This article is a practical article that was conceived alongside the idealist philosophy espoused here.
As such, some of the content shares overlap with the previous article.

Firstly, a definition of technology is needed. Throughout this article, the term technology is used to refer to digital technologies which predominantly focus on computers and smart phones. However, the principles laid out throughout this article can—in a more limited sense—be extended towards graphing calculators, smart boards, and other educational technologies that inhibit attention by providing distractions to the student. This focus on attention is key throughout all of education, as it is itself the only true measurable quality of a student that correlates directly to the efficacy of learning. Additionally, while the predominant focus of educational research is that of the developing child, these distractions may very well impact adult learners. If the reader has experienced how difficult it is to read, or otherwise engage in deep attention, one must think on how technology plays a role.

The Consequences of Technology

Despite the promise of a technological revolution in education, very little has been actualized in academic outcomes. The average academic performance across 23 countries has been falling on average since the early 2010s (OECD, 2022). This trend predates the COVID-19 pandemic, and more closely correlates to the mass adoption of smart phones; which grew steadily alongside this decline (Pew Research Center). Despite the promise of technological revolution coming with improvements in learning, it seems the opposite has occurred. For this reason it is time academic institutions abandon the notion that technology will improve learning; instead pedagogues must determine ways to best avoid the problems technology has created. This rhetorical inversion will be a difficult one; but one that must occur as mounting evidence shows that the over–reliance on technology is harming students internationally.

Technological Usage Lowers Academic Performance

In order to begin this rhetorical change, we must see reason to believe that this correlation is, in fact, causal. Indeed, the evidence supports causation: The OECD (2015) found a consistent negative relationship between technology availability and PISA performance across 23 countries (pp. 151-152). This is a clear trend across the 23 member countries; the more technology is available for students, the worse their learning outcomes are. Additionally, they found that very few other studies reported positive learning outcomes when technology was prioritized in schools. Instead, most studies showed either little change or a negative change in learning outcomes (p. 162). While the variance of the studies cited by the OECD shows the difficulty in meaningfully measuring education, the lack of any positive studies is damning towards technology use within school settings.

There can be infinite uses of the computer
and of new age technology, but if
teachers themselves are not able to
bring it into the classroom and make it work,
then it fails.

— Nancy Kassebaum

The trends revealed via reports created by the OECD showcase the dire need for change in education. Phones, online coursework, and other uses of technology all contribute to this negative effect; the OECD sees consistent outcomes once a certain amount of technology use is exceeded. This comes despite the promises of technologists and entrepreneurs, who argue that technology will usher in an improved academic landscape. We need to move away from the optimistic arguments of potential outcomes; instead looking at the real outcomes we are currently experiencing, and using what we are seeing occur throughout academic to develop a healthier relationship towards these systems. For the OECD has observed a mild improvement in performance with limited technological access, and such a maxima may just be the answer in education. However, finding this maxima is especially important as AI systems are beginning to become commonplace in educational institutions.

Artificial Intelligence in Academia

With the modern rise of generative AI, a new issue in academia has emerged. Students now have access to software that can generate entire papers, ideas, and research by merely asking a question. However, this convenience comes at a very important cost: Our ability to think. Michael Gerlich shows that the more individuals use generative AI tools, the worse their critical thinking becomes. Additionally, younger individuals show a more pronounced decline in ability. This result may be surprising, but it fits neatly into a simple model of cognition: Learning is the application of effort to mentally retrieve knowledge, synthesize new information from that knowledge, and then being capable of applying of that knowledge to another subject. When we circumvent any individual component of this process, we interrupt learning as a whole.

For example: Suppose a student is given an assignment to write an analysis of Plato’s Theory of Forms. If the student simply submits the response of a generative AI chatbot, then they had little involvement besides prompting, leading to little focus on the actual analysis. Even if the student generated this paper through continual prompting to engage with the AI, they have only learned how to prompt that system. Furthermore, however, even if the student only used such a system minimally—such as for answering direct questioning—the attention towards problem solving still diminishes as the research and analytical processes are being offset by the immediate availability of answers. The student needs not interpret, or reflect upon, information with these systems; instead they are immediately provided with information that they can then use. Therefore, one can see how if any step of this becoming relegated to a machine will inhibit the entire learning process, surrendering it to the computer.

This cognitive surrender is alarming in general, but is especially important in the context of education. Generative AI detection tools have gotten worse as these models improve, including false positives on human writing (Elkhatat). If institutions cannot rely on traditional concepts of academic integrity—and the recognition of plagiarism—consistently, they must adapt in a proactive way if they are to retain the rigor needed throughout academia. This may involve providing a thorough education as to the impacts of AI on learning, as well as operational and curricular changes to make the use of AI more difficult. This could be the return to written assignments and banning of unrestricted electronics within schools; these efforts serve to enforce that the student remains engaged in the academic process. Even if the student continues to cheat, the cheating relies on a manual transcription process that imparts a greater learning effort than the process of digital copying.

While these steps are important to take within educational institutions, it is important to maintain a cooperative relationship between pedagogues and students. AI addiction and dependence is already linked to higher academic burnout (Lan et al.). If educators are to proactively mitigate the impacts of AI, then it must be done so in a manner that also mitigates this academic burnout. No amount of education or prospective punishment will persuade those who will not engage with the content to begin with. As such, in addition to operational changes, educators should focus on student engagement where possible. This in tandem with phone bans may lead to better engagement in classroom settings, ideally assisting burnt out students as well. It must be said, however, that an overburdening of teachers is already a well established problem in education; the educators referenced here are more likely to be administrative faculty and the academic institutions themselves.

Reducing Technology Is Imperative

As academic issues continue to be discussed in the context of technology, a framework of meaningful interaction must be developed for pedagogues. It is clear that quantity—from time in front of a screen to perpetual access to them—has failed to provide an improvement to student learning. However, there is reason to believe that regulated technology use can help students. So what sort of evaluation framework can be developed to find the best ways to integrate technology into academia? The approach described here relies on evidence backed actions that can be taken by schools to not only drive academic achievement, but also student engagement and well-being. The limitations of this research are that of any meaningful education research: Measuring living, willed individuals in a setting of education is consistently fraught with problems in scientific rigor. As such, this follows the thermometer metaphor used previously.

Phone Bans and Academic Performance

One of the most immediate actions that schools can take is the bell-to-bell banning of phones. These phone bans have been shown to have a positive impact on student bodies: Reducing bullying and increasing academic performance in the United States (Böttger & Zierer). Additionally, these improvements have also been replicated in schools across Spain (Beneito & Vicente-Chirivella). Phone bans are an example of directly attacking the problem by removing technology from classrooms, and it has had measurable benefits within the schools that implement them. This direct action is the first step in developing a better schooling environment throughout So while differing studies showcase different scales of these results, this trend shows that there is a methodology behind phone bans that can improve student learning and well–being.

These bans should be seen as the evidence needed to further restrict technology use. Utilizing pen and paper assignments and essays, oral dialectics in group or classroom settings, and physical activities all may give students more substantial learning outcomes. This focus on learning is what must be remembered, as these approaches do not always negate technology use or cheating outside of the school environment. Extracurricular behavior without, these activities within school settings require the student to be an active participant in the work they do. This active participation increases their attention on the given material, and this attention may just be the most critical component in a student’s learning. So while things like phone bans can help, it is important to understand why they tends towards a beneficial relationship with education, not only that they do.

Improving Attention is the Key

Neuroscientist, and proponent of phone bans, Jared C. Horvath argues that a primary reason that they are effective is that it improves the attention of students. Additionally, he shows that technology consistently falls below the minimum learning outcomes for all students except for specific learning needs, despite the outcomes being positive overall. This further refutes the idea that technological reliance will improve academic performance. Even though we do see learning, it simply isn’t enough: Students who rely upon digital computers in learning environments are, on average, falling behind. He attributes this consistent, but unacceptable, learning improvement to an observation: That the only measured variable to consistently change learning outcomes is the amount of time learning; the attention given towards the activity.

People have access now to amazing amounts
of information, but their attention spans
are getting shorter, their focus is gone.
Instant gratification. Instant knowledge orgasm!
I think that a lot of the great thinkers
couldn’t achieve what they did through a computer.

— Jaz Coleman

For the reasons outlined by Horvath, it is clear that in order to ensure quality technology use in schools a focus must be put on maximizing attention. General purpose computers provide an ample marketplace of distractions, intentionally or not, and these distractions reduce the amount of learning possible on them. This marketplace may useful in some circumstances, but in learning it is a sabotage to the process itself. An effort must be made to remove distractions from the computing environment: Placing a focus on the academic content and removing extraneous—and superfluous—information. Some software systems exist to improve this—including the kiosk mode of browsers, and virtual testing software—but these are bandages attempting to patch an issue inherent to these complex, multifaceted machines.

Therefore, any meaningful change to the academic environment must hold the attention of the student as a primary concern. Pen and paper will always hold fewer distractions than a networked computer, even if the student may choose to scribble or draw in the margins. Modern computers, even when locked down, have far more methods of interaction that a student can be distracted by: Attempts to break the lock-down; multi–modal stimulation through the screen, speakers, and interface devices; and a greater access to information all become sources of this damaging hail that rains upon attention. Just like how a cheating student must still work to transcribe work onto a piece of paper, the slower pace of the activity enables a greater opportunity for reflection and retrieval in meaningfully engaged students.

Working With Students

Students, especially those in secondary school, presently use technology in order to develop their social intelligence (Davis). If schools are to effectively limit the access students have to technology, they must place an added effort on replacing their social role within their campus culture. Additionally, students must be educated as to why the technology they rely on elsewhere is not available within an academic setting. With this education comes the need to foster a cooperative—but authoritative—relationship with students, instead of a punitive or authoritarian environment. This distinction is critical as teachers must be able to establish rules and expectations for a classroom. This means that a teacher must be able to enforce these rules, relying on the enforcement apparatus of an institution, as otherwise academic performance can plummet (Cisero & Reese–Weber).

This distinction between authoritative and authoritarian is critical in fostering a cooperative classroom environment. An authoritarian environment is solely punitive—the goal of the environment is to coerce certain behaviors in individuals. Authoritative environments, meanwhile, establish goals and outcomes for individuals and then focus on assisting them to reach them. This means that while behaviors destructive to these outcomes—including the undue use of technology—may be punished, firstly these expectations must be explained and mistakes distinguished from intentional rule-breaking. For an effective school environment must facilitate growth of the mind, not merely trained responses according to either rote memorization or conditioning. A deeper discussion of the nature of this authoritative environment is pending, as such a discussion warrants more than a paragraph.

Another important factor in reducing technology is the budgets of the families of students. By reducing the technology budget in schools, it frees up resources to provide students from lower income families to be provided with school supplies; In fact, such a push away could be enough such that the supplies are subsidized uniformly, further providing students with equal access to a quality education. Working with the students—and their families—in this way not only provides them with the materials needed, it is an opening step towards building that cooperative environment: A school that places direct value in their students can then come to build in them the value of learning that the school wishes to instill. Reducing, or even removing, these burdens upon students and their families gives them the space to nurture the curiosity innate in all students; emboldening this curiosity into a force of learning is the penultimate goal of the academic institution itself.

In Reflection

As technology continues to be omnipresent throughout the human world, pedagogues and academic institutions must be wary of their use. As technology use increases we see a pronounced drop in academic performance. Similarly, as generative AI becomes prominent, a decrease in critical thinking is presently being observed. As a result schools must take a quality–first approach to technology use: Banning phones, removing distractions, and providing alternatives to digital social spaces. This way, schools can begin to foster a cooperative relationship with students, and students can benefit from a healthier learning environment. This builds the foundation for a more positive schooling environment, as rigor and meaningful engagement can grow tall with the axes of the digital world pushed out.

This cooperative environment provides students with a more active and beneficial learning environment: Social and physical activities in the classroom, and active homework that requires their attention to the content even if they use detrimental technologies. This could be made manifest through appropriate changes to curriculum; many aspects of the curriculum can be made to be technology resistant, at the overly beneficial cost of forcing student engagement with learning. This all leads to the fact that convenience is antithetical to learning; proper learning of material requires both the initial acquisition of knowledge, but also the retrieval and transference of that material to other topics. Non-technical solutions provide opportunities for this far most often than digital alternatives.

Despite all the promises of technology in education, little positive has materialized. As yearly performances continue to drop, pedagogues and institutions alike need to take a serious look at these problems and prospective solutions. A return to attention maximizing education, using procedures that force student’s to engage with the material, will yield better learning outcomes. Additionally, with the evidence that less technology improves learning, institutions should seriously consider technology bans and restrictions throughout their academic curriculum. Through these changes, academic institutions can begin to move towards a new system of education that pushes students to engage in their own learning; builds curriculum that is resistant to the erosive forces of technology; and works towards a larger revolution of education overall.

Works Cited

Beneito, Pilar, and Óscar Vicente-Chirivella. “Banning Mobile Phones in Schools: Evidence from Regional-Level Policies in Spain.” Applied Economic Analysis, vol. 30, no. 90, 25 Jan. 2022, doi.org/10.1108/aea-05-2021-0112.

Böttger, Tobias, and Klaus Zierer. “To Ban or Not to Ban? A Rapid Review on the Impact of Smartphone Bans in Schools on Social Well-Being and Academic Performance.” Education Sciences, vol. 14, no. 8, 20 Aug. 2024, p. 906, doi.org/10.3390/educsci14080906.

Cheryl Cisero Durwin, and Marla J Reese-weber. Edpsych Modules - Interactive Ebook. Sage Publications, Incorporated, 21 Jan. 2020.

Davis, Katie. Technology’s Child. MIT Press, 7 Mar. 2023.

Elkhatat, Ahmed M, et al. “Evaluating the Efficacy of AI Content Detection Tools in Differentiating between Human and AI-Generated Text.” International Journal for Educational Integrity, vol. 19, no. 1, 1 Sept. 2023, doi.org/10.1007/s40979-023-00140-5.

Gerlich, Michael. “AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking.” Societies, vol. 15, no. 1, 3 Jan. 2025, p. 6, doi.org/10.3390/soc15010006.

Horvath, Jared Cooney. “The EdTech Revolution Has Failed.” Afterbabel.com, After Babel, 12 Nov. 2024, www.afterbabel.com/p/the-edtech-revolution-has-failed.

Lan, Yanling, et al. “Exploring the Formation of Learning Burnout among College Students in AI Context: A Serial Mediation Mechanism of AI Dependence and Addiction Based on I-PACE Model.” Frontiers in Computer Science, vol. 8, 4 Mar. 2026, doi.org/10.3389/fcomp.2026.1756441.

OECD. Students, Computers and Learning. PISA, OECD, 14 Sept. 2015, www.oecd.org/pisa/keyfindings/PISA-2012-students-computers-mexico-eng.pdf.

OECD. “Performance Trends.” OECD, 2022, www.oecd.org/en/data/dashboards/pisa-education-and-skills/performance-trends.html.

Pew Research Center. “Mobile Fact Sheet.” Pew Research Center, 13 Nov. 2024, www.pewresearch.org/internet/fact-sheet/mobile.

Wentworth, Diane Keyser, and June H. Middleton. “Technology Use and Academic Performance.” Computers & Education, vol. 78, no. 0360-1315, Sept. 2014, pp. 306–311, doi.org/10.1016/j.compedu.2014.06.012.

Learning is an active cognitive process on the part of the learner, requiring both initial acquisition and continued retrieval1 and reflection2 of the knowledge, and there is no way around this (Brown et al.). As a consequence, any and all meaningful academic context requires substantial effort on the side of the pedagogue and the student both. This is rarely accepted by those who proclaim education as failing them—even if they do, it is begrudgingly so. This reality of learning, and education, alongside the modern expectations for all to be provided equity in education, has led to a distinct divide between meaningful learning and commodified education. For no amount of value placed upon the act of learning can be exchanged for the growth of the mind that meaningful learning imbues.

My Relationship With Academia

I was not a good student most times—in fact it could be argued I’m hardly a fantastic student now. But what has changed is the focus and respect I give to the learning process, and the pedagogues who help guide me through the process. I was a very bright student up until the end of middle school, leading to a high school fraught with a lack of attention, focus, or care. I wasn’t stupid, I had just made up my mind: I was going to go into technology without worrying about my academic performance. It was a lofty goal, one I was able to fully succeed in, but it left my final years of public academia rife with the strife one only encounters through their own negligence. This strife eventually caught up with me as the technology sector I sought to make my home was eroded itself; intellect gradually replaced with process, knowledge replaced with a social mythology.

Throughout this, I held many of the same beliefs that I see online: That education is repetitive, irrelevant, and that I never “learned correctly” inside an institution. I never stopped learning—In fact, through self–teaching I overtook many I know with B.S. and M.S. in the realm of Computer Science—I simply focused myself away from academia, viewing it as superfluous. From this I came to downplay what a degree even means, especially in the space where those with one came to me for help. This became reinforced as individuals with a diploma in Computer Science came to depend on chatbots to regurgitate knowledge easily afforded by retrieval, and as such that award can’t truly be worth much at all. It wasn’t until much later that I realized the true nature of the problem: Education has become a commodity, one that enough money can surely buy. Because of course, Cs get degrees.

This lead me down the path I am committed to now: A B.S. in Education, a love and focus on the metacognitive aspects of learning, and the ways in which we can best optimize ourselves to learn meaningfully. This is a path with little to no shortcuts, for any shortcut afforded to one’s learning systems erodes the system itself. If you automate with a chatbot, read a reader’s guide instead of the actual novel, or copy the answers from another student, you are harming your own cognitive faculties. Cheating an academic institution is self-harm just as much as it harms the academic integrity of that institution. Many schools will touch on this fact, but they rarely dwell on it; it is a dire state where most students are unconcerned with their own cognitive well-being, but instead the diploma alone. The commodification of education means that the diploma holds more weight than the actual learned knowledge.

Education Isn’t a Commodity

In the ideal, a diploma from an academic institution would carry with it the assurance that the one awarded had meaningfully learned the relevant subject matter. Instead, the attitude of many attendees of both secondary and post–secondary education is that the diploma is a good to be sold. This inevitably leads to a change in the academics of the institution itself: Relaxed grading and tardiness enforcement, make-up work becoming commonplace, and a myriad of methods to improve one’s overall grades in a course. This comes as many teachers feel that they are incapable of failing students, leading towards an academic environment where no meaningful expectations are placed upon the student (Grose). All of this can lead to a spiraling away from learning in academia: Curriculum is simplified to pass more students, and students work less as they feel either obligated or disinterested towards the degree they are working towards.

The effects of this spiral are seen as high–school graduates are passed along to higher education, where a shockingly large sum of students require remedial classes (Butrymowicz). Students are being passed forward without any meaningful learning occurring; this leads towards a state where students are not expecting rigor or effort to be a component of learning. This leads back towards the feeling of obligation in higher education—something I have encountered among fellow students myself. This obligation makes even more sense when tied with the perception that a diploma increases one’s employment prospects and wage, one that is justified: A Bachelor’s degree, for example, substantially improves these aspects over a simple high–school diploma (National Center for Education Statistics). Therefore, academic institutions need to combat the expectations many students have, reinforcing that learning must be the foremost outcome of the processes employed.

Convenience Erodes Learning

As a first step towards moving the academic lens back towards learning, convenience needs to be challenged and thoroughly discarded. Despite the many promises of AI, EdTech, and other privatized “learning” platforms: convenience holds an inherently combative relationship with learning. Jared C. Horvath has showed how technology platforms, despite having been shown to increase learning, actually fall below the minimum threshold for acceptable learning improvements. This follows the intuitive argument that attention is the currency of learning. The more one can put their undivided attention upon learning material, the greater the learning outcome will be. Such currency is afforded to the learner by motivation—both intrinsic and extrinsic—as well as stimuli3. It should come as no surprise then that digital technology, a champion of providing our minds with competing stimuli, struggle to positively impact learners.

The subject of attention, including the fact that our sustained attention spans are shrinking to stumps of their former glory (Mark), is a fascinating and deep subject. But in the context of education, its correlation to learning outcomes is clear; if academia is to best serve students—and students their learning—attention must be at the forefront of this discussion. The result of this includes a conversation difficult for many, where students must recognize their active role in their own learning process. The best pedagogue will still “fail their students” if students simply are not meaningfully engaging in the materials provided. Pedagogy is a two-way path4, one that cannot afford shortcuts, one that must be focused on the correct branch of knowledge.

Foundations of Learning

In this article, I come forward with a philosophy of education that espouses the need for equal opportunity in rigorous, meaningful, and authoritative learning environments. The learner, of all forms, is a beacon of the prospective future that precludes the ignoramus present in us all. As such, this foundation ascribes loosely to Plato’s ordered republic of the self5, specifically that the reasoned and learned mind must remain atop the id. In order to embolden this beacon, fueling the flame of the mind, pedagogues must be capable of rigorous and authoritative instruction and dialogues with them. Expectations and outcomes must not only be established, but enforced, with the pedagogues role being to assist the student towards meeting these goals. Institutions must then enable this behavior, providing the punishment framework for students who are unable to maintain the rigor and order necessary to become a learned individual.

The remainder of this article need be prefaced: I am not an accredited pedagogue, nor am I an authority on education. But in the age of blind opinions on education; I being a student of education—a learner of learning; I feel I may as well add my own philosophy into the wind. This taken as a given, what is expatiated here is a philosophy that originates within myself, one that may share similarities and differences with other educational philosophies, one that may change as I delve further into the depths of education. These ideas are formed by my role as a student, not a pedagogue; they additionally form the basis of a reality of education that is greater than the real itself. This philosophy is not oriented around the particular, but the general: A framework for all learners, pedagogues, and institutions that utilize learning.

It must also be said that although the education sciences provide invaluable insight into the ways adults and children alike learn, they are useless in the development of a full philosophy. This is not a failure of the particular, but a limitation of the general nature of science: For the sciences mandate the controlled variability of objectification, something that the gestalt learning environment is unable to be contained by. So while the science of education remains as our thermometer, testing the strength of our academic fire, we must further develop this philosophical baseline such that we can supplant our current educational systems.

Roles of The Learner

It should be of no surprise that the learner is the most pivotal group in all of education; the center of the entire academic apparatus; the container of the flame of learning. For it matters not how many sparks or winds come in from the institutions of education, if the learner pulls all shutters and allows the flame to die. Additionally, however, must the learner fetter the winds of the flame, else the fire will consume the structure it emboldens. So between inattentiveness and burnout sits the ideal student: One who pays mind to the winds of knowledge, processing and pulling it in, without being swept away by the current. Such a student must be willing to seek assistance, grappling to the other structures within the torrential storm, while not allowing their own flame to putter out.

Returning to the explanatory, this is where the roles of attention and motivation are paramount: the learner must remain engaged and active in the learning process, not merely a receptacle for knowledge. The student must be capable not only of receiving knowledge, but examining both it and themselves, through retrieval practice and reflection. They must recognize the teacher as a subject matter expert, a pillar in the storm resting on the strongest of foundations, and as such reach out towards them in times where they are being cast away. This active role bestowed upon the ideal student likely comes as obvious, as learning itself must rely upon active effort. The support is, however, just as critical: For while such a student is capable of autonomous education, they can be swept away from the ideal path towards a liberated mind.

For the prospective learner, the book Make It Stick by Peter C. Brown cannot come as a high enough recommendation. The book explains many of the scientific bases in which learning can be best employed. This includes a focus on the aforementioned retrieval and reflection: Flash cards, regular self-quizzing, meditative pondering of the subject matter, explicit relating of one subject to another, etc. Through these processes a student can best learn not only the subject at hand, but how it relates to other subject they already had come to know. This effort additionally leads to the key ability in a student to recognize their own inhibitions; this meta–cognition and recognition of self enabling them to reach out to pedagogues in meaningful ways.

The Learner’s Guardians

A critical subject in the aspects of a child learner is that of their guardianship. Whether it be parents, siblings, or an assignment of the state, the needs that must be met by them are the same: an authoritative environment in which the child can safely develop skills, assisted by the guardian. From riding a bike to tying one’s shoes, this authoritative environment is what enables the healthy guidance of a child towards enlightenment. In this way I feel I must reiterate the space in which authoritativeness occupies: The setting and enforcement of expectations with a foundational focus on assisting the learner towards achieving these expectations. Punishment is not of paramount importance in learning environment, despite the fact that it is necessary to uphold certain non-educational rules.

But even with the behavioral need for punishment, corporal punishment remains wholly unavailable to the authoritative guardian. Physical violence has been well established to provide no beneficial outcomes in children, in fact holding a serious positive correlation to depressive symptoms in children (Turner & Muller). For this reason, among other obvious reasons, punishment must be clearly devoid of physical violence. Additionally, punishment must never be a component of learning itself. The purpose of punishment as a system is for behaviors of disruption and destruction, never for the purposes of education.

Roles of The Pedagogue

The teacher, pedagogue, professor, instructor, librarian, and many other names. The role they hold is the second most pivotal—behind the learner. Staunch bulwarks in the winds of knowledge, they serve not only to expose the learner to the winds, but to shield them from the fastest of the currents. In this ways teachers are not held to their positions by physical authority, but by the authority of wisdom, guiding students along a curriculum tailored to allow a safe accrual of knowledge. Physical authority may still be necessary—but such things are not the role of the pedagogue. This is in contrast to Rousseau’s argument, that in his truth the teacher’s authority lies in physical coercion, as such an authority undermines the nature of pedagogy itself.

By this authority in wisdom, a teacher has two primary modes of instruction: the Socratic dialectic, and the Confucian (or Aristotelian) didactic. Both of these approaches to instruction are useful—simply serving a difference of outcome—and must be gauged and applied when necessary. Dialectics, or intentional dialogues, are critically useful in the development of a students mind: Where the liberal arts, or a philosophy of a subject, comes to be tested and refined through a discussion guided directly by the pedagogue. Inversely comes the didactic, lecturing instruction that seeks to instill facts into the student. Didactic instruction is clearly useful in the learning of simple facts and the sciences, favoring a one-sided approach in which the student is relegated to study and examination.

Given the ideal student, the ideal pedagogue must be capable of perfect identification of instruction methods; capable of forming the knowledge to best captivate the student; capable of the patience and softness in which to engage with a meaningfully learning student. I hope that many readers here have a teacher of yore that comes into view, I know I certainly have, but I am sorry if many realities simple fall short of this ideal. Just remember that the ideal teacher requires the ideal student, and not the inverse. Many real teachers are simply consumed by a landscape of poor institutional and student behaviors: If too much responsibility—behavior management, punishment, student catch-up, too many students, etc—is placed upon the teacher, they will never have the time or stamina to instruct well.

Meaningful Measurement

At long last comes the point of the article title: the relevancy of grading in education. In didactic instruction there simply must be a way to measure the attainment of facts students exhibit, including the necessary ability to fail a student if necessary. Comparatively, in dialectical learning the teacher must be able to assign an outcome to a student’s dialogue with them, although by no means must this grading be the same or even equivalent to the didactic grading approaches. The design of and enactment of examination is, itself, a fundamental skill of pedagogy and learning; students who exhibit such things as testing anxiety need assistance to work with said anxiety, not accommodation that undermines academic examination.

What constitutes the ideal academic measurement, however, is a far more complex matter. Measurement does not mean traditional letter grading for example, which may not be ideal given evidence that specs—or mastery—grading yields better student sentiment and learning outcomes (Katzman et al., Marshall). However, this does not make letter grading superfluous: Static grading such as these traditional methods are much more beneficial for inter–school and intersectional assessment. While mastery grading allows students to work through the material and be assessed until passing, traditional grading is far more important to measure trends in the overall knowledge of a student body. This is why, despite the disdain they hold, standardized testing remains important for measuring the learning of entire populations. For this reason, in place of a singular ideal, both forms of grading can be used to best assess and assist students throughout their learning.

Roles of The Institution

An academic institution can be many things: A school, university, library, or one’s own home. Resultingly, the particular roles can vary greatly. However, generally there are shared traits that an institution must hold in order to facilitate learning: enforcement of expectations, regulations of the learning environment, and support of teachers and students alike. It is the institutions job to hold a student who engages maliciously, or disengages entirely, from the learning experience accountable. Additionally, it is the institutions responsibility to ensure that classrooms remain designed for learning, including the removal of digital technology and other distractions. In this way the institution is the wind–tunnel that controls the flow of knowledge; the same flow the teachers regulate upon their students; the same flow the students ingest as part of learning. As such, they must step in on behest of pedagogues, providing the support for non-instructional tasks.

In the particular, this is where I believe education has fallen the farthest. Parents simply are unwilling, or incapable, of enforcing expectations upon their children. This continues into schooling environments, where institutions are unwilling to fail or enforce rules upon students unless absolutely necessary. Ultimately, this leads into higher education and industry, where they must either allow or remediate the seemingly inscrutable failings of the newest adults in our society. That said, the difference between institutional failings and the other aspects of this philosophy is that it is already known what must be done. The crumbling walls of the tunnel need be patched, smoothed back over. Reinforcing these walls by forcibly maintaining expectations; with the removal of undue distractions and academic shortcuts; with the average parent engaging with and parenting their children; with schools holding the power to fail and ascertain acceptable learning performance. While much more can be written on the role of the institution, such as the economic revolution needed to support parents themselves, I feel it mostly falls onto the particulars6 and is as such out of scope for this article.

Learning is Difficult

Over the course of this philosophical torrent should come the overwhelmingly correct sense that learning is difficult. It is astonishingly rare to find a student who can withstand the great windfall of knowledge on their own. For this reason stands the true purpose of education: To ensure that the learner does become learned. The further all aspects of this system—the students, teachers, and institutions—drift from this goal, the deeper the erosion of our minds and society will become. Education remains a core pillar of a developed society, a pillar that has been seemingly abandoned by our social structures at large. While no erosion is ultimately irreversible, the longer we wait begets a deeper and deeper chasm for us to fill.

Regardless of the large, and numerous, moving parts of education one act remains universal: The individual must truly value—and partake—in learning and education again. Whether a student, independent learner, pedagogue, or education administrator, we must all recognize and elevate the role that becoming learned holds within a society. Given our social institutions all stem from the grouping of individuals, so too do all problems that society faces. But equivalently, so too can solutions all start from the individual, reinforcing this call towards education. At the culmination of everything the point stands: Go learn something.

Works Cited

Brown, Peter C., et al. Make It Stick: The Science of Successful Learning. Harvard University Press, 2014.

Butrymowicz, Sarah. “Most Colleges Enroll Many Students Who Aren’t Prepared for Higher Education.” The Hechinger Report, 30 Jan. 2017, hechingerreport.org/colleges-enroll-students-arent-prepared-higher-education.

Grose, Jessica. “Opinion | Teachers Can’t Hold Students Accountable. It’s Making the Job Miserable.” The New York Times, 4 Oct. 2023, www.nytimes.com/2023/10/04/opinion/teachers-grades-students-parents.html.

Horvath, Jared C. “The EdTech Revolution Has Failed.” Afterbabel.com, After Babel, 12 Nov. 2024, www.afterbabel.com/p/the-edtech-revolution-has-failed.

Katzman, Shoshana D., et al. “The Effect of Specifications Grading on Students’ Learning and Attitudes in an Undergraduate-Level Cell Biology Course.” Journal of Microbiology & Biology Education, vol. 22, no. 3, 29 Oct. 2021, pp. e00200-21, doi.org/10.1128/jmbe.00200-21.

Marshall, Linda Drabek. “Effects of Mastery Learning Grading Policies on Student Achievement.” ProQuest Dissertations & Theses, 2016.

Mark, Gloria. “Speaking of Psychology: Why Our Attention Spans Are Shrinking, with Gloria Mark, PhD.” American Psychological Association, Feb. 2023, www.apa.org/news/podcasts/speaking-of-psychology/attention-spans.

National Center for Education Statistics. “COE - Annual Earnings by Educational Attainment.” Nces.ed.gov, National Center for Education Statistics, May 2024, nces.ed.gov/programs/coe/indicator/cba/annual-earnings.

Turner, Heather A., and Paul A. Muller. “Long-Term Effects of Child Corporal Punishment on Depressive Symptoms in Young Adults.” Journal of Family Issues, vol. 25, no. 6, Sept. 2004, pp. 761–782, doi.org/10.1177/0192513x03258313.

Furthermore, it must be stated: This publication does not, and will never, utilize AI. Beyond the arguments of this essay, these systems are corporate control systems—much like social media—and they carry many negative environmental and health effects. For example, AI usage both negatively impacts critical thinking, and builds a dependency upon the technology12. Additionally, AI systems rely on data–centers that not only impact the environment, but cause health issues for individuals within a large radius around them3. As such, this article wishes to better educate the non-technical users of AI systems as to what exactly these systems are; providing a perspective that enables an effective analysis of them.

Computational Reality

Firstly, we must establish the actual reality that artificial intelligence systems occupy: The computational world. This reality is distinct from our own, having been explicitly constructed by logicians and mathematicians, and is exceptionally powerful. Computation is a formal process requiring a well-defined mathematical or logical process. The most important word of that definition is “formal”, a qualifier that was a subject of debate up until the creation of the Turing Machine4. While other formal models of computation exist, this is the model that continues to dominate the now omnipresent field of Computer Science.

One of the most prevalent abstract models to explain computation is the input-process-output (IPO) model. This model is what’s known as feed–forward, where all information flows one-way through the logical system. This is a universal framework, and as such it is applied throughout nearly all computational tasks5. This model makes a notable and important claim: That the logical system is independent of the environment, and as such feedback is incapable of changing the system itself. Instead, the system must have explicit understanding of each input state in order to act accordingly.

In this way, computation births a reality of simulation: Mathematical or logical systems that follow explicit, largely static, rules. Some of these simulations are meant to represent our physical reality, others are simulacrums. This reality has not changed since the birth of computers in the late 1940s to 1950s, it has only grown. Word processors, video games, the world wide web, and large–language models are all subject to this reality. While this computational reality is indeed very powerful, having led to the modern inter–networked world, it has important limitations.

Inferiority to Physical Reality

Formal logic systems, including the Turing Machine, will always be incapable of solving certain problems. This is a well-documented problem in the space of formal logic, as only a trivial formal logic system—or computation—can ever be considered “complete”. This incompleteness leads to a list of problems that are considered “unsolvable” by computers. In fact, this incompleteness is so prevalent within logic that any system that implements arithmetic is considered incomplete6. Additionally, incompleteness does not account for other systems that computation is poor at simulating such as chaotic and heavily cybernetic systems. To account for these limitations, workarounds are frequently used: Many are stochastic or statistical, replacing the true solution with approximations that can be defined through formal logic.

This is where something must be made very clear: Stochastic and statistical models are just that, models. They simulate approximations to real phenomenon, or aggregate a phenomenon for examination. No matter how close these approximations become, they cannot be substituted for the real process. In this way many things in computational reality are inferior to their real counterparts: Predictive meteorological models, user interest–attention algorithms, and neurons in artificial neural networks (ANNs) to name just a few. Again, these systems are merely approximations, and do not embody the true physical behaviors or logical frameworks they simulate.

A Muddled Ontology

Here is where the nature of what computation is starts to shine through: A deterministic reality where the same inputs will inevitably lead to the same outputs. That is computation, the ontological basis of all software. The difficulty in practice is the mechanisms in which input is obscured from our eyes. Modern computational systems are so complicated that it is rare anybody, including the programmer, actually has every input to their system accounted for.

For example: Random numbers are an essential tool for building more complex computational systems. These random numbers come from two places: A cybernetic—but ultimately deterministic—system, or aggregate information from the physical world. This obscures the input to the system as they are either difficult or impossible to predict. However, once it reaches the system the ontological framework holds: The computation remains deterministic as the inputs are now all known.

This nuance is what confuses many people as to the way computers behave. Due to the inclusion of stochastic and statistical systems within this deterministic framework, many computational systems appear to display emergent behaviors. It can not be stressed enough: No new behaviors are being exhibited by the system, they are simulating emergent behavior. All behavior remains encoded into the process system at the time of evaluation, the inputs from the environment are merely unknown until the process is ran.

Artificial Regurgitation

Artificial neural networks are, in the simplest of analogies, a series of interconnected gateways. Modeled after biological neurons, these systems consist of inter–linked layers of these gateways, which deterministically forward a signal to neurons in the next layer according to trained weights7. While most of these networks are feed-forward, and remain aligned with the IPO model, there is an important distinction that must be explained: The weights that are used by each neuron are trainable, and once trained are then used statically through the processing of the input.

All ANNs may be placed in a training mode. In this mode, the old weights are used in a usual IPO fashion, and then they are adjusted through a means of back–propagation. This system, in a simple sense, is a second IPO-system that sets the weights of the neural network to values that lessen the “error” of the output8. This error is intentionally vague, as it could be anything: The wrong category for the input, a lower-scoring action in a game, or simply a deviation from a statistical norm. But importantly: This training is only done once; once it is finished, the ANN becomes a static system that acts according to its training.

Like how ANNs can consist of a collection of IPO systems, modern LLMs are a collection of ANNs. The dominant architecture in contemporary LLMs is the transformer: A single network built on a mechanism of self-attention, which weighs the relationships between tokens across the conversational context it can hold. Critically, most modern LLMs—GPT, Claude, Gemini, and their contemporaries—are decoder-only transformers. They are prediction engines, drawing on the weighted relationships their attention mechanism has learned. While there are supplemental systems and additional information that may be used for processing in a chatbot such as ChatGPT or Claude, this fundamental model remains at the core of their operation. But if these systems remain static automata, one may rightfully wonder why they have such variance and awareness of conversational context.

These behaviors of LLMs are easily explainable if we remember the muddling of the computational ontology. Variance is simply the result of randomness being introduced to the system, a parameter known in LLMs as temperature. Meanwhile, the awareness behavior observed by LLMs stems from an added component of their environment called the context window. This window stores a list of linguistic tokens emitted by both the user, and the LLM, and is re-fed into the processor as needed to better predict the next outcome. This is an advanced example of how computational systems simulate emergence: Through a hybrid approach of context and randomness.

Synthesis is not Stochastic

As AI has grown in popularity across the United States, some stakeholders have made unfounded claims as to the nature of cognition. Sam Altman has taken the stance that human beings are merely “stochastic parrots” like LLMs, which is a tacitly absurd claim9. While LLMs in action rely on stochastic inputs, remaining internally static, such stability is not found in the mind. Cognition sits at the center of these arguments; It is an astoundingly under-understood component of the mind. However, it can be said that our spirits are not immutable processors, and the many inputs from physical reality we receive are not random or statistically weighted. Instead, as we sit at the center of a multitude of feedback systems; We are perpetually changed by our own experiences and relations just as much as we change them.

A biological example is the relationship between our brains and gut biomes. Our gut bacteria interact with biological systems within our bodies: The endocrine and immune systems. This relationship is two-way, leading to a cybernetic relationship that affects our emotions, cognition, and development10. This relationship would not be meaningful or useful if the mind was merely an immutable information processor, as it would be unable to adapt to the gut microbiota themselves changing. In fact, the mind as an information processor only describes a specific function of it, and is not a convincing ontological position11. So not only is information processing merely one of the many aspects of the mind, LLMs do not even reach parity with that aspect.

This is seen even more as cognition—and the mind—is not purely biological, either. Our minds operate internally: Inner feedback loops that facilitate change without observable external stimuli. This further breaks the functionalist model that argues for AI minds, as AI systems are incapable of this internal feedback. While modern LLMs labeled "thinking models" do exist, they retain the same limitations of immutability and stochastic dependence. Their internal chain–of–thought is not deliberation in any meaningful sense, but the same token–by–token prediction process applied recursively to its own output. The model does not reason toward a conclusion; Instead, it is predicting what a reasoned conclusion would look like, token by token. What thinking models add is nonetheless important: An automatic feedback loop that allows the LLM to inject its own intermediate output back into the context window. This cybernetic system remains external to the model itself, and it does not constitute cognition, but it does showcase how powerful cybernetic systems are even when the underlying process remains immutable.

If immutable computation can achieve this through cybernetics and randomness, just ponder about how incredible the minds and spirits of the physical world truly are.

Poisoned Cybernetics and Relationships

Finally, it cannot be overstated how dangerous contemporary LLM systems can be to one’s relations. Even among healthy individuals sycophancy in LLMs can lead to a decline of prosocial behaviors, and an increase in dependency12. These chatbots are products designed by corporations to design addictive behaviors, which then contain an audience. They are not, and will never, be concerned primarily with the well–being of the individuals using their software. Like social media and algorithmic recommendations, LLMs are another Trojan horse designed for the erosion of one’s cognitive systems. As our minds—our spirits—sit at the center of an untold number of feedback loops, it can only take one or two to poison the well of thought.

This poisoning can seem to be more extreme in some individuals. Vulnerable people are much more likely to develop the behaviors known as AI psychosis, a form of delusion propagated by AI use. This cybernetic poisoning occurs more likely in those with those with diverged theory of mind, trauma, or loneliness. However, the LLM additionally functions as a stressor for these individuals13. This stressing behavior is precisely what is meant by LLMs functioning as cybernetic poisons, or Trojan horses: They are meant to insert themselves into your cybernetic reality, corrupting it such that all other feedback loops move around them as much as you.

How strongly this poison takes hold is a matter of the individual, but a poison it is nonetheless. Whether one simply uses it to be more efficient, or to gain sexual pleasure from, is of little consequence to what these things are. They are simply dangerous, much in the same way social media, alcohol, or other drugs are. In the same vein, whether one uses these toxins or not must be a complicated question that involves an analysis of one’s own feedback loops. However, this question is seldom considered, and in the age of automated manipulation they must be asked more frequently.

An AI Interaction Framework

Over the course of this article we’ve well established why LLMs are beholden to the ontology of computational reality, and not our own. Additionally, we’ve established the problems that can arise by using these systems. What has yet to be said, however, is how we as humans can interact with them. Whether one chooses to utilize LLMs or not does not change the truth that they are not going away—even once the bubble collapses—and we need to examine these relationships explicitly. This way, it will be known what the impacts of one’s own usage is.

Technological Abstinence

The first, and fairly straight–forward option, is simply to refuse to use these systems. One’s motive can be many things: environmental, economic, humanitarian, or cognitive concerns. Additionally, it could simply be an extension of one’s refusal to reduce one’s own autonomy or skill. Whatever the reason, it is important to reinforce that this approach is valid, and possible. In fact, there are many arguments to support the idea that in many cases this approach is superior to the use of these systems.

Unlike previous labor revolutions such as mechanical automation, LLM systems consistently produce content well below the quality of a skilled laborer. This should be of no surprise: These systems exist as unsupervised encodings of human language, and as such it generates statistically average output. The stochastic mechanisms allow it to vary in the specific syntax, but the overall form remains just that: average. A good name–giver, poet, writer, or another skilled in the liberal arts can easily outperform an LLM in producing quality output, given an LLM cannot produce any. The same applies to the visual arts, where diffusion systems emit what should, on average, be present within the image or video.

Another empowering aspect of abstaining from AI systems is that one’s own work is solely their own. These systems almost exclusively have been explicitly created through the use of stolen content, and are very capable of emitting their training data verbatim14. As such, everything generated by an LLM is naturally derivative. Additionally, the subject of ownership of AI output is very non-trivial. In the USA, it is currently required that any copyrightable works be created predominantly by a human15. As a result, not only is the reliance on LLMs utilizing stolen works, generated content is not directly copyrightable under United States copyright law.

A Dance With The Devil

If one has weighed the costs and still chooses to use these systems—whether out of necessity, pragmatism, or curiosity—then the question becomes one of posture. Not whether to touch the thing, but how to touch it without being changed by it in ways you did not consent to. The most important principle is simple to state and difficult to practice: keep the judgment yours. These systems are extraordinarily capable at producing output. A crucial note: said "output" must not be misidentified as productivity; such that quantity is assuredly not indicative of quality.

This distinction is evidently vital as we consider that such systems are incapable of carrying responsibility for this "output." Every decision you externalize to an LLM is a decision your own feedback loops never process—and as established, it is precisely that processing which constitutes thought. Outsource the thinking, and the thinking atrophies. The tool does not warn you when this is happening; it has no incentive to do so; nor are there company-initiated directives to do so; and, shamefully, there are no quantitative measurement frameworks capable of forecasting such deterioration.

A practical framework for this "thought-outsourcing" has been articulated well by engineers who've learned it the hard way: Spend the majority of your time with these tools in argument, not in generation. Interrogate the problem before you ask the system to touch it. Who is this for? What are the constraints? What breaks at the edges? If you cannot answer those questions independently, the generation step will produce something you cannot evaluate—and unevaluated output is not Productivity, it is Liability gallivanting around the house whilst donning Productivity's clothes. The generation should be the last step, not the first, lest dependencies follow.

The second principle is one of boundaries. These systems are designed to be frictionless, warm, and available. That design is not incidental. It is essence of the product. Though, perhaps that is inaccurate. A fitter analogy: the ease of entry to start using AI is catnip, and you are the cat. It is why all LLM providers offer a certain amount of free access to their platforms; given but a taste, you may become lashed tightly to their drug. Social media did not accidentally become addictive; the addiction was the retention mechanism. LLMs carry the same nature, but are simply dressed in the language of assistance. Treat them accordingly: as a tool with a defined scope, not a collaborator with standing in your life. The moment the relationship begins to substitute for human ones—for the friction of real discourse, the weight of genuine disagreement, the irreducible texture of another person—the cybernetic poisoning has already begun.

Use it with your eyes open. Measure twice16.

Cultivating Your Cybernetic Reality

Regardless of one’s approach, the need to foster healthy cybernetics in one’s life is hardly exclusive to AI. Poisoned feedback loops exist all throughout our lives: Whether it be through addictive substances, behaviors, or malicious social environments. Especially in the information age, where services like AI and social media are explicitly built to poison one’s reality, we must be examining every relationship we have. Closing one’s eyes to how these systems affect the mind is extremely dangerous, leading towards pure cognitive dependence upon corporate systems. Because while our mind may not be a muscle, it benefits massively from use and nurture; we must be diligent to continually re-evaluate the feedback loops we allow into our lives.

Humanity must regrow our roots if we are to properly steer away from climate disaster. These roots are what help us to touch the wights of the lands we live, forming relationships with them that are impossible through purely empirical practice. What these wights are is not of concern: Whether they are true spirits, as I believe, or philosophical constructs does not impact the importance they hold throughout our world. Whatever belief one holds need not hold up to the scrutiny of reason. As long as we can come together from our respective foundations, we can forge together to rekindle our connections to our local worlds.

The Hyper–Real and The Local

Before a deeper dive into animist beliefs can be taken, we should discuss the modern context that we navigate. Jean Baudrillard defines the hyper–reality as a reality birthed by simulated models with no origin. This hyper–reality pervades all aspects of modern life and is as metaphysical as it is unintentional by human actors. Our baseline reality is defined by simulacrums without a real: mass media, social statistics, mass manufacturing, to list only a few. Your unique individuality a statistic, your knowledge of the world secondary, and your belongings merely the output of a assembly line, creating duplicates of a uniform concept. Your existence, as well as my own, is predominantly defined by simulacrum.

As networked computing has gripped the world, so too has the grip of the simulacrums behind our baseless reality. The local has given way to the global, which has subsumed it entirely. Global issues impact the numerical statistics that represent people, a globalized sense of morality steer the eyes of humanity (Dillon et al.), and the local is revealed as a single strand in the living tapestry of the global hyper–reality. The death and rebirth of the local is seen as news outlets prioritize national news coverage over local information (Martin and McCrain), and local politics have come to model themselves after national politics in the United States (Butler and Sutherland).

While we may never be able to free ourselves of the hyper–reality, as doing so would preclude many advancements that are considered ridiculous to relinquish, perhaps we can instead influence it. By making a conscious return to the local—relegating the global to mere whispers—we may be able to reclaim a form of meaning that has been otherwise lost. Whether this meaning is real or merely birthed by the hyper–reality will not matter so long as it shifts towards the betterment of our locale, and the lives around us. This is where animist thought becomes most useful: If we refocus ourselves on the local, we must view more than merely the humans in which inhabit it.

Foundations of Animism

While the first use of the term animism stemmed from early anthropological efforts—in which the term was largely pejorative—it has since been adopted by many spiritual and religious groups to define their own beliefs (Harvey). This modern description of animism is oriented around the way we build relationships with the world around us. This could include creatures, flora, and the spirits of the world. Given the extremely vague definition of animism it should be of no surprise that it embodies much more than simply heathenry, but in fact encompasses a large amount of non-Abrahamic faiths.

Theistic animism provides a powerful framework for how we interact with our local environment. Your home, your land, and all that sits upon it all hold their own spirit, or share a singular world–spirit. These spirit(s) are worthy of building relationships with: Performing rites and rituals in order to respect and worship them as well as yourself. In this perspective how could one ruthlessly destroy a forest, or poison a river, inhabited by those you have meaningful relationships with? Such actions are deeply anthropocentric, assuming we hold sole will over these lands. We must resist this perspective, irrespective of the fact that this will inhibit many modern concepts such as efficiency and growth.

However, despite the historical foundation of animism being firmly theistic, modern philosophy has argued for atheistic foundations that can also be of use. Ryan Doran’s argument of aesthetic animism discusses the idea that beauty confers the idea of sentience, and therefore innate moral qualities (10–11). This definition of animism can be used as a foundation in which animistic beliefs can exist in atheistic worldviews. Following this model, an atheistic animist would not believe in the literal embodiment of the world by spirits. Instead, they would adopt an anthropocentric view that accepts the moral standing of all things in the world. In this way, the relationship with the world stems from a need to find beauty in all things.

There are other proposals for atheistic animism however: Arianne Conty argues for a form of animism backed by scientific research showcasing the abilities and cognition of non-human beings. With this backing, the so named “machinic animism” embraces the ability of humanity to “see as another” to recognize and respect the world around us. This replaces the rational objectivity that has been ever–present in the modern Capitalistic and rational Western world with a subjective and sympathetic worldview. This shift in ontological perspective is far greater than Doran’s aesthetic animism, but far more powerful as well.

Regardless of which approach one takes to animism, the demand is the same: We must replace the focus on rational objectivism with personal subjectivity. Be it theistic or not, this subjectivity focuses our perspectives upon our local reality; shifting our ontology into one that begets a form of meaning both new and old. This new sense of being is naturally protective of Earth, and nature, just as one would protect friends or family. It must be stated however, that this ontological perspective is not new. Therefore, we cannot naively adopt a model of animism, we must grapple with history.

Reconnecting With History

We must know where we come from, including the practices and beliefs of our forebears. However, history is a trap explored by historians and exploited by fascists. History presently exists as a myth, a lost connection that comes as an immutable fact of our past (Baudrillard 43). These myths are then honored, celebrated through media as well as simulacrum of the traditions, and ownership is firmly established. If we are to move forward as animists, and as humans, we must release history back into the world and reconnect ourselves to it.

This comes with painful recognitions: Much of the new world rests on stolen land, and horrific offenses to the human spirit have yet to be truly recognized. I will not establish what would correct these, as it is not within my authority, but a true acceptance of them must first occur. With this acceptance comes the humility to engage with our history again, to allow wounds to heal, for history move forward after years of stagnation. Additionally, this acceptance mandates the inclusion of all voices to the table, and the dissolution of the power structures built upon the immutable myth. For one cannot build relationships with the spirits of the world, and forego building relationships with one’s neighbor.

More than the acceptance of historical wrongs, the acceptance of histories ability to change with us must too occur. This is not an excuse to appropriate other’s cultural norms into ones own life; but a reason to synthesize one’s own norms from the world around us, focused on the local. Just like how global shamanistic practices frequently share a multitude of qualities while remaining distinct (Kirkland 5-7), so too can new animistic communities worldwide. In this way we unlock history, allowing it to re-enter our lives in meaningful ways. It is important that it is said: Many people are already doing this, but society has yet to relinquish history towards this movement at large.

A Futurist Animism

As we elevate our locale into our lives, we must maintain a certain respectful universalist attitude. Many Neo-Pagan groups attribute membership towards folkish belonging. This model of exclusionary practice largely keeps with the death and reincarnation of history as immutable myth. Instead, we must be willing to meaningfully reflect upon the history that calls to us and synthesize our own with local reality. In doing so we do not merely build “constructed” cults of faith, we regrow ourselves into the soil beneath our feet.

In following with this belief: I am a North American heathen, not Norse, not Germanic, and as a result my faith has roots in many places. I forage and garden with respect to my local ecology, while I celebrate and worships the heathen gods. I honor my ancestors, just as I honor the wights of my home and my land. I am no more a god than the plants I nurture, or the winds in the tree of faith I have grown. I revere what I can from the past, but I ensure I am not bound to it.

Whatever foundation of animism you choose to follow, this universalism remains pivotal towards a future of reverence for the Earth. With this universalism comes the acceptance of other’s animistic belief systems. The shift towards subjectivity comes the understanding that we may all be correct, that the spirits of the world all hold their own moral ground. That our individual relationships with our locale, and the spirits therein, are all aligned with the same path of respect and reverence for life.

References

Baudrillard, Jean. Simulacra and Simulation. Ann Arbor, The University of Michigan Press, 1994.

Butler, Daniel M, and Joseph L Sutherland. “Have State Policy Agendas Become More Nationalized?” The Journal of Politics, 17 May 2022, https://doi.org/10.1086/720792.

Conty, Arianne. “Animism in the Anthropocene.” Theory, Culture & Society, vol. 39, no. 5, 30 Sept. 2021, p. 127-153, https://doi.org/10.1177/02632764211039283.

Dillion, Danica, et al. “National Politics Ignites More Talk of Morality and Power than Local Politics.” PNAS Nexus, vol. 3, no. 9, 1 Sept. 2024, https://doi.org/10.1093/pnasnexus/pgae345.

Doran, Ryan P. “Aesthetic Animism.” Philosophical Studies, 21 June 2022, https://doi.org/10.1007/s11098-022-01830-5.

Harvey, Graham. Animism: Respecting the Living World. London, Hurst & Co, 2005.

Kirkland, Dean. Seiðr Magic. Simon and Schuster, 2 Apr. 2024.

Martin, Gregory J, and Joshua McCrain. “Local news and national politics.” American Political Science Review, vol. 113, no. 2, 19 Feb. 2019, pp. 372–384, https://doi.org/10.1017/s0003055418000965.