What happens when a tool capable of writing an essay in seconds, explaining a mathematical procedure, or clearly unpacking a complex scientific paper becomes part of everyday life in the classroom? This is no longer a hypothetical question but a reality faced today by students, teachers, and parents alike. Generative artificial intelligence (AI) has entered schools and radically altered the rules of the game. Some welcome its potential for personalized learning and instant support, while others see it as a threat to critical thinking and academic integrity. Is AI an ally of knowledge, or merely a convenient shortcut that harms us in the long run?

The article avoids one-sided answers and instead offers an interpretation built on a combination of two perspectives embodied by its authors. On the one hand, it provides a direct glimpse from the school desk through the personal experience of a Ljubljana high-school student, revealing how AI is practically reshaping essay preparation, problem-solving, and language learning. On the other hand, it situates this personal perspective within a broader framework: a systematic overview of the latest scientific studies, meta-analyses, and international guidelines that highlight the measured effects, risks, and recommendations for responsible use.

Our aim is not to deliver final judgments but to describe the present state as perceived from both of these vantage points. In conclusion, we also offer our shared understanding of these developments and our conviction that mastering the dialogue with artificial intelligence has become a key literacy of the 21st century.

Artificial Intelligence as a Personal Tutor

In this chapter, we present concrete examples of how students practically use artificial intelligence in their everyday learning challenges: from preparing essays and writing homework to solving mathematical problems, learning languages, and organizing notes. This is a perspective through the eyes of a high-school student who employs AI as a tool for deeper understanding and learning—not merely for obtaining answers. Her experience serves as a starting point for broader reflection on when such use is genuinely meaningful, where it can contribute to better learning, and when there is a danger of it becoming merely a shortcut that bypasses understanding. It has become evident that AI holds the greatest value as a personal tutor precisely where it does not replace the cognitive process but rather stimulates, guides, and helps to disentangle complex concepts.

Preparing for an Essay: “A Socratic Dialogue with Artificial Intelligence”

For many years, it was taken for granted that one could not truly prepare for a high-school essay. Success was believed to depend primarily on personal talent and momentary inspiration. Preparation usually meant reading the required works and searching for the occasional analysis on internet forums—analyses that were often superficial and, in Slovenia at least, long since read by most students.

With the advent of artificial intelligence, this perception has radically changed. Today, we can converse with AI about literary works in a manner reminiscent of a Socratic dialogue—through questions, doubts, counterarguments, and the pursuit of clarity. AI is no longer just a passive source of information but becomes an active interlocutor that enables a deeper understanding of texts and the development of independent thought.

When it comes to lesser-known works, especially Slovenian ones, it is sensible first to provide AI with the full text. This allows it to respond directly on the basis of the original material and avoid confusing characters, events, or meanings. For world classics, this is usually unnecessary, as such works are already well represented in the models’ knowledge.

AI can efficiently summarize content, analyze characters and their relationships, identify the core ideas and values of a work, and help unpack its literary and philosophical layers. Yet its greatest strength lies elsewhere: it does not impose uniform interpretations but instead stimulates reflection, poses additional questions, and helps students shape their own interpretations—expressing them more clearly and convincingly.

Moreover, AI is not limited to standard analyses. It can assist in formulating answers to more open-ended and creative questions often found in essay prompts: how literary characters might behave in the modern world, how a contemporary reader perceives a certain work, or what relevance its message holds today. In this way, AI is not merely a tool for analysis but becomes a partner in developing argumentation and understanding context.

Preparing for an essay with artificial intelligence is therefore no longer about passively reading summaries but about engaging in a dynamic and creative dialogue, where the student develops personal viewpoints, tests them critically, and refines them. If in the past success relied largely on talent, today that talent can be enhanced through the well-directed use of AI—an instrument that opens new pathways to understanding literature and the world.

Writing Entire Essays with AI

In addition to preparation, many students also use artificial intelligence in a more direct—yet riskier—way: by simply entering the essay prompt and letting the system generate the entire piece. The result is often surprisingly polished and seemingly convincing work, something that could be handed in to a teacher with minimal edits, saving precious time. Yet in practice, this strategy rarely proves successful.

Essays written entirely by AI often differ stylistically and structurally from what is expected in Slovenian schools. They may include subheadings, rely on generic quotations, or present overly generalized claims that are insufficiently grounded in the text itself. More importantly, such essays frequently fail to follow the teacher’s specific instructions—a key criterion in assessment. As a result, teachers often quickly suspect that the essay was not written by the student but by artificial intelligence.

Such essays are often graded lower than those written by students themselves. The reason lies not only in inappropriate form but also in the absence of personal reflection, originality, and an authentic voice. An AI-written essay may be linguistically flawless, but it often lacks what a good teacher notices immediately: the student’s genuine intellectual engagement.

For this reason, most students use AI in this way mainly for assignments that are not directly graded—for instance, within compulsory elective activities. In the second year, we were required to submit an essay on a film watched in class. Pressured by numerous other obligations, some students chose to have AI write it for them. Yet the teachers rejected these submissions, rightly suspecting they were not original. The students had to rewrite their essays under supervision at the end of the year, which meant extra work, stress, and wasted time.

Such use of AI is therefore ineffective in the long run. Not only does it often fail to deliver the desired results, it also contributes nothing to knowledge, intellectual development, or writing skills. Still, it must be acknowledged that many students turn to this option not out of laziness but because of overload: constant assessments, tight deadlines, and numerous extracurricular commitments. In such moments, artificial intelligence primarily represents a convenient solution to a lack of time and a way to secure a bit more peace or sleep.

This is precisely why it is all the more important not to treat AI as a substitute for one’s own work, but as a tool that supports and enhances our abilities. Writing an essay is not merely a school assignment but an exercise in thinking, understanding, and expression—and it is in this role that AI can become an ally, rather than an escape from learning.

Solving and Explaining Science and Math Problems

Beyond literary analysis and essay writing, artificial intelligence can also provide significant support in subjects such as mathematics, physics, and chemistry. This is often where difficulties arise: we may not know how to start a problem, which formulas to use, or how to proceed once we get stuck midway. Sometimes we even reach the wrong result without realizing where we went wrong—and these small, hard-to-detect mistakes are the most frequent source of frustration.

Since a teacher or classmate is not always available—especially not in the afternoon at home or right before a test—AI can step in as a patient explainer and tutor. If we provide it with the text or a photo of a problem, it can not only solve it but also carefully explain the process: step by step, with a clear rationale at each stage. This way, we don’t just get the correct answer—we also understand the path that leads to it.

This feedback loop is immensely valuable: it allows us to quickly spot where we made mistakes and why. Detecting errors in our own work is time-consuming and often unfeasible, especially when we don’t know what we’re actually looking for. In this regard, AI can save us a great deal of time while deepening our understanding, enabling us to learn from our mistakes instead of merely stressing over them.

It is also highly useful for assignments where only the final result is provided but not the full solution process. In such cases, AI can reconstruct the reasoning from beginning to end, allowing us to compare its approach with our own. This makes it possible to verify our work, correct missteps, and strengthen our comprehension.

For more complex problems involving branching procedures, multi-step reasoning, or a degree of creativity, so-called “thinking models” of AI are especially valuable. These advanced approaches deliberately extend the processing time and carry out multiple layers of reasoning in the background, often through a technique known as chain-of-thought prompting. Instead of simply presenting an answer, the system “thinks out loud”—laying out each step while continually checking the consistency of its reasoning. This approach may take longer, sometimes several minutes, but the reward is a carefully considered and logically structured response that reveals not only the final result but also the entire line of reasoning.

Such answers are extremely useful not only for solving individual tasks but also as learning aids when preparing for tests, final exams, or oral defenses. By explaining its reasoning, the model allows the student to follow the thought process, learn from it, and simultaneously check their own understanding. In this way, AI becomes a tool for strengthening analytical thinking rather than merely a supplier of quick solutions.

All of this demonstrates that in science and mathematics, AI does not function merely as a “calculating machine,” but as an instrument for developing understanding—which is, after all, what matters most in learning.

Learning Foreign Languages

Learning a foreign language often requires mastering a vast amount of new vocabulary. Simply reading or passively listening usually isn’t enough, as words are quickly forgotten without active use. This is why flashcards have long been a popular tool: they promote memorization through repetition and recall. Yet preparing such cards takes considerable time—and this is precisely where artificial intelligence can help effectively.

With AI, we can quickly generate exercises tailored specifically to our needs: we provide a list of words we want to learn and ask the system to present them with explanations, translations, or usage examples in random order. We then try to recall the correct word or translation. This method encourages active retrieval, which is proven to enhance long-term memory.

AI is not limited to basic flashcards. If we show it examples of exercises from past tests or workbooks, it can create new tasks in the same format but with fresh vocabulary that we are currently studying. Such individualized practice allows us to learn in a targeted and efficient way rather than mechanically.

Interestingly, artificial intelligence is no longer used for language learning only by students but also by some teachers. Occasionally, a test problem will even include the note: “created with ChatGPT.” This shows that AI is not just a tool for reinforcing knowledge but is becoming part of the broader learning environment—an instrument used by both teachers and students.

Writing is also a crucial part of language learning. When writing in a foreign language, we often repeat the same mistakes, which are difficult to spot on our own—especially when writing by hand. Since teachers, due to time constraints, typically review only a few written assignments, AI can step in as an additional language mentor. If we provide it with a photo of a handwritten text, it can transcribe, correct, and explain the errors. In this way, we don’t just receive an improved version of the text but also gain an understanding of why something was wrong and how to improve it in the future.

With regular use of artificial intelligence for writing and vocabulary practice, we can deepen our knowledge of a foreign language more quickly and more permanently. This means that AI does not replace learning—it complements and strengthens it.

Collecting Information and Organizing Notes

In some subjects—especially biology, geography, history, or social sciences—students must master large amounts of material. Information is often scattered across different sources: textbooks, worksheets, websites, or densely written paragraphs without clear structure. Such material is difficult to digest, requiring considerable time just to understand the basics, let alone to memorize effectively.

In these cases, artificial intelligence proves extremely useful. We can provide it with fragmented notes or raw texts, and it can turn them into neatly organized, logically structured summaries or tables. It sorts data into meaningful categories, organizes them by comparisons, time periods, functions, classes, or features—in short, it reshapes information so that it becomes comprehensible and easier to remember.

For example, in biology we had to compare different organ systems across animals, from sponges and cnidarians to amphibians and mammals. Manually collecting and writing these details into comparative tables would have taken a great deal of time. AI, however, can generate such a table in seconds from the sources we provide (or those already in its training data), showing the differences in digestion, respiration, circulation, excretion, and nervous systems across animal groups.

This leaves the student with more energy and time for understanding and learning. AI-prepared material can be easily exported into Word, PDF, or Excel, and then supplemented, edited, printed, or shared with classmates. This not only supports more effective studying but also facilitates collaboration and knowledge exchange. In this way, AI becomes a digital assistant that doesn’t replace the student’s work but simplifies it where tasks are mechanical and time-consuming, freeing attention for what truly matters: comprehension, connections, and independent thinking.

Just before this article went to press, OpenAI introduced a new feature called Study Mode, designed for targeted learning with artificial intelligence. The system first asks the user about the topic they want to understand, as well as their age or educational level. Based on this, it begins a conversation in the form of questions and feedback. The user first attempts to answer the questions on their own, after which the system offers clarifications, corrections, and prompts for further reflection. What makes this approach distinctive is its use of the Socratic method, which fosters deeper understanding, self-reflection, and active engagement through a series of guiding questions.

Learning with this mode becomes more interactive and in-depth, since detailed instructions are no longer required—the AI itself takes on the role of a tutor, adapting in real time to the learner’s knowledge and pace. The feature was developed in collaboration with several educational institutions and is grounded in the latest didactic insights. It is available to all ChatGPT users regardless of subscription tier and will soon be integrated into the educational version, ChatGPT Edu. This approach not only facilitates comprehension of complex content but also strengthens independent thinking, nurtures curiosity, and encourages the responsible use of AI in education.

Systemic Challenges, Measurable Effects, and Potential Risks

The impact of artificial intelligence on education is not merely theoretical; its effects are already concrete, measurable, and often multidimensional. In this chapter, we illuminate two sides of the same coin. On one hand, there are promising data showing improved learning outcomes, increased motivation, and more personalized support for individual learners. On the other hand, serious questions arise: does excessive reliance on AI foster superficial learning? Do we risk losing independent thought, perseverance, and critical autonomy? It is precisely this tension between opportunities and pitfalls that provides a crucial starting point for deeper reflection on AI’s role in the future of education.

Meta-Analyses and Research on the Pedagogical Effects of AI

The first large-scale studies on the impact of generative artificial intelligence (AI) on learning show predominantly encouraging results. The most reliable insights come from the method of meta-analysis, which aggregates findings from numerous individual studies to assess the overall effect of a given method or technology on learning outcomes.

A meta-analysis that included 65 independent studies (Sun & Zhou 2024) estimated that the use of generative AI improves student performance by about half a standard deviation on average (Hedges’s g ≈ 0.53). This is a moderate yet meaningful effect, comparable to raising an average student from the 50th to the 69th percentile in achievement. The strongest effects appeared in tasks involving text generation (e.g., essay or report writing) and in self-directed learning, where AI acts as a tutor providing real-time feedback.

Another meta-analysis (Zhang, Jantakoon & Laoha 2025) systematically examined the effectiveness of AI technologies in education, drawing on 13 empirical studies from eight countries. The research revealed a significant and large overall positive effect (g ≈ 0.86), highlighting the considerable advantages of integrating AI into educational processes. The most pronounced impact was observed in chatbots and generative AI (g ≈ 1.02), while online learning environments and virtual reality showed moderate effects (g ≈ 0.79). The authors note substantial variability among the analyzed studies but emphasize the robustness of positive outcomes and the importance of considering contextual factors when implementing AI solutions.

Other systematic studies, focused specifically on conversational agents such as ChatGPT, distinguish between cognitive and non-cognitive effects. Analyses of 27 empirical studies (Suo, Yin, Wang, et al. 2025 [preprint]), published between 2022 and 2025, confirm that these agents have a significant positive influence on learning. On the cognitive level (measured by grades, task completion time, etc.), they report moderate positive effects (g ≈ 0.36). Even greater effects (g ≈ 0.52) appear in non-cognitive outcomes such as motivation, perseverance, and self-efficacy—that is, the belief that one is capable of successfully completing tasks. These findings suggest that students often perceive generative AI as a safe, non-judgmental environment that encourages them to engage more with learning material and reduces fear of making mistakes.

Among individual skills, writing stands out as a particularly promising area. Randomized controlled trials (RCTs), considered the gold standard for measuring effectiveness, show that students who receive feedback from AI while writing essays achieve better structure, cohesion, argumentation, and content development compared to those who receive only traditional feedback (e.g., from teachers or peers). Beyond improvements in text quality, studies also highlight higher levels of motivation and engagement among students using AI systems, though they also report mixed emotional responses (Zhang 2025; Lo, Wong & Chan 2025).

In the context of foreign language learning, a large meta-analysis of 31 comparative studies (Lyu, Lai & Guo 2025) confirms that chatbots have a moderate positive effect (g ≈ 0.61) on cognitive skills such as writing and vocabulary acquisition, as well as on affective factors such as motivation and interest. The effects are strongest when interaction with AI is guided and reflective, enabling learners not only to receive immediate feedback on errors but also to understand their causes. Such a deeper approach is made possible primarily by advanced chatbots using generative AI, accessible via mobile devices—identified by the meta-analysis as key factors in boosting effectiveness.

The overall conclusion of these studies is clear: the effects of generative AI use are on average positive and moderate, with the greatest benefits appearing when the technology is not employed passively but as a tool to support comprehension, writing, and active learning. The teacher’s role in guiding this use remains crucial for achieving optimal results and avoiding merely superficial knowledge.

Mechanisms of Effectiveness and Limits of AI Use in Education

At first glance, findings on the impact of artificial intelligence (AI) in education seem riddled with contradictions, creating considerable confusion. On the one hand, large-scale meta-analyses that synthesize results from hundreds of individual studies generally report moderate but statistically significant positive effects on learning.

On the other hand, high-profile field experiments such as the Wharton study (Bastani et al. 2024) paint a far more complex and troubling picture. In this particular study, researchers found that while access to a general model such as GPT-4 temporarily improved students’ performance on assignments, the same group performed worse on a final exam—taken without AI assistance—than students who had never used the tool. This suggests that uncritical use of generative AI, which primarily facilitates access to final answers, may in the long run undermine the development of independent knowledge and problem-solving skills.

The apparent contradiction between these findings is therefore not necessarily a paradox but underscores a key insight: what matters is not simply whether AI is used, but how it is designed and integrated into the learning process. While carefully developed pedagogical tools—with built-in safeguards that encourage critical thinking—can genuinely support learning, unrestricted use of general-purpose systems may lead students toward passive shortcuts, hampering their cognitive growth.

The positive effects of generative AI in education are not accidental; they stem from the technology’s ability to strengthen and automate already established didactic principles. Successful implementations foster active learning, provide immediate feedback, allow for personalized learning paths, and reinforce self-regulation skills.

A study by Iqbal et al. (2025), involving 465 pre-service teachers in China, examined in more detail the impact of these tools on learning outcomes. The researchers found that generative artificial intelligence enhances achievement primarily through two key mechanisms. The first is cognitive offloading, where the tool takes over routine tasks such as information retrieval or syntactic checking, enabling learners to direct their mental resources toward more demanding processes such as critical analysis and the synthesis of ideas. The second mechanism is shared metacognition, in which the technology acts as a catalyst for collaborative learning by encouraging students to reflect together, evaluate one another’s ideas, and coordinate problem-solving strategies. In this way, generative AI functions not merely as an individual tool but as a platform that strengthens collective intelligence and deepens understanding within a learning group.

The key question is not whether a student uses AI, but which part of the cognitive process is delegated to it. Beneficial offloading occurs when AI takes over routine, low-level tasks—for example, text formatting, synonym searching, summarizing a source, or carrying out a familiar calculation. In such cases, AI expands the learner’s cognitive capacity, allowing greater focus on higher-order thinking such as planning, argumentation, synthesis, and conceptual integration. Harmful offloading, however, occurs when AI assumes responsibility for the core intellectual work—such as understanding a problem, formulating a research question, developing a thesis, or constructing an argument. In these cases, AI replaces thinking rather than supporting it. The student does not practice fundamental cognitive skills, and genuine learning simply does not take place. The tool becomes a cognitive crutch.

In addition, generative tools are prone to the phenomenon known as hallucination, in which they produce information with great confidence that is in fact false, fabricated, or lacking any factual basis. This may involve citing non-existent sources, misinterpreting facts, or generating entirely invented data. Consequently, when these tools are used for academic purposes such as essays and research assignments, rigorous verification of all citations, references, and factual claims is essential. This necessity underscores the importance of advanced information and data literacy, enabling users to critically evaluate and validate content.

Generative AI as a Learning Partner: Conditions for Effective Use

The central message of this article has been confirmed across examples and analyses alike: generative artificial intelligence contributes most to education when it shifts from being a passive tool to becoming a partner in thought. It does not write instead of the student but engages with them in dialogue, asking questions, checking understanding, offering counterarguments, and helping to refine initial ideas into clear, original expression. In such a relationship, AI is no longer merely a “task performer” but a co-creator of the learning process—while the student retains the central role of deciding, judging, and taking responsibility.

This mode of use rests on three interlinked didactic principles that together create a learning environment in which the student actively participates in the cognitive process. The first principle is learning dialogue, where the student and AI engage in a sequence of questions, explanations, counterarguments, and revisions. This process promotes active recall, reduces the risk of superficial understanding, and counters the illusion of certainty that may arise from fluent yet shallow answers. The second principle is traceability of the thought process. Drafts, revisions, and explained changes provide insight into the path of reasoning, creating conditions for learning from mistakes, fostering reflection, and enabling the gradual construction of knowledge. The third principle is dual verification. A student first formulates a claim with the help of AI, but must then be able to articulate and defend the same claim independently. Such repetition under identical standards ensures that knowledge is genuinely internalized and transferable to new contexts.

When this logic is transferred into the classroom, the focus shifts from products to processes—from simply having “solved tasks” to explaining methods, weighing alternatives, and applying knowledge in new situations. Assessment practices must adapt accordingly: drafts, ongoing feedback, reflections, and oral defenses gain greater importance, while AI-free tests remain essential to show how much knowledge persists in independent use. This reduces the gap between quick execution with AI and genuine understanding without it.

In such use, another often-overlooked dimension comes to the fore: the emotional literacy of models and the importance of human connection. Communication that is encouraging, clear, and respectful can strengthen a student’s perseverance and confidence. By contrast, a patronizing, sarcastic, or overly self-assured tone can quickly lead to passivity and intellectual withdrawal. Well-designed use of AI should therefore take over routine tasks—such as language corrections, basic error marking, or literature gathering—and free time for teachers and students to focus on what no model can replace: conversation, mentorship, and shared thinking.

Yet such use of AI requires not only sound pedagogical practice but also ethical reflection. It demands careful handling of personal data, transparency of processes, and equitable access to high-quality tools. Early phases of new technologies often favor those with better devices or access to paid model versions. Long-term fairness does not happen on its own; it requires school-level licenses, reliable infrastructure, and didactic support that make new learning patterns accessible to all, not just the best equipped and most motivated. For Slovenian students, solid language support for Slovene in AI models is an advantage, but linguistic accessibility in commercial systems cannot replace the need for systemic regulation in this field.

Although this article provides a clear didactic framework, the discussion does not end here—it truly only begins. Key challenges remain that demand thoughtful answers: Where does assistance in thinking end and replacement of thinking begin? How should assessments be designed to balance evaluation of the learning process (where AI is a partner) with the demonstration of independent knowledge? How can developers design interfaces that encourage deeper understanding rather than merely generating quick answers? And ultimately, how can we guide this collaboration with AI so that it strengthens students’ intellectual autonomy and their capacity for dialogue—both with people and with machines?

References

• Bastani, Hamsa, Osbert Bastani, Ahmet Sungu, Hao Ge, Özge Kabakcı, and Rick Mariman. Generative AI Can Harm Learning. SSRN Working Paper, The Wharton School, 2024.

• Iqbal, Junaid, Zohaib Farooq Hashmi, Muhammad Zubair Asghar, and Muhammad Nauman Abid. “Generative AI Tool Use Enhances Academic Achievement in Sustainable Education through Shared Metacognition and Cognitive Offloading among Preservice Teachers.” Scientific Reports 15 (2025): 16610.

• Lo, N. P. K., Andy Wong, and Samuel Chan. “The Impact of Generative AI on Essay Revisions and Student Engagement.” Computers & Education: Open 100249 (2025).

• Lyu, Bin, Chun Lai, and Jingjing Guo. “Effectiveness of Chatbots in Improving Language Learning: A Meta-Analysis of Comparative Studies.” International Journal of Applied Linguistics (2024).

• Sun, Lin, and Lijun Zhou. “Does Generative Artificial Intelligence Improve the Academic Achievement of College Students? A Meta-Analysis.” Journal of Educational Computing Research (2024).

• Suo, Xiaochen, Baoyuan Yin, Wanqing Wang, et al. “Exploring the Impact of Generative AI-Powered Conversational Agents on Student Learning: A Systematic Review and Meta-Analysis Grounded in Activity Theory.” Authorea, July 1, 2025.

• Zhang, Jincheng, Thada Jantakoon, and Rukthin Laoha. “Meta-Analysis of Artificial Intelligence in Education.” Higher Education Studies 15 (2) (2025): 189–203.

• Zhang, Kai. “Enhancing Critical Writing through AI Feedback: A Randomized Control Study.” Behavioral Sciences 15 (5) (2025): 600.

Translated from the Slovene original, available here: Umetna inteligenca v šoli.

Subscribe now

Why do we remember a film more easily than a lecture? Why does a good novel move us more deeply than even the most precise statistical report? And why are we sometimes willing to believe utter nonsense—so long as it is skillfully presented? The key to these questions lies in stories. They are not merely a way to pass the time; they represent a fundamental tool of thought, a means by which we interpret the world and share our understanding with others.

We often underestimate how powerfully stories shape our perception of reality. Human beings need narratives to help us make sense of what happens around us. When we are left without compelling explanations, we become vulnerable to conspiracy theories, which are, after all, stories as well. Though untrue, they effectively fill the void, impose order, and offer simple explanations for complex phenomena.

Stories captivate us because they resonate with the way our brains function. They fit remarkably well with our cognitive architecture. Humans are skilled at recognizing causes, anticipating the future, and inferring the thoughts of others. We did not develop these abilities because of stories—yet it is precisely through stories that we can exercise them to the fullest. When we listen to a narrative, we imagine events unfolding, follow actions, feel tension, and search for meaning. A story is therefore not merely a sequence of information, but an experience we relive in our minds. And what we experience, we remember. A story is often not just a tool for explanation, but its very essence.

The earliest larger organisms, which spent most of their time immobile, had no need for brains. Their behavior was simple, their environment predictable, their responses automatic. Everything changed, however, with the emergence of active movement and hunting. An animal that had to pursue prey while avoiding predators required a central system to coordinate conflicting streams of information. From this necessity, the first brains came into being.

At first, these systems followed a simple principle: approach what is useful, avoid what is harmful. This basic algorithm enabled survival for worm-like creatures that lived some 600 million years ago. Yet the system was rigid and incapable of learning, unable to adapt to change.

About a hundred million years on, early vertebrates developed the ability to learn from experience. Their brains began to construct internal maps of the world. These maps allowed them to interpret their surroundings and anticipate future events. The brain became a tool for shaping and updating an inner image of the external world—a turning point in the evolution of behavior. From then on, life was no longer merely reactive, but anticipatory. Still, at first, this inner image remained passive. The true leap came when our ancestors began to actively use these inner maps to rehearse the future.

Building on this foundation, three key skills emerged that directly enabled the comprehension of stories. The first was inner simulation. Our ancestors could imagine different scenarios before they actually unfolded. They could learn from imagined experiences without incurring real risks. This capacity to generate narratives became one of the crucial evolutionary advantages.

A second great step was taken by the ancestors of primates several tens of millions of years ago, when they began to weave an understanding of other beings into these simulations. They could envision what others were thinking, feeling, and intending. For the first time, stories acquired characters with inner lives. A narrative was no longer just a sequence of events, but a stage on which relationships unfolded between individuals with differing perspectives and goals.

The decisive breakthrough came with the rise of language. While other species communicated only about immediate states—danger, food, dominance—language made it possible to speak of things not present in the here and now: the past, the future, and entirely imagined worlds. It allowed humans to begin sharing their narratives with others. Language became a means of synchronizing mental worlds within a community. Stories turned into a powerful medium for transmitting experiences, values, and cultural patterns. Thus began the great accumulation of knowledge, which could be passed on and expanded across generations.

This evolutionary heritage of brain development has profound consequences for the functioning of modern society. The ability to tell stories grants us extraordinary power, yet it can also dangerously mislead us. On the one hand, stories act as social glue. Shared narratives—myths, histories, religious teachings, or corporate visions—create a sense of belonging and enable cooperation among strangers. Stories are also an effective tool for transmitting knowledge, since we remember complex content more easily when it is cast in narrative form. Above all, they help us make sense of the world. They impose structure and logic on events that might otherwise appear random.

Yet it is precisely this power that also makes us vulnerable. Because our brains are so attuned to storytelling, we are highly susceptible to manipulation. Propaganda, advertising, and fake news often succeed because they address us in the form of a story. More often than not, we prefer to believe a simple tale with clear characters rather than a messy reality filled with contradictions and uncertainty.

A story we accept quickly becomes part of our worldview. It reinforces beliefs, shapes identity, and influences decisions. We often reject facts that contradict it and search for confirmation of what we already believe. Thus, the very stories that connect us can also blind us.

It is therefore essential to recognize that if people lack compelling stories to help them understand the world, their minds will spontaneously cling to whatever narratives are available and seem persuasive. In the absence of clear explanations, substitute stories take root—whether accurate or not. This is why a healthy community must pay close attention to the stories it circulates and promotes. It matters profoundly which narratives fill the public sphere, for they shape not only our understanding of the world and its problems, but also the horizon of our common future.

Subscribe now

Translated from the Slovene original, available here: Bitja zgodb.

Despite its growing importance as one of the most significant threats in today’s world, hybrid warfare often remains underestimated. This form of conflict transcends traditional battles involving tanks and fighter jets, taking the shape of subtle, covert operations conducted simultaneously across multiple fronts, often without a single shot being fired. It can be likened to a complex geopolitical chess match, where the pieces are not just military units but also fake news, cyberattacks, economic coercion, and political manipulation. This game of manipulation and strategy frequently blurs the lines between peace and war, leaving the identities of the aggressor and the victim ambiguous, creating an atmosphere of chaos and uncertainty.

Fake profiles and so-called “useful idiots” on social media platforms, who spread disinformation and sow distrust and chaos, are among the most powerful tools of hybrid warfare. Their effectiveness lies in their ability to systematically erode social cohesion, foster mistrust in key institutions, and deepen polarization. This mechanism allows attackers to exert subtle yet far-reaching influence on public opinion. Ordinary individuals, often unwittingly, become vectors for disseminating strategically designed disinformation, fostering an environment ripe for the destabilization of societal and political systems.

Democratic states, rooted in open public discourse and a plurality of media voices, are uniquely vulnerable to the threats of hybrid warfare. Media outlets often find themselves on the front lines, playing a critical role in shaping public opinion and providing credible information. Their responsibility lies in creating a space for legitimate criticism and discussions about abuses of power, while also recognizing and countering the traps set by disinformation campaigns. These campaigns can spread chaos, deepen mistrust, and undermine the authority of key democratic institutions. To avoid unwittingly becoming tools of aggressors, media must operate as transparent and reliable sources of information, strengthening public resilience to manipulation and actively exposing the tactics of hybrid warfare. By doing so, they contribute not only to protecting institutions but also to ensuring the long-term stability of democratic societies.

Key tactics of hybrid warfare include sophisticated forms of manipulation that subtly guide the target toward decisions that ultimately serve the attacker’s interests. This strategy hinges on crafting a distorted perception of reality, influencing the interpretation of events and information. For instance, by spreading carefully crafted false information, attackers can lead their targets to take actions that are not only unnecessary but may even harm their own interests. A core element of this strategy, known as “reflexive control,” is convincing the targeted party that they arrived at these decisions independently, even though their choices were meticulously directed through manipulative tactics. Reflexive control exploits deep-seated cognitive biases, informational gaps, and unconscious trust in flawed premises. It frequently overlaps with disinformation campaigns, cyberattacks, and economic pressures, forming a complex and nearly imperceptible network of threats. These threats subtly but effectively undermine social and political systems, destabilizing the very foundations of democratic societies.

Subscribe now

In a world grappling with complex challenges like climate change, ecological degradation, and social inequities, finding innovative, inclusive solutions is more critical than ever. One promising approach is “two-eyed seeing,” a concept introduced by Mi'kmaw Elder Albert Marshall. It promotes a worldview that integrates both Indigenous and Western knowledge systems.

While the idea seems straightforward, it requires careful consideration to avoid oversimplification. It's essential to recognize that while both knowledge systems offer valuable insights, they are distinct in their foundations and methods.

Science, as a collective human endeavor, draws on contributions from diverse civilizations and relies on empirical evidence, objectivity, and rigorous testing to achieve universal explanations. In contrast, Indigenous knowledge is holistic, intertwined with cultural practices, spirituality, and generations of environmental observation, focusing on interconnectedness and sustainability.

"Two-eyed seeing" does not claim these systems are epistemologically equivalent but rather complementary, with the potential for synergy. Each has its strengths: science excels in providing generalized, testable predictions, while Indigenous knowledge offers place-based wisdom and a deep understanding of specific ecosystems.

The idea of “complementarity” in “two-eyed seeing” goes beyond coexistence; it envisions an active partnership where each system not only fills gaps but also enriches the understanding of the other. For instance, Indigenous knowledge often provides context-specific insights that might be overlooked by broader scientific generalizations. This active partnership is evident not only in environmental conservation but also in fields like health research, where Indigenous knowledge of medicinal plants has led to new scientific discoveries.

It's also important to recognize that different knowledge systems have varying domains of strength and applicability, and one should not be indiscriminately substituted for the other. While Indigenous knowledge is invaluable, it shouldn’t replace scientific inquiry in fields requiring rigorous testing, like medicine or engineering.

The historical context of colonialism adds another layer of complexity. Science has sometimes been misused to justify oppression and exploitation, leading to skepticism within Indigenous communities. Acknowledging this historical harm is essential, and scientists must approach collaboration with humility and respect, ensuring that Indigenous knowledge is neither appropriated nor exploited.

In addition to historical harm, there is a risk of tokenism, where Indigenous perspectives are acknowledged but not meaningfully integrated into decision-making processes. Genuine collaboration requires a deeper engagement that respects the full depth of Indigenous knowledge systems.

Despite these complexities, "two-eyed seeing" offers a powerful framework for addressing contemporary challenges. For instance, in Canada, Indigenous communities have worked with scientists to restore salmon populations, combining traditional knowledge of salmon migration with scientific data to develop effective conservation strategies.

The Confederacy of Mainland Mi'kmaq received funding to integrate Mi'kmaq Ecological Knowledge with science, addressing high-priority threats and enhancing fish habitats through biological assessments and habitat restoration. Similarly, the Nova Scotia Salmon Association has undertaken projects to restore ecosystems in various rivers, promoting the recovery of aquatic species at risk by implementing activities that reduce identified threats, such as declining water quality.

Indigenous peoples have accumulated generations of observations on salmon migration patterns, spawning habits, and habitat preferences. This intimate familiarity with local ecosystems provides insights into salmon behavior that may not be captured through conventional scientific methods. For instance, Mi'kmaq observations about the timing of salmon runs and the relationship between water temperature and spawning success provided critical data points that science had not fully understood, leading to better-targeted conservation measures.

Salmon also hold profound cultural and spiritual importance for many Indigenous communities. This relationship fosters a stewardship ethic that prioritizes the long-term health of salmon populations and their habitats, aligning conservation efforts with cultural values. Traditional fishing techniques, such as selective harvesting and seasonal restrictions, have been developed to ensure the sustainability of salmon populations.

This collaboration highlights the potential of "two-eyed seeing" to generate innovative solutions that respect both knowledge systems. Other examples include integrating Indigenous healing practices with modern medicine and incorporating Indigenous perspectives into curricula, promoting a more inclusive and holistic understanding of the world.

While Indigenous knowledge offers profound environmental insights and sustainable practices, it may not provide the same level of precision required in highly specialized fields like pharmaceutical development, where controlled experimentation and statistical analysis are critical.

Ultimately, "two-eyed seeing" encourages us to embrace diverse ways of knowing and seek collaborative solutions. By acknowledging the strengths and limitations of both science and Indigenous knowledge, and by considering the complex historical context, we can contribute to a more equitable and sustainable future.

It’s also important to recognize that the term "Western science" is a misnomer. Science is not confined to any geography or culture; it is a collective human endeavor, shaped by contributions from various civilizations. From the astronomical observations of ancient Babylonians and Egyptians to the mathematical advances of Indian and Islamic scholars, the pursuit of knowledge has always been global. Therefore, framing science as "Western" obscures its rich, diverse heritage and risks perpetuating a Eurocentric view of knowledge production.

As we confront urgent global challenges like climate change and biodiversity loss, “two-eyed seeing” is not just an innovative approach—it is an essential strategy for creating resilient, sustainable solutions. By integrating these distinct but complementary ways of knowing, we can harness the full potential of human insight and innovation to create adaptive, resilient solutions for a truly sustainable future.

However, implementing “two-eyed seeing” in practice is not without its challenges. Differences in epistemologies—where Indigenous knowledge is often qualitative and contextual, while science is quantitative and reductionist—can lead to misunderstandings. Power imbalances, rooted in colonial histories, may also hinder genuine collaboration. Building trust requires time, mutual respect, and a willingness from both sides to engage in a shared learning journey.

This is a slightly adapted version of a contribution to the discussion within the World Commission on the Ethics of Scientific Knowledge and Technology (COMEST).

Subscribe now

Puritanism was a religious reform movement that emerged in the late 16th and 17th centuries, aiming to "purify" the Church of England from remnants of Roman Catholic practices. At its core, Puritanism emphasized a covenant relationship with God, personal responsibility, and a disciplined, pious lifestyle. Puritans believed in the necessity of hard work, simple living, and moral integrity as expressions of their faith and devotion.

Although these values were initially based on inner discipline and humility, over time they evolved into values centered on external success, where material achievements, status, and power became primary indicators of personal worth. This led to the concept of the "American Dream," which promises that anyone, regardless of their background, can achieve success through their own efforts.

Donald Trump embodies these values in their superficial, extreme form. His rhetoric and image as a successful businessman appeal to those who believe in the importance of individual achievement and material wealth. His slogan, "Make America Great Again," taps into nostalgia and the desire to return to past successes. Through his populist rhetoric, he plays on a sense of nostalgia and evokes the idealized American myth of success, which serves as a fundamental reference point for many people.

On the other end of the political spectrum, there is also no strong emphasis on community; instead, we see a similar moralistic stance in the form of progressive social justice movements. These movements often call on individuals to take personal responsibility for alleged unjust advantages, such as privileges related to race or socio-economic status. Although these views may initially seem opposed, in reality, both positions are based on the same values—individualism and personal responsibility. Progressive movements emphasize the need for justice and equality in achieving personal success, but they often address this through the lens of individual responsibility and behavioral change.

American politics thus features two different versions of individualism that stem from the same core values but have different approaches to achieving success and social justice. The first version advocates for ruthless competition, where success is achieved by the strongest and most influential; it is closely linked with capitalist ideology, celebrating personal success and entrepreneurship, regardless of social consequences. The second version focuses on fair competition, where all individuals have equal starting points. This more progressive idea stresses the importance of rules that ensure equal opportunities and prevent privileges like race and socio-economic status from hindering fair competition. Both versions are based on values of personal responsibility and freedom but differ in their understanding of fairness in achieving success.

In this context, a comparison with Europe is noteworthy, where values are more oriented toward social welfare and community. In many European countries, a stronger collectivist spirit emphasizes the importance of social security, universal healthcare access, and support for the most vulnerable. For instance, Scandinavian countries like Sweden, Norway, and Denmark have well-developed welfare systems that include high taxes in exchange for extensive public services, reflecting their commitment to collective responsibility.

In contrast to the American ideal of the self-made individual climbing the ladder of success, European countries often support the idea that society as a whole takes responsibility for the well-being of all its members. This highlights a difference in cultural values that influences the acceptance of welfare policies and collective responsibility in the two societies.

If Kamala Harris had merely advocated for more collectivist ideas in the same value climate, she would not have garnered more votes than she did. A successful political strategy requires not just different ideas but also an understanding of and connection to the existing values in American society.

Trump likely won in part because he embodied the values of freedom and individual initiative that American culture holds dear, which helped him garner broad support despite the controversies surrounding him. Thus, a political alternative in the United States must consider deeply ingrained values and find a way to present progressive goals within the framework of the "American Dream," linking individual success with shared responsibility and justice.

The research, currently under peer review, was led by Niels G. Mede from the University of Zurich and involved a collaboration of scholars from prominent institutions, including ETH Zurich, the University of Bern, the University of Amsterdam, Harvard University, and Stellenbosch University. Conducted between November 2022 and August 2023, the study surveyed over 71,000 participants from 68 countries, offering a globally representative snapshot of public science communication practices.

The researchers set out to answer two key questions: (1) Where do people obtain information about science? and (2) How do they engage in discussions about scientific topics? With the rise of digital platforms as primary sources of information, understanding the differences in “science information diets” across diverse cultural and political landscapes has become essential. The study examined how people in different countries and regions access and share scientific knowledge, taking into account factors like digital media access, education levels, and press freedom.

Data was collected through an extensive online survey with representative samples for each country, accounting for age and gender. Participants were asked how often and where they encountered scientific information in the past year—whether through social media, news outlets, conversations with family and friends, fiction (like movies and books), or visits to museums and other cultural institutions. Additionally, the survey explored how frequently people discussed scientific topics with others, either privately or publicly, and how often they engaged in science-related protests, such as those on climate change.

One of the most striking findings of the study is that social media has become the dominant source of scientific information in most countries. This trend is especially evident in developing nations, where traditional media may be less accessible or trusted. Southeast Asian countries (such as Indonesia, the Philippines, and India) and many African countries stand out in this regard. In these regions, access to print or specialized scientific media is often limited, making platforms like Facebook, YouTube, TikTok, and Instagram vital for public engagement with science.

However, traditional media, such as newspapers and television, remain important in countries with strong media systems, such as the Scandinavian nations. In countries like Sweden, Finland, Switzerland, and the Netherlands, people are more likely to obtain scientific information from traditional sources like newspapers, TV news, and radio broadcasts. This is linked to a stable, pluralistic media landscape and strong public service support. Interestingly, factors like education, freedom of speech, and internet access did not significantly influence the choice of information sources.

The study also highlighted cultural differences in how people discuss science. In countries with more collectivist values, such as Japan, South Korea, and China, individuals are less inclined to engage in discussions about science. In contrast, people in African countries like Kenya, Nigeria, and Uganda tend to discuss these topics more openly within their communities.

The findings also indicate that people in countries with lower GDP are often more dependent on social media for science information. In countries with limited digital coverage, science-related information continues to spread through conversations and events at museums and public institutions.

The study concludes that social media is becoming a key tool for reaching a broader audience with science information, highlighting the importance of ensuring that content on these platforms is reliable. The authors recommend that scientists and experts engage more actively in sharing credible scientific information on social media to combat misinformation.

This summary is based on:

Mede, N. G., Cologna, V., Berger, S., Besley, J. C., Brick, C., Joubert, M., … Schäfer, M. S. (2024, October 17). Public communication about science across 68 countries: Global evidence on how people get information and communicate about science-related matters. https://doi.org/10.31219/osf.io/xb3ha