Marcos, a bilingual teenager growing up with two languages ​​and two distinct approaches to reading, keeps up relatively well in Spanish Language and Literature class: he reads slowly and makes some mistakes, but he understands the texts and participates without too much difficulty. In English, however, he hesitates over common words, loses his place in the text, and takes much longer than his classmates to write or decipher seemingly simple sentences. Furthermore, when asked to read aloud, he looks down even before it’s his turn.

These kinds of situations baffle many families and teachers. How can someone function relatively well in one language and experience significant difficulties in another? Can dyslexia manifest itself differently depending on the language?

Far from being anecdotal, both questions are appearing with increasing frequency in bilingual and multilingual contexts. They raise a central question for current research: to what extent do the difficulties associated with dyslexia depend not only on the brain that reads, but also on the writing system that the brain attempts to decipher?

Which languages ​​make dyslexia more visible?

The idea that a person can have dyslexia in English but not in Spanish may seem contradictory. However, current scientific evidence suggests that the difficulties associated with this learning disorder can be much more pronounced in some languages ​​than in others. Although dyslexia has a neurobiological basis—that is, it is related to how the brain processes written language—its manifestations can also vary depending on the characteristics of each language’s writing system .

This is because not all languages ​​exhibit the same degree of regularity in the relationship between letters and sounds: it is precisely this relationship that learning to read automates. In English, for example, the same groups of letters can sound very different ( drought and brought are not pronounced the same way, just as mint , lint , and hint are pronounced differently from pint ), which can increase the complexity of learning to read.

Relationship with writing

These differences affect not only reading but also how writing is learned. Neuroscientist Taeko Wydell has pointed out that in writing systems like Japanese, where learning combines motor repetition and oral pronunciation of characters, some difficulties can manifest themselves differently.

In this sense, documented cases of bilingual students who have difficulties in English, but not in Japanese, have contributed precisely to questioning the idea of ​​an identical dyslexia in all languages.

Transparent and not-so-transparent languages

Similarly, a recent study of bilingual Welsh-English adults with dyslexia showed that they exhibited a different reading profile than monolingual English speakers. The bilingual participants showed fewer difficulties in tasks related to phonological processing and reading pseudowords (invented words used to assess the processing of language sounds), likely because Welsh has a much more consistent, or transparent, orthography than English.

As a main finding, the authors concluded that learning to read simultaneously in a consistent, or transparent, language and in an inconsistent, or not so transparent, language can modify reading and writing strategies and alter the way in which difficulties associated with dyslexia manifest themselves.

In fact, current scientific reviews indicate that some difficulties, such as the speed of access to language sounds or certain alterations in reading fluency, tend to remain relatively stable across languages. However, other difficulties depend much more on the orthographic characteristics of each language, which explains why these differences may be more visible in some writing systems than in others.

When dyslexia goes unnoticed

In consistent languages ​​like Spanish (that is, languages ​​in which the sounds of letters and their combinations remain almost always regular), many people are able to read with relative accuracy from an early age. However, this apparent normality can be deceptive.

Although reading demands more time, greater concentration, and increased cognitive effort, it may seem functional from the outside. However, as has been documented for years in numerous studies , this does not mean the problem disappears. Rather, students often manage to partially compensate for these difficulties for years, which explains why some achieve good academic results and yet still experience significant reading fatigue.

However, the situation changes when these students encounter more inconsistent spellings. Several studies with bilingual individuals indicate that difficulties tend to become more apparent in languages ​​with more irregular spellings. This leads to the emergence of errors that previously seemed nonexistent, and to the development of different compensatory strategies depending on the specific spelling characteristics of each language.

Therefore, it is not surprising that some cases of dyslexia are detected precisely during the learning of a second or third language since, in reality, it does not “appear” suddenly but rather, certain linguistic characteristics simply make previous difficulties much more visible.

Languages ​​that hide versus languages ​​that reveal

Having reached this point, we can recognize that the initial question contained a misleading idea: thinking that dyslexia belongs to a specific language .

Dyslexia has a neurobiological basis related to language processing, but its manifestations also depend on the characteristics of the writing system each reader encounters. Therefore, a person may appear to be a proficient reader in Spanish and experience significant difficulties in English. This is not because they are dyslexic only in English, but because some languages ​​act like a magnifying glass , highlighting difficulties that until then remained partially hidden, while others manage to conceal them for years.

And perhaps that is one of the most important ideas that current research brings: understanding dyslexia not only involves understanding how the reading brain works, but also how it interacts with the different languages ​​it learns to decipher.

Ultimately, understanding how the brain and written language interact in each language will not only help to debunk past and future myths, but also to improve the educational response in increasingly linguistically diverse classrooms.

Author Bio: Diego Paniagua Martín is Clinical Linguist | Expert in Linguistic Competence and Disability at UNIR – International University of La Rioj

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When a plumbing issue arises, the decision between calling emergency plumbers or opting for regular Indianapolis plumbers can be critical. Whether it’s a burst pipe in the middle of the night or a leaky faucet during the day, the type of service you choose can significantly affect both cost and convenience. This article will explore the differences between these two types of plumbing services, highlight situations that necessitate emergency intervention, delve into the cost implications, and guide you on selecting the right service for your specific needs.

Comparing Emergency Plumbers and Regular Plumbers in Indianapolis

Understanding the distinction between emergency and regular plumbing services is crucial for homeowners. Emergency plumbers are available 24/7, ready to address urgent issues like severe leaks or clogs that can lead to extensive water damage if not immediately resolved. Regular plumbers, on the other hand, typically operate during standard business hours and handle routine maintenance and non-urgent repairs. When considering plumbing services in Idianapolis Plumbers, choosing between these options depends on the urgency of the situation and your ability to manage a potential delay.

The flexibility of emergency plumbers in terms of availability is a significant benefit. However, regular plumbers offer the advantage of scheduled appointments, allowing for better planning and integration with other home-related services, like a Home Automation Integration system. This can be particularly beneficial for those with intricate home setups to ensure all elements function harmoniously.

For non-urgent situations, regular plumbers can efficiently handle tasks like an Indoor Air Quality Test or routine pipe maintenance.

Key Situations Requiring Emergency Plumbing Services

Certain scenarios demand immediate attention from emergency plumbers to prevent further damage. Burst pipes during winter months are an example, as they can lead to flooding and substantial property damage. Additionally, severe toilet clogs or water heater malfunctions can disrupt daily routines and may require immediate resolution.

In homes with children, addressing plumbing issues promptly is vital for safety and comfort. A Childproofing Assessment often identifies potential hazards, including plumbing issues that could affect child safety. Similarly, homes with advanced Smart Home Security Protocols may experience disruptions if water-related systems are compromised, necessitating quick intervention by emergency plumbers.

For those living in older homes, issues related to Lead-Based Paint Compliance may arise in conjunction with plumbing problems, requiring specialized emergency services to ensure both water safety and compliance with health regulations.

Cost Considerations for Emergency vs Regular Plumbing

One of the significant differences between emergency and regular plumbing services is cost. Emergency plumbers generally charge higher rates due to their around-the-clock availability and the urgent nature of their work. This can be a critical factor for homeowners evaluating their options.

However, the cost of delaying necessary repairs might outweigh the premium paid for emergency services. For instance, a burst pipe can escalate into a costly repair if not addressed immediately, potentially affecting Eco-Friendly Landscaping or causing damage to Modular Furniture Design elements in the home.

Regular plumbing services are more economical for planned maintenance and non-urgent repairs. By scheduling regular inspections, homeowners can prevent costly emergencies and maintain the efficiency of their Energy Efficiency Audit results.

Choosing the Right Plumbing Service for Your Needs

When deciding between emergency and regular plumbing services, consider the urgency of the issue and the potential impact on your home. Emergency plumbers are ideal for immediate threats to home safety or functionality, especially when time-sensitive solutions are necessary.

For ongoing maintenance or less critical repairs, regular plumbers offer a cost-effective and reliable solution. Furthermore, engaging with regular services can help in planning Emergency Preparedness Planning for your home, ensuring that plumbing issues are minimized and managed effectively.

Ultimately, the choice between emergency services and regular Idianapolis Plumbers depends on your specific circumstances and priorities. Whether it’s an urgent repair or scheduled maintenance, having a clear understanding of your needs and the available services is essential.

Conclusion

Choosing the right plumbing service involves weighing the urgency of your situation against cost and convenience. Emergency plumbers provide immediate solutions for critical issues, while regular plumbers offer efficient service for planned tasks. By understanding these differences, you can make informed decisions that protect your home and family’s well-being.

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● The presence of ‘AI influencers’ benefits marketing, but triggers copyright infringement.

● Deepfake and voice cloning technologies have the potential to violate personal data protection laws.

● AI-based virtual characters are at risk of spreading disinformation, and legal liability rules are needed for their developers.

The emergence of artificial intelligence (AI) influencers demonstrates that AI development has entered a more complex phase than chatbots or digital image generators . AI can now create lifelike virtual figures: with faces, voices, speaking styles, and even personalities that can interact with the public like real people .

These artificial intelligence-based digital figures are generally designed to create virtual personas with identities, characters, and social media presence. Among brand owners, digital marketing agencies, and advertising professionals, AI influencers are seen as an efficient alternative for digital marketing campaigns.

AI influencers are used to market products, build brand image, influence public opinion, and shape consumer behavior.

Various studies show that virtual influencers offer a higher level of control over brand messaging, content consistency, and reduced reputational risk.

Unlike human influencers who have personal lives, political views, and the risk of controversy, AI influencers can be tuned to always align with a company’s communication strategy.

However, generative AI technology is now capable of replicating a person’s face, voice, expressions, and even creative character with a level of similarity that is difficult to distinguish from reality.

As a result, the issues that arise are no longer just technological innovation, but also the exploitation of human identity in the digital space.

When AI “eats” human work

Most generative AI systems work by studying millions of data sets from the internet , ranging from photos, music, videos, illustrations, to writing.

This data is used to train AI to generate new, natural-looking content. The problem is, many of the works used in the AI ​​training process are copyrighted and taken without the consent of their creators.

Under Law Number 28 of 2014 concerning Copyright , creators have the exclusive right to reproduce, adapt, distribute, and publish their creations. When a work is used to train an AI model without permission, the question arises whether this process can be considered a form of copyright infringement.

The problem becomes more complicated when AI begins to mimic the distinctive style of a particular creator. For example, various generative AI models are capable of producing images that resemble the style of famous painters like Vincent van Gogh or the work of the animation studio Studio Ghibli .

In the music industry in 2023, the AI ​​song “Heart on My Sleeve” made global headlines for imitating the voices and vocal characteristics of Drake and The Weeknd without the involvement of the two musicians.

This style mimicry phenomenon raises new concerns: AI is not only using human work but also replicating the creative identity of its creator. Copyright law protects not only the economic value of a work but also the moral rights of the creator, including their creative integrity and reputation.

Human faces and voices can now be faked

The issue of AI influencers becomes more serious when deepfake and voice cloning technologies allow AI to imitate a person’s face and voice simply from pieces of digital data available on the internet.

In some cases, this technology has been used to create fake videos of public officials, synthetic pornographic content, and even advertisements using people’s faces without their consent.

In Indonesia, this issue is directly related to Law Number 27 of 2022 concerning Personal Data Protection (PDP Law) . A person’s face and voice can be categorized as biometric data that receive special legal protection.

This means that using someone’s identity to build an AI avatar without permission is no longer just a digital ethics issue, but has the potential to be a legal violation.

Worse still, digital identity exploitation now targets more than just public figures. Anyone can become a victim. At this point, the line between reality and digital manipulation is becoming increasingly blurred.

New threats to digital democracy

AI technology allows someone to create fake videos that appear authentic. For example, a deepfake video of a public official making a specific statement. AI can also be used to create political propaganda, digital insults, and disinformation that is difficult for the public to verify.

In the Indonesian context, this condition is related to Law Number 1 of 2024, the Second Amendment to Law Number 11 of 2008 concerning Electronic Information and Transactions (ITE Law) , particularly regarding the manipulation of electronic information, the spread of fake news, and defamation.

The problem is, AI development is progressing much faster than the law’s ability to anticipate its impact.

AI cannot be held liable because it is not a legal entity. AI has neither consciousness nor free will. So, when AI is used to create deepfakes , forge identities, or exploit the work of others, who is responsible?

The current global debate is starting to move towards the concept of shared liability , namely shared responsibility between AI development companies, digital platforms, and end users .

AI companies can be held liable if they use illegal data or fail to establish safeguards against misuse of the technology. Digital platforms also bear responsibility if they allow the distribution of infringing content without adequate oversight.

While AI users can still be considered the most responsible parties when intentionally using AI to defame, create synthetic pornography, or exploit the identities of others for economic gain.

AI must remain subservient to humans

In the latest discussions of the World Intellectual Property Organization (WIPO)—the international organization that develops global intellectual property rights norms and policies—the issue of AI and synthetic media was one of the main agenda items.

WIPO highlights the need for a balance between technological innovation and the protection of creators’ rights, the right to identity, and the public interest in the era of artificial intelligence. One principle being emphasized is that AI must remain a tool that serves humans, not replace or exploit them.

Therefore, the biggest challenge in AI development is how to maintain human values ​​amidst increasingly limitless technological capabilities.

Today, AI can create virtual humans. Tomorrow, AI may influence how humans perceive reality itself.

If the law fails to establish clear boundaries, then the future of AI will no longer be simply about technology, but about how humans maintain control over their identity, creativity, and dignity in the digital age. is

Author Bio: Rianda Dirkareshza is Coordinator of the Business Law Study Program, Undergraduate Program, Faculty of Law at the National Development University Veteran Jakarta

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George Saunders won the Man Booker prize in 2017 with his novel Lincoln in the Bardo. Earlier that year he wrote about his revising process.

My method is: I imagine a meter mounted in my forehead, with “P” on this side (“Positive”) and “N” on this side (“Negative”). I try to read what I’ve written uninflectedly, the way a first-time reader might (“without hope and without despair”). Where’s the needle? Accept the result without whining. Then edit, so as to move the needle into the “P” zone. Enact a repetitive, obsessive, iterative application of preference: watch the needle, adjust the prose, watch the needle, adjust the prose (rinse, lather, repeat), through (sometimes) hundreds of drafts. Like a cruise ship slowly turning, the story will start to alter course via those thousands of incremental adjustments.

The artist, in this model, is like the optometrist, always asking: Is it better like this? Or like this?

The interesting thing, in my experience, is that the result of this laborious and slightly obsessive process is a story that is better than I am in “real life” – funnier, kinder, less full of crap, more empathetic, with a clearer sense of virtue, both wiser and more entertaining.

And what a pleasure that is; to be, on the page, less of a dope than usual.

Revision, Saunders says, is The Work. It is the writing. Revising isn’t a final polish once the real writing is done, not a tidy-up before submission. You know, you write something, you read it, you make it better, then you go again. And, he implies, there is no moment when the draft signals it has had enough. You have to decide when enough is enough.

Doctoral researchers are not necessarily taught to think about revision this way. Many treat a draft as a rough version of what they will eventually submit, a sketch that needs tidying up rather than a text to be repeatedly worked over. Going through something many times, making fractional adjustments on each pass, sounds pretty inefficient. And yet Saunders, who has spent decades attending to sentences, treats it as entirely ordinary.

The second thing Saunders does is stop thinking about himself. The reader becomes the only concern. His phrase for the state he tries to reach is “without hope and without despair,” ( he borrowed that from Chekhov). This means reading the text for what it actually is rather than what he feared or wished he’d written. And this means suspending his own investment in the argument and attending instead to what a first-time reader will actually see on the page.

Doctoral researchers can find the switch to thinking about the reader difficult, for reasons that make complete sense. Living with an argument for months or years, knowing the literature inside-out, seeing how all the pieces connect, all of that knowledge gets in the way of you reading your own prose with a critical eye. The reader in your head already knows what you know, supplies the links you’ve left out, follows logic that you haven’t actually spelled out.

Letting a draft sit helps. So does an alt. reader who doesn’t share your expertise, because they read what is actually on the page rather than what you intended.

Getting the distances Saunders recommends requires an attitude shift. Saunders says his process is like that used by an optometrist. When you are sitting staring at the letters on the wall chart the optometrist gives you a choice of lens and asks you, “Is it better like this? Or like this?” They’re the questions to ask in revising. The judgement is always comparative. Your decision is  not whether the text is good in an absolute sense, but whether this version is better than that one. And in particular, is it better for a reader coming to it without your prior knowledge?

The third thing about Saunders’ process is that it requires trying things and abandoning them. Make an adjustment, check the needle, keep it or undo it, move on. Many adjustments will do nothing. They won’t move the needle. That’s not a problem, Saunders says, it’s how this kind of testing out process works. No single change is decisive. Improvement is cumulative.

There’s an important message here for doctoral researchers working to deadlines. Revision takes longer than most people allow for. Time is spent on drafting, and revision gets whatever hours are left before the due date, which is often not many. The result is a text where the decision-needle is still somewhere in the middle of the dial. The answer is to finish drafts earlier than feels necessary. Submitting a chapter to your supervisor two weeks before the deadline rather than the night before is a good idea. Let it sit, read it cold and make dozens of small adjustments instead of one panicked midnight pass through the text. Earlier isn’t just better in principle; it changes the kind of revision you can do.

The final thing is that, after all that revising work, the text that comes out is pretty much guaranteed to be better. For Saunders this means funnier, kinder, more empathetic, less full of crap. “What a pleasure,” Saunders writes, “to be, on the page, less of a dope than usual.” For an academic writer, revising means a text that is more authoritative, persuasive, well reasoned, elegant, interesting. And yes, the academic writer also seems like less of a dope.

So Saunders’ lesson is this. Trust your revising process, give it enough time, and it will take you somewhere you couldn’t imagine or see from that first draft.

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As an educator, I attest to the remarkable aspects of the teaching profession. Teachers truly have the capacity to be agents for positive change in this troubled world.

That said, a myriad of factors have left the teaching field in a global state of occupational precarity.

Factors include strenuous impacts from the COVID-19 pandemic, inadequate funding and limited resources, escalating school violence, increased public surveillance as well as parental distrust often amplified by social media hostility.

While teachers’ primary responsibility is to teach, the profession is ever-evolving and more socially complex than is often publicly understood.

Although there has been ample coverage about many of these issues, seldom discussed is how existing school infrastructures constrain collegial practices and contribute to teacher isolation. This was the topic I explored in my award-winning doctoral dissertation.

Wide-ranging tasks

Teachers’ roles and responsibilities are far more expansive than just teaching, but such wide-ranging tasks remain invisible.

In Manitoba, where my study was situated, the provincial government released its teacher competencies regulation in 2025.

This framework mandates teachers to not only lead curricular instruction, but also to safeguard students’ well-being, routinely consult and collaborate with Indigenous communities, participate in peer mentorship and engage in ongoing professional learning — among other diverse occupational responsibilities.

As per Manitoba teachers’ provincial collective agreement, teachers are professionally obligated to routinely participate in school committee work, attend faculty meetings, liaise with parent/community groups and other miscellaneous organizational functions.

These ethical endeavours are corroborated by the Manitoba Teachers’ Society. The union code of professional practice mandates teachers to “[develop] an environment that is free from all forms of discrimination, hate and oppression.”

Teachers’ professional agency

Co-facilitating social justice leadership is increasingly understood to be a responsibility of teachers’ daily practice and professional identity. Whether addressing queerphobia or anti-racism, advancing such efforts are integral to ensuring all students are safe, included and ready to learn.

There is a need for teachers to be able to routinely congregate to engage in communication and dialogue that supports and enacts all their professional obligations. Being able to exercise knowledge and work within cultures grounded in dialogue are essential to promoting teachers’ professional agency.

Yet while teaching is an inherently social enterprise, teachers are infrastructurally siloed. They navigate school systems generally unconducive to collegial leadership practices.

Manitoba teacher study

There has been ample research documenting the importance of teachers’ collegiality as well as their agency and voice as vehicles to augment professional practice.

As well, researchers like educational theorist Henry Giroux have underscored how teachers’ perspectives as public intellectuals matter for fostering students’ understanding of what it means to live in a democratic society.

But how are teachers able to live these ideas? My study interrogated the ill-defined boundaries of teachers’ professional roles in engaging in broader social justice advocacy through collective school leadership initiatives.

As both a doctoral student and a music teacher, I worked with a seven-person participatory research team of teacher-colleagues. We used the photoVOICE method, which involved documenting our experiences relating to grassroots leadership in our schools through photography and later reflecting upon our perceived impacts and the barriers we encountered. Together, we generated insight about how schools may be consequently improved.

My colleagues participated in tasks including: peer mentorship related to anti-racism education, organizing assemblies and school gender and sexuality alliance groups, designing and facilitating professional development sessions on equity and social justice topics, and inspiring students and colleagues to become involved in local causes.

Teacher-colleagues in my study yearned to professionally connect with one another. They were committed to participating in collective leadership campaigns aimed at cultivating equitable and inclusive school communities. They also reflected on the barriers impeding such work.

Teachers in my study found that engaging in collegial tasks was consistently deferred to lunch hours or after-school meetings. Another barrier was their limited ability to freely express ideas that may enrich or improve schools and students’ lives without fear of penalty.

Constraints they perceived in being able to publicly express dissenting opinions led them to withdraw and feel demoralized. Self-censorship and fear of reprimand also contributed to their hesitance to collaborate on or support initiatives.

It is clear that teachers are expected to engage in highly social, interactional and equity-seeking tasks both in- and outside classrooms, but there are gaps that can prevent them from fulfilling these functions.

Circumventing structures

Being isolated is exacerbated in my field of music teaching, as many music teachers often serve as the sole instructor in their school community.

These tensions can be circumvented, however. My teaching practice has benefited from collaborations with community musicians, including ongoing projects with an Elder to support Indigenous ways of knowing and music-making, and a tabla instructor to support a large demographic of South-Asian students.

These experiences have led to my own professional growth, enhanced morale, new friendships and greater student engagement and learning.

Cultures of collaboration

As our society navigates post-pandemic occupational burnout and strenuous social tensions, it is time to re-imagine our school structures to be better conducive to collaboration.

Teachers need more scheduled time to liaise with their peers. They need to be able to participate in professional and reflective dialogue, collaborate on larger school functions outside the classroom, and exercise their informed professional perspectives without fear of reprisal.

At the grassroots level, school principals should encourage teachers to seek new professional learning communities, network with mentors and/or mentees and participate in professional peer dialogue.

The world is changing, the socio-emotional needs of students are increasingly complex and the teaching profession is also changing. No individual leader can address all of these interconnected social challenges facing schools.

Rather, there is power in the collective. We need to reduce workplace isolation to support teachers’ collegial capacity to mitigate loneliness, bolster engagement and professional learning, and contribute to more socially just and equitable school environments.

Author Bio: Jordan Laidlaw is a Ph.D. student, Educational Administration at the University of Manitoba

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When I was growing up, teachers would assign research papers that required going to the library, or later, searching for relevant material on the internet. If the paper was going to turn out well, we students needed to patiently comb through piles of material, weaving what we found into a coherent argument that was well-supported with evidence.

Unbeknownst to us at the time, our teachers were giving us a chance to develop our patience.

That chance is rapidly disappearing with increased use of artificial intelligence tools. Now you can have an AI do everything from school assignments to legal writing, sermon preparation, vacation planning, work emails and academic research. Researchers are already documenting how using AI tools in these contexts likely erodes critical thinking skills.

But what hasn’t been appreciated is AI’s effect on patience. As a philosopher who has written extensively about virtue, including the virtue of patience, I am especially concerned about what people can do to resist this trend.

What is patience, and why is it important?

Patience involves responding calmly when it is taking longer than you want to accomplish your goals.

When I am stuck in a traffic jam, or the checkout line is barely moving, I might wish that I was meeting my goals faster, but my calm demeanor is a sign that I am being patient. If I react to delays like these with frustration or anger, that is a sign that I am being impatient.

The same applies in the case of doing research. If it is taking me awhile to find everything I need, that can test my patience. But if I react to such a delay with calmness, I avoid frustration or anger and hence impatience.

Philosophers, theologians and educators have long considered patience an important character trait to cultivate. It is a virtue that contributes to well-being. More specifically, researchers have linked it to a variety of good outcomes, including healthier lifestyles, greater emotion regulation, more fulfilling relationships, increased caring about equity and justice, increased cooperation, greater purpose in life, lower depression and higher life satisfaction.

Why AI tools erode a capacity for patience

AI tools are helping foster a culture of immediacy, thereby diminishing the capacity for patience. Admittedly, we already started down this path with the dawn of the internet and the launch of fast and easy search engines. But now, AI instantaneously delivers fully developed answers, further reducing the delays once experienced as people searched, assessed and integrated information from various sources.

The training in patience that people used to get from thorough research and investigation is being replaced by a growing sense of impatience with thinking that takes time and effort. And this impatience doesn’t just stop with research. It extends to writing as well.

Research on AI and patience is still in its infancy. But my conclusions about these impacts rest on plausible inferences from what researchers know more generally about cognitive psychology. For instance, psychologists have long understood that people’s expectations change due to repeated use and exposure to something.

This adaptation explains why the hourlong train ride to work can start out as exhausting, but become part of your daily routine. Or you might initially be impressed by how fast your new computer is, but after a while you take it for granted and get frustrated if loading a PowerPoint presentation takes even a few moments.

Hence using AI tools is likely to recalibrate what feels normal to you. In particular, it is likely to normalize getting immediate, fully formed answers to your questions. This shift, I contend, makes people increasingly impatient with the very tasks of research and investigation that helped train us to become more patient in the past.

One concrete illustration of this change is with students. If a professor gives an assignment involving interpreting an author’s text and then developing a critique of the author’s position, students today are very tempted to offload the patient work of interpretation and critique to an AI.

Or consider sermon preparation. Pastors normally take hours a week to examine the original language for their text, consult commentaries, develop illustrations and examples, and deliberate about practical applications. Now, this process can all be done in a matter of seconds using AI, and one study found that a majority of pastors are using it for sermon preparation. There is no patience training happening here.

What can be done?

There are ways to cultivate patience in the age of AI tools, but they will not be easy. Here are three:

• Deliberately choose a slower path. Select this option because it comes with intellectual struggles, not in spite of them. Don’t rely on AI summaries or shortcuts, but try to come up with the answers on your own. This choice needs to be deliberative since the default human tendency is to take the easier route. But the long-term benefit is worth the short-term cost.
• Design your environment. Remove AI tools from your surroundings and carve out dedicated time free of distractions and notifications. Reading and writing take time, and by being willing to invest that time and not get impatient with how long it is taking, you can cultivate patience.
• Encourage and reward intellectual engagement. Institutions such as schools and churches have a structural role to play. The more such institutions can resist integrating AI tools into every aspect of their operations, and instead incentivize human intellectual engagement even at the expense of efficiency, the better as far as patience is concerned.

There is one other hopeful suggestion. Patience can be developed in lots of different areas of life that have nothing to do with research and which are less susceptible to AI incursion. Working on a craft project, detailing a car, weeding a garden, practicing your basketball shot, lifting weights – all these activities can foster patience too. The more this character muscle is strengthened, the more it will be available to use in many different areas of your life.

Author Bio: Christian B. Miller is Professor of Philosophy at Wake Forest University

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Everyone in academia knows Reviewer 2. You ‘ve probably received their comments. You may even in a dark moment, have been them. This post is a practical guide to achieving full Reviewer 2 status. It’s offered in the spirit of knowing your enemy.

Reviewer 2 has an attitude. Even before they have read the abstract, even before they have looked at the data or followed the argument, they know what they are going to recommend. Rejection, or something close to it.

The reviewing task is actually pretty straightforward.  You see, Reviewer 2 doesn’t read the paper. They read at it. They form a view, and spend the rest of the manuscript looking for confirmation. This is efficient. It saves time. And it produces feedback that might bear only a tangential relationship to what the author actually wrote. But no matter. If you want to be Reviewer 2, practice this selective attention. You know the position to take. Right from the very start you must critique, not appreciate. Then you just have to gather the evidence for a rejection or major revisions,

As Reviewer 2 you read the paper looking for what it does not do. You are not primarily interested in what it does. Now, this is surprisingly easy. No paper does everything. Every study has a scope, a sample and a set of questions it chose to ask and a longer list of possible things that were set aside. Reviewer 2 loves a longer list. It’s so easy to say “The paper does not address X. It fails to engage with Y. It overlooks Z entirely. There aren’t definitions for A, B and C” Even though these omissions were often deliberate and explained, or reasonable given the research design, or the paper refers out to a place where you can find all the details, you assess the paper for what is not there.  And absence is soooo easy to find.

The methodology section deserves special treatment. As Reviewer 2  you should always find it inadequate.  You’re not looking to understand what the researcher did and why, but to locate the gaps between this study and some other study, the ideal one, one which used a different paradigm. (It’s probably the one you would have designed). The sample is too small, or too large and therefore superficial, or drawn from the wrong population, or described in too much detail when you wanted less, or in too little detail when you wanted more. The point is not to engage with what the researcher actually did and why. The point is to invoke a different study and hold the submitted paper against that imaginary standard. This is very effective. The author cannot respond to a study that does not exist.

Citations are your other great resource. Reviewer 2 and their friends are almost always missing from the reference list. This is an oversight that must be corrected. You should recommend three to five papers that speak directly to the topic.  It is purely coincidental that two of them have your name on them. Frame this as a gap in the literature review. Do not explain what these papers contribute or how they would strengthen the argument. The author can work that out. Your job is to note the absence.

Tone is important. Reviewer 2 writes as if the paper has tired them out. “The author seems unaware of” is a reliable construction. So is “this is well-trodden ground,” deployed regardless of how recently the ground was last trodden. The rhetorical question works well: “But what does this actually tell us about practice?” That’s not a genuine question by the way. It’s a weary sigh formatted as inquiry.

Your recommendation should not quite match the substance of the review. If you have identified what sound like fundamental problems, you can recommend major revisions rather than rejection, because this keeps the paper in play long enough to demoralise the author across multiple rounds. But you can of course reject it outright and note that the work is not ready for publication in this journal.

The reason Reviewer 2 persists is that sometimes there really is a problem with the sample or an argument or the literature. Sometimes a theoretical framework does need more development. Reviewer 2 survives because their criticisms sometimes do find something real, even when their concerns were written without care. This is what makes their feedback so difficult to dismiss and so exhausting to receive – writers cannot always tell which objections matter.

Good reviewing of course starts from the opposite position. Reviewer 1 reads to understand what the paper is trying to do before assessing how well it does it. R1 engages with the methodology on its own terms, which means asking whether the choices made were reasonable for these research questions and paradigm, not whether they would have made different ones. When R1 identifies an issue, they say precisely where it is and why it matters for this argument, not just that it exists. They recommend papers to help, not because they wrote them or their mates did or they are out there and they are their favourites. And R1writes in a register that acknowledges an actual person did this research and wrote this paper and will have all the feelings about whatever is said.

The anonymity of peer review is at issue here. Peer review can produce honest generosity, the kind that is straightforward because there is no social cost to being fair. It can also provide the freedom to be dismissive, even rude, without consequences. Reviewer 2 is what happens when a reviewer chooses the second option and mistakes it for high standards.

The test of good peer review is whether the reading and comments helped the author understand what the paper is doing and what it still needs. That requires reading the paper that has been sent, which is where Reviewer 2 is always unwilling to start.

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Having Black teachers and other educators of color improves students’ classroom experiences, research shows. They often serve as role models, set high academic expectations and teach material that connects to students’ lives outside of schools.

This can lead to higher standardized test scores, better school attendance and more classroom engagement – particularly when it comes to students who share their teacher’s racial or ethnic background, but also for all students.

Yet over the past four decades, the teacher workforce has barely become more diverse, even as the student population has changed.

In the late 1980s, about 70% of public school students and over 85% of teachers were white. Today, the public teacher workforce is still around 80% white, compared with fewer than 50% of students.

I am a scholar of education policy who studies policies affecting Black and other teachers of color, including strategies for diversifying the teacher workforce.

My colleagues and I recently published two studies that help explain why the teacher diversity gap is so difficult to close, despite programs designed to do so.

Even with local district and state-funded teacher recruitment programs that aim to bring in more teachers of color, it is not easy to reverse a workforce gap that took decades to create. Black teachers were pushed out of classrooms during school desegregation in the 1950s through 1970s, resulting in long-lasting effects that shape how a classroom looks today.

This legacy is rarely discussed in teacher recruitment policies that state they support diversity goals. As a result, they often fail to confront or address the racial inequalities that created the gap in the first place.

A history that still shapes the workforce

In 1954, the Supreme Court’s Brown v. Board of Education ruling struck down state-sanctioned segregation in public schools. The landmark decision opened school doors for students of color.

But desegregation also closed many doors for Black teachers.

Before this ruling, teaching was one of the most accessible and respected professions available to Black Americans, especially Black women, who taught in segregated Black schools. Most other fields, from law to medicine, were largely closed to Black workers by formal exclusion and discrimination.

Teaching became one of the few reliable paths to middle-class stability.

In the 1960s and 1970s, many segregated Black schools across the South and border states closed or merged with white ones.

As this happened, thousands of Black teachers and principals were dismissed or demoted. Some estimates put the number of educators pushed out of the profession as high as 100,000.

Desegregated schools in those same regions often kept white teachers and let Black teachers go, a pattern that education scholar Leslie Fenwick has called “Jim Crow’s pink slip.”

A common strategy

Today, all 50 states, in addition to Washington, have “Grow Your Own” programs – public initiatives to recruit local community members, including current students or school staff, into teaching positions.

Grow Your Own programs have roots as far back as the 1940s, led by the country’s largest teachers union and a network of high school clubs.

The basic idea is simple: Because teachers are more likely than other professionals to work near their hometowns, recruiting local community members into teaching may help grow the teacher workforce.

Educators raised near the schools they teach in often better reflect the demographics of the communities they serve than teachers who come from farther away.

In recent years, most of these teacher recruitment programs have focused on encouraging high school students to pursue a teaching career through elective courses that introduce students to teaching as a career.

Other types of Grow Your Own programs include college scholarships where recipients commit to teaching in the same geographic area or state following graduation. Some programs support classroom aides and other school staff who want to become full-time teachers by enrolling them in required university-level teacher-preparation coursework.

By definition, nearly every program intends to expand the teacher workforce. About half also list increasing diversity as an explicit goal.

While Grow York Own programs are gaining popularity at state and district levels, unfortunately there is no good national count of how many people participate in them.

The Teacher Academy of Maryland

Since 2004, nearly 2,000 high school students per year have enrolled in the Teacher Academy of Maryland, a program my colleagues and I study. These students take high-school-level classes on child development and teaching, alongside their regular coursework. They complete internships in nearby K-12 classrooms, and they can simultaneously earn college credits that transfer to a teacher-preparation program.

The hope is that more high school students will decide to become teachers – a possible solution to a declining teacher workforce. The Teacher Academy of Maryland also is part of a statewide policy push to diversify the teaching profession.

Against this backdrop, my colleagues and I asked three questions in one study published in 2026: Does the Teacher Academy of Maryland actually increase the number of new teachers? Does it diversify the teacher workforce? And what can other states and programs learn from a program like this?

We compared students who had the option to join the Teacher Academy of Maryland program with students just a couple of years older who attended the same high school, but graduated before the program began in that school.

Students enrolled in a school offering the program were about 45% more likely to become teachers than their older peers who did not have the option to join the program. We interpret this effect as large, especially since only 1.4% of Maryland high school students overall become teachers.

The program helped students finish high school and enroll in college. It also steered some students who might have become classroom aides toward full-time teaching positions.

However, over the period we studied, the effects on entry into teaching were over twice as large for white students compared with Black students. The program produced more new white teachers than new Black teachers. It expanded the teaching pipeline in proportion to the students who enrolled, most of whom were white.

Why the gap is not closing

Our follow-up study, also published in 2026, spells out what it would actually take to boost the number of teachers of color.

To fulfill Maryland’s goal of diversifying the teaching profession, recruitment programs would need to focus specifically on Black and other students of color. That could mean concentrating resources in majority-Black districts like Baltimore City.

So far, the Teacher Academy of Maryland has run in only a few of Baltimore’s 30-plus public high schools.

Race-conscious policies face serious headwinds today. Affirmative action is illegal in college admissions and is being challenged in K-12 teacher hiring.

That said, treating teacher diversity as a numbers problem alone will not close the gap. The roots run deeper: Segregation and exclusion have shaped who is in the classroom today.

Lessons for policymakers and program leaders

Our research points to a clear lesson: Programs to address teacher shortages and programs to diversify the workforce are not the same thing.

States, school systems and Grow Your Own programs that take teacher diversity seriously should design their work specifically with that goal in mind. In practice, that means recruiting Black and other students of color directly. And it means being honest that closing this gap will take decades, not a single grant cycle.

Author Bio: David Blazar is Associate Professor of public policy and education at the University of Maryland

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What becomes of public language when a growing proportion of the texts circulating in the press, on the internet, and on social media begin to be written by machines? This question doesn’t just concern journalism as a professional activity. It can also affect the richness of the language we use to understand, describe, and debate reality.

Historically, the press has been one of the spaces where common language has developed and been enriched. It is obviously not the sole driver of linguistic change, but it is one of the places where societies circulate new words, new expressions, and new ways of naming emerging phenomena. Several studies on journalistic language and neologisms show that newspapers have long played a crucial role in the creation and dissemination of new vocabulary, particularly when it comes to reporting on events, technologies, or social transformations to a broad audience.

This role could be weakened if a significant portion of journalistic writing were delegated to generative AI systems. Large language models generally rely on predicting the most probable word—or more precisely, the most likely token—within a sequence. They thus produce fluent and plausible texts, but also tend to favor statistical regularities, the most frequent formulations, and already established turns of phrase .

This does not, in itself, mean that language automatically degrades. The problem arises when this logic becomes dominant in the production of texts that populate the public sphere.

When AIs train on texts produced by other AIs

The risk becomes more serious when these systems start being trained on texts produced by other AIs. This is what several recent works describe as model collapse : a degeneracy process in which the data generated by a model ends up contaminating the training of subsequent generations .

Applied to language, this means that if systems increasingly learn from synthetic texts, and if these texts eventually saturate the web and public spaces, the linguistic reservoir available for future training shrinks. The more artificial texts there are, the less the models are exposed to the real diversity of human language use. Ultimately, this can lead to an impoverishment of language in various domains.

Reproduction and amplification of biases

First, when data diversity decreases and models rely primarily on pre-established patterns, biases present in the training data are likely to be reinforced rather than corrected. Recent literature on the evolution of language models specifically warns against the fact that recursive processes can amplify existing biases instead of diversifying perspectives.

Furthermore, writing tends to become increasingly repetitive : the same syntactic structures, the same intermediate tones, the same formulations, and the same ways of organizing paragraphs recur constantly. This evolution is particularly important for journalism, because the press does more than simply transmit information: it connects specialized knowledge with a broad public, prioritizes issues, translates technical vocabulary, and experiments with new formulations. When the language of the public sphere becomes too uniform, its capacity to adapt subtly to novelty weakens .

An erosion of linguistic innovation

In this context, rare or specialized words, less frequent constructions, and certain pragmatic nuances—such as irony, ambiguity, or variations in perspective—tend to be absent. The increased proportion of synthetic texts in the training data is associated with a decline in performance and a poorer representation of the diversity of human language. Simply put, the system preserves the center better than the margins .

However, many linguistic innovations arise precisely in these margins: in the form of unstable usages, occasional repurposing, or local solutions invented to name a new reality. If the system systematically favors the most probable formulations, these emerging forms have less space to circulate and establish themselves .

This issue should not be understood as an abstract opposition between “human” and “machine”, but rather as the difference between a language nourished by the contingencies of social life and a prose produced from already learned regularities.

An impoverishment of the linguistic ecosystem

The issue is not simply a reduction in the number of different words. It also concerns the ability to make subtle distinctions. When language becomes vaguer, more repetitive, or more predictable, the tools a society has to describe problems, nuance positions, and debate in the public sphere are also diminished.

On a broader scale, the question is therefore no longer simply what happens to an AI model, but what happens to the public language ecosystem as a whole. If the web becomes filled with synthetic texts, readers, journalists, and institutions will gradually be exposed to a less diverse public language. Some recent work even goes so far as to suggest a form of “contamination” of the digital ecosystem by synthetic data and shows that the way in which real and artificial data are combined is crucial to preventing more significant degradation .

An inevitable scenario?

However, the risk should not be exaggerated. Research does not conclude that all use of AI inevitably leads to collapse or degradation. Some studies show that when synthetic data is mixed with real data, rather than replacing it entirely, the degradation mechanisms do not manifest themselves in the same way, and errors can remain limited. In other words, the problem does not lie in the occasional use of AI or in a careful combination of synthetic and human data, but in the massive replacement of human writing followed by the recycling of this artificial output as if it were a living language.

With the integration of AI into journalistic production routines, journalism becomes more efficient. But what does a society lose when the language circulating in the public sphere becomes more uniform, more predictable, and less open to novelty? If the press relinquishes, even partially, its function of writing, translating, naming, and experimenting with language, it is not only professional practices that are transformed. It is also one of the main spaces where the common language has historically been able to enrich itself, renew itself, and expand its range of possibilities that is thereby weakened.

Author Bios: Xosé López-García specialises in Digital periodism, digital communication and Cristian Augusto Gonzalez Arias who is an Investigator, Pontificia Universidad Catolica de Valparaiso both at the University of Santiago de Compostela

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Every year, thousands of prospective students scour the websites of top schools and universities in search of the magic key: the right label, the right accreditation, the right ranking. AACSB , EQUIS , the Shanghai Ranking , Times Higher Education – all these acronyms promise excellence. But what do they actually measure?

The answer is less reassuring than it seems. Accreditation is the official recognition that an institution complies with a set of predefined standards and procedures. Labeling, on the other hand, is the certification of conformity to quality criteria in specific areas (internationalization, professional integration, innovation, etc.).

These systems therefore primarily assess compliance with rules, not the actual performance of the institution. This shift is not insignificant.

The “paradox of conformity”

Compliant and efficient: these two qualities are not synonymous. An institution can show mediocre results while strictly adhering to all the procedures of an accreditation or label – for example, at the university level, the “HR Excellence in Research” ( HSR4R ) label of the European Commission which values ​​institutions that have signed the “European Charter for Researchers” and the “Code of Conduct for the Recruitment of Researchers”, or the Sustainable Development and Social Responsibility Label DD&RS , launched in 2015 by a collective coordinated by France Universités and the Conference of Grandes Écoles.

Conversely, an innovative institution can deviate from required standards without necessarily offering lower quality education or an unsatisfactory societal impact.

Compliance would determine performance: this is the misleading shortcut that our research highlights.

What we call the “paradox of conformity” refers to this troubling phenomenon: the more labels and accreditations an institution accumulates, the less its actual performance – measured by student success, teaching quality or research impact – tends to improve .

For example, AACSB-accredited institutions optimize their compliance, not necessarily their services to students and society . According to a study of more than 2,300 students in twenty-four accredited business schools , 45% of students surveyed reported no significant improvement in their key skills – critical thinking, complex reasoning, and writing – during their first two years, and 36% after four years.

Risks of standardization and misuse

University rankings – whether it be the Shanghai Ranking, the Times Higher Education or the QS World University Rankings – amplify this paradox by adding several specific risks, which we document in a recent scientific publication .

The first risk is that the rankings are often unfounded. These rankings rely on a limited number of indicators—number of scientific publications, academic reputation, student-to-faculty ratio—which mechanically favor large, well-funded Anglo-Saxon universities. French universities, primarily state-funded and regional in scope, are structurally penalized.

Paradoxically, many of them continue to use these rankings as a compass, even though they objectively have little chance in this competition. They find themselves running a race whose rules were written for others. The League of European Research Universities (LERU), which brings together the best European universities, is clearly opposed to these rankings .

The second risk is standardization. By imposing the same criteria on all universities worldwide, these accreditations and rankings push institutions to become more similar, to the detriment of their unique characteristics. This phenomenon leads to the emergence of a “single ideal of a university” that corresponds to the reality of only a handful of global institutions.

Local universities (numerous in France and around the world), committed to student success and regional development, have little interest in sacrificing their fundamental missions to climb a few places in a world ranking or obtain international accreditation.

Third risk: deviations and misuse. When an indicator becomes a strategic objective, behaviors adapt to optimize it – not always ethically. Accreditation bodies offer consulting services to the institutions they evaluate, creating a clear conflict of interest.

Going beyond the appearance of performance?

Behind the façade of accreditations and rankings, the reality is often less rosy. National and international school and university assessments – such as the OECD’s PISA program or the surveys of the High Council for Evaluation of Research and Higher Education ( HCERES ) – suggest that the actual performance of pupils or students is stagnating , or even declining, in institutions that are nevertheless covered in distinctions .

Accreditations produce an “appearance of performance”  : the institution seems successful because it complies, not because its students are making greater progress or its research is impacting society. This corresponds to a “decoupling” between compliance and performance, often facilitated by the “ceremonial” nature of accreditations and rankings.

However, the “institutional effect” on salaries upon graduation from university, or even from elite schools, is significantly reduced: it is primarily the individual profile of the student and the context of the job market that determine their integration. The reputation of the institution, built on rankings, accreditations, and labels, loses much of its significance at this level of analysis.

Criticizing rankings or accreditations is not, however, the same as arguing against university evaluation. Quite the contrary: public organizations have a duty to be accountable to their stakeholders for their activities. Mechanisms such as the HCERES , the ANR , and institutional accreditations already contribute usefully to these evaluations. But a relevant evaluation should be specific to the objectives of each institution, and not uniformly applied to organizations of very different natures, sizes, and missions.

The “paradox of conformity” described here is not limited to higher education. In many public organizations—hospitals, high schools, colleges, government agencies, local authorities—the proliferation of standards and accreditation or ranking procedures has become an end in itself, sometimes at the expense of the actual quality of service provided. The lesson is simple but essential: conformity does not necessarily equate to quality. Before choosing an institution—or placing your trust in it—it is worthwhile to look beyond the tree of accreditations and rankings to see the forest of reality.

Author Bio: Laurent Mériade is Professor of Management Sciences – Associate Professor – IAE – CleRMa at Clermont Auvergne University (UCA)

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