers and digital books must be seen as the most recent manifestation of the changes in reading habits that have been underway for centuries. And they don't harm reading, quite the contrary.
Student empowerment and parental responsibility
Learners' failure is often blamed on the school. There may be other reasons for students' lack of guidance and empowerment. In this respect, the imposition of a set path by the parent is often a source of internal conflict and demotivation for the learner. Hence the need to build a framework conducive to student empowerment, through listening and dialogue.
